Suits The C-Suite

SGV thought leadership on pressing issues faced by chief executives in today’s economic landscape. Articles are published every Monday in the Economy section of the BusinessWorld newspaper.
08 July 2024 Rajiv Kakar

Key GenAI cybersecurity challenges and risk mitigation strategies

Generative artificial intelligence (GenAI) has the capacity to understand, learn, adapt, and implement knowledge across a broad range of tasks at a level equal to or beyond human capability. Unlike Narrow AI, which is designed to perform a specific task such as voice recognition or recommendation algorithms, GenAI can apply intelligence to any problem, and be able to perform any intellectual task that a human being can do.While it holds extraordinary promise for the future, GenAI comes shrouded in various concerns, extending from ethical dilemmas to security susceptibilities. This article will explore some of the key challenges of GenAI and risk mitigation strategies from a cybersecurity perspective.Key challenges of GenAI A persistent issue of AI is the lack of transparency, frequently referred to as the black box problem. It’s difficult to understand how complex AI models make decisions, and this can create a security risk by allowing biased or malicious behavior to go unchecked.Businesses are rapidly exploring GenAI solutions with little forethought on the security implications on the rest of the IT estate. There is currently no limit for the complexity of attack surfaces of AI systems or other security abuses enabled by AI systems. In addition, AI models heavily rely on third-party technologies, where the large language models (LLMs) like ChatGPT are outside the control of an enterprise. Consequently, the learning parameters where AI systems may be trained for decision-making outside an organization’s security controls or trained in one domain and then “fine-tuned” for another raises concerns about intended and actual usage.Datasets used to train AI systems may become detached from their original and intended context, or may become stale or outdated relative to deployment. This introduces the problem of decisions made on incorrect data. Moreover, changes during training of models may fundamentally alter AI system performance and outcomes.LLMs typically capture more information than they process, and considering the privacy policy of ChatGPT, the platform may regularly collect user data such as IP address, browser info and browsing activity. These may be shared with third parties, competitors, and regulators. The use of pre-trained models that can advance research and improve performance can also increase levels of statistical uncertainty and cause issues with bias management, scientific validity, and reproducibility.On top of the computational costs for developing AI systems and their impact on the environment and planet, it is very difficult to predict failure modes for the emergent properties of large-scale pre-trained models. AI systems may require more frequent maintenance and triggers for conducting corrective maintenance. Additionally, it is challenging to perform regular AI-based software testing, or determine what to test, since AI systems are not subject to the same controls as traditional code development.“Artificial stupidity,” the term used to describe situations where AI takes decisions that may seem illogical to humans due to its inadequate understanding of the real-world context, presents another challenge. Talks of AI singularity, a hypothetical scenario where AI outstrips human intelligence, have also started to gather momentum. Critics argue that a super-intelligent AI poses a real existential risk, as it might spin out of human control. The dehumanizing effects of GenAI are another cause for concern. Over-reliance on AI risks devaluing human skills and minimizing human interactions. Moreover, the widespread application of GenAI may give rise to economic disparity, as the benefits of AI may not distribute evenly across society. Finally, the misuse of GenAI, particularly for harmful purposes like illegal surveillance, spreading propaganda, or weaponization, cannot be overstated.The already dense and complex AI landscape is further complicated by substantial hype and a multitude of diverse solutions. The resulting application environment is scattered with multiple third-party technology solution components which require thorough vetting in enterprise contexts. Types of GenAI attacksThere are various types of GenAI attacks manifesting across enterprises. Adversarial attacks involve manipulating an AI model's input data to make the model behave in a way that the attacker desires, without triggering an alarm. For example, an attacker could manipulate a facial recognition system to misidentify an individual, allowing unauthorized access. A data poisoning attack involves maliciously manipulating the data used to train AI models. By introducing false or misleading data into the training dataset, attackers can compromise the accuracy and reliability of AI systems. This can lead to biased predictions or compromised decision-making. On the other hand, a model theft or model inversion attack may attempt to steal and/or reverse-engineer AI models to obtain sensitive information. In a transfer learning attack, an attacker manipulates an AI model by transferring knowledge gained from one domain to another, resulting in the AI system producing incorrect or harmful outcomes when applied to new tasks. In input manipulation, interacting with a chatbot or an AI-driven system can lead to incorrect or harmful responses simply by changing words or asking tricky questions. For instance, a medical chatbot might misinterpret a health query, potentially providing inaccurate medical advice.AI can also be used by malicious actors to automate and enhance their cyberattacks. This includes using AI to perform more sophisticated phishing attacks, automate the discovery of vulnerabilities, or conduct faster, more effective brute-force attacks.GenAI security risk managementTo mitigate attack vectors, organizations must establish comprehensive regulations and standards that can guide the responsible use and development of GenAI. A GenAI Risk and Control framework can be very helpful in highlighting areas of vulnerability and risk mitigation in some of the following areas:  Threat recognition. Identify possible threats GenAI might enable, such as autopilot system hacking, data privacy threats, decision-making distortion, or manipulation.Vulnerability Assessment. Evaluate weak spots in the system that might be exploited due to GenAI characteristics.Risk Impact Analysis. Look into potential implications if any threats were actualized (financial implications, impact on company reputation, etc.)Mitigation Strategy Development. Develop strategies to mitigate these risks, whether that means strengthening your network security system, creating backup systems, securing data privacy with improved encryption, or continuously auditing & updating the AI’s programming against potential manipulation.Contingency Planning. Develop a plan for responding to any breaches or issues that occur, despite mitigation efforts. Include steps to fix the issue, mitigate the damage, and prevent future occurrences.Constant Monitoring & Updating. GenAI systems should be regularly monitored and updated to patch vulnerabilities and keep up with the evolving threat landscape.Training & Awareness. Ensure that all users of GenAI systems are properly trained on security best practices and are aware of the potential threats.External Cooperation. Cooperate with other firms and institutions to share threat intelligence and promote a collective defense strategy.Regulation Compliance. Ensure compliance with all applicable laws and regulations surrounding data security and AI, such as general data protection regulation (GDPR).Incident Response Plan. Prepare a clear and concise plan to follow when a breach occurs, which includes reporting breaches, managing and controlling the situation.Organizations must consider upgrading cloud security and moving towards zero trust principles, whereby every access request is authenticated, authorized and validated every time. Antivirus systems should be upgraded from the current norm of using a pre-programmed list of known attack vectors (signature based) to systems that can observe unusual patterns and alert on deviations (anomaly based). Embracing GenAI monitoring by introducing the appropriate tools allows organizations to monitor AI prompts and see that they do not deviate from original scenarios.Review and strengthen security around a GenAI application stack emphasizing on integration points between systems (API’s) and identify AI systems and assets by drawing up a plan of usage. Organizations can assign a dedicated team to test AI models at base and application level, as well as introduce moderation and control on user developed applications, tools and products. Any experimental or uncontrolled work on GenAI within the enterprise must be monitored.Applying these strategies can minimize the risks associated with GenAI and help efficiently manage cybersecurity.Navigating AI pitfalls by mitigating risksWhile the potential of GenAI is undeniable, a cautious, forward-thinking approach is crucial to navigating its potential pitfalls. It is imperative to establish comprehensive risk mitigation, standards, and frameworks that can guide the responsible use and development of GenAI. Rajiv Kakar is a Technology Consulting Principal of SGV & Co.This article is for general information only and is not a substitute for professional advice where the facts and circumstances warrant. The views and opinions expressed above are those of the author and do not necessarily represent the views of SGV & Co.

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01 July 2024 Ryan Gilbert K. Chua

Realizing potential with GenAI

The significance of managing generative artificial intelligence (GenAI) initiatives is underscored by a white paper from Pactera Technologies, a leading global technology company, which indicated that a substantial 85% of these projects fall short. Forbes corroborates that the majority of GenAI projects do not meet expectations, underscoring a problematic trend in the field. GenAI projects possess characteristics that differentiate them from standard software development undertakings. Consequently, the strategies employed in overseeing and realizing the potential of GenAI projects demand a tailored approach distinct from conventional software project management. To address this issue and enhance AI project management methodologies, this article will discuss the following fundamental principles designed to refine the management of GenAI-related projects.Establishing clear business objectives and the importance of planningTo fully harness the potential of GenAI, it's essential to establish specific, measurable, achievable, relevant, and time-bound (SMART) goals for the GenAI solution to achieve. This crucial step involves a deep understanding of the underlying business problem or challenge that the GenAI solution intends to address. It is also vital to consider whether GenAI is the most suitable solution, ensuring that the technology is not simply being used for its own sake.Identify and rationalize potential use cases for GenAI that are in sync with core business objectives. This involves a process of prioritization - pinpointing which GenAI applications can deliver the highest value in alignment with the strategic direction of the organization. By focusing on areas where GenAI can make a significant impact, businesses can channel their resources more efficiently and create a tailored approach that maximizes its benefits. For instance, a common application of GenAI is in knowledge management, which could provide value across the enterprise.Understanding the project life cycle is another fundamental aspect of managing and executing a GenAI project successfully. Establish the stages the project will go through, including a comprehensive methodology that covers various phases such as planning, developing, testing, deploying, and monitoring the GenAI solution. While each stage of the project is important, an emphasis on key differences in developing and monitoring traditional and GenAI solutions is important. For example, in developing GenAI solutions, model “training” directly impacts the performance of the solution in production. Likewise, monitoring performance for its accuracy and precision would be continuous throughout the use of the solution.Selecting the appropriate tools and methodology is equally critical. Whether in terms of data processing software, programming languages, and platforms for deployment, these must be chosen with the aim of enhancing the productivity and effectiveness of the GenAI solution.Understanding dependencies and prerequisites GenAI solutions depend on a process often referred to as "learning," which involves feeding them a substantial volume of historical business data. This data acts as the foundation upon which the GenAI model is adjusted and refined, making it crucial that this information is of high quality. The principle of "garbage in, garbage out" is applicable here, as any shortcomings in the data can lead to flawed results. As the system continues to process new data, its effectiveness is influenced by the accuracy, completeness, and overall integrity of the information it receives.Another key aspect is the existing technological infrastructure and the broader system of the company. The current architecture and its capacities must be evaluated to determine how they might integrate with or support the effective deployment of the GenAI system. This includes considering the capability of current systems to communicate with the GenAI solutions and manage the additional workload. Scalability also cannot be overlooked. While GenAI can be a powerful business enabler, it requires the proper infrastructure to unlock its full potential. For example, GenAI solutions require significant computational power to function properly, thus, a powerful hardware component will accelerate “learning” of complex algorithms. The implications of GenAI on existing business processes are profound. The adoption of GenAI systems can lead to a complete overhaul of current processes, possibly making some obsolete. This makes it essential to perform a meticulous gap analysis to understand the differences between current state and future state business processes. This helps businesses ensure they can capitalize on the advantages GenAI offers while mitigating any operational disruptions.Cross-functional collaborationGenAI initiatives will require cross-functional collaboration. A diverse team composition is necessary due to GenAI projects intersecting multiple domains, requiring a holistic understanding of each area to create solutions that are not only technically advanced but also practical and relevant to the business. For example, a GenAI solution includes business process, application, infrastructure, and data components. To be able to design the solution, it will require the business unit to define the business problem, legal unit to provide compliance requirements, IT unit to provide data, infrastructure and other system requirements, HR unit to manage change, and senior leaders to drive its adoption.   Adequate training will be crucial in ensuring that each team member can contribute effectively and understand the complexities of the tasks at hand. A data scientist, for example, must understand not just the intricacies of algorithms and model-building but also the business problems the technology is meant to solve. It is also imperative to involve cross-functional teams from the earliest stages. Collaboration should be established from the beginning, mixing technical expertise with business insights and ethical considerations. This allows every aspect of the project to be scrutinized from multiple perspectives, fostering an environment where technical feasibility, business viability, and ethical implications are all weighed and balanced. This blended approach ensures that the solutions developed are realistic, beneficial for the business, and designed with a consideration of their impact on stakeholders and society at large.Change managementOne common issue in implementing a GenAI solution is resistance. While people may be hesitant to adopt new technologies in favor of established routines, it's essential for companies to anticipate this resistance and prepare with strategies to address concerns and ease the transition for everyone involved.To facilitate adaptation, the company should provide substantial training and dedicated support. Instructional programs designed to enhance understanding of the new GenAI system can empower employees. Additionally, a hypercare support system, which offers intensive post-implementation assistance, ensures that immediate help is available for any issues or questions that may arise during the initial stages of using the new technology.Stakeholder management is also a critical component in ensuring a smooth transition. Clear and transparent communication regarding sunk costs associated with GenAI systems is necessary, as well as assurances that the investments are calibrated for long-term benefits. Stakeholders must also understand the timeframes involved, from the initial implementation phase to when positive returns can be expected. By managing expectations with clarity, the company can secure sustained commitment and support for GenAI initiatives.Performance monitoring and optimizationDetermine baseline metrics that act as a standard against which the added value of the GenAI system can be measured. Once the system is operational, the company must assess its performance, leveraging both qualitative and quantitative methods in its evaluation while utilizing appropriate metrics and benchmarks. For instance, the company might compare the output generated by the GenAI system against previously established baseline metrics, such as output produced by humans prior to when the GenAI system was implemented.In addition to monitoring technical GenAI metrics such as accuracy and precision, the company must measure the impact of the system through a business-focused lens. This means putting a spotlight on how the system influences business metrics, outcomes, and the overall impact on company operations. Realizing the potential of GenAI The potential of GenAI transcends simple enhancements in organizational efficiency. Its profound ability to generate, model, and interpret intricate data place it at the forefront of driving business innovation. GenAI empowers corporate leaders to envision a new horizon for their organizations, leveraging this rapidly advancing technology well past the bounds of simple gains in productivity. Through GenAI, businesses are not just improving processes, but revolutionizing their approach to problem-solving and strategic planning, planting the seeds for long-term value. Ryan Gilbert K. Chua is the Business Consulting Leader and Technology Assurance Leader of SGV & Co.This article is for general information only and is not a substitute for professional advice where the facts and circumstances warrant. The views and opinions expressed above are those of the author and do not necessarily represent the views of SGV & Co.

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24 June 2024 Roderick M. Vega

Instilling integrity into the corporate DNA

The current climate of persistent macroeconomic, geopolitical, and market volatility, coupled with stringent regulatory scrutiny, continue to put the moral compass of organizations to the test. These global conditions underscore the critical importance of the values businesses uphold, particularly trust and integrity. Trust serves as a significant competitive advantage, particularly when market unpredictability challenges business resilience. Without trust from employees, customers, suppliers, and investors, an organization’s future viability is jeopardized.  At the same time, companies rooted in integrity ensure long-term sustainability by adhering to ethical practices, which reinforce their brand and operational stability. A lack of integrity erodes trust, leading to significant operational and strategic challenges. The interplay between these two raises a critical question: "How can trust endure without integrity?" This query forms the crux of the EY Global Integrity Report 2024, which surveyed over 5,400 respondents across 53 countries and territories. On the upside, 49% of global respondents believe that compliance with their organization's standards of integrity has improved over the past two years, marking a seven percent increase from the EY Global Integrity Report 2022.  However, 38% of global respondents acknowledge a willingness to engage in unethical behavior to advance their career or remuneration. This pervasive mindset creates substantial risks that could lead to various adverse impacts within an organization. The cost of low corporate integrity is high. Specifically, corporate violations in the United States and the United Kingdom incurred penalties totaling US$1 trillion, as a result of half a million infractions between 2010 and 2023.  This article explores actionable insights from the EY Global Integrity Report 2024 and identifies human-centered approaches that leaders can use to build an integrity-first culture within their organizations. The current state of integrity Despite improved perceptions of organizational standards of integrity, companies continue to grapple with significant incidents and violations. The EY report highlights that 20% of companies acknowledge experiencing major integrity breaches, such as fraud, data privacy or security incidents, or regulatory compliance violations, within the past two years. Notably, among those reporting significant integrity incidents, over two-thirds indicate the involvement of third parties. An analysis of over 500,000 corporate violations from 2010 to 2023 reveals that certain financial and employment violations, including accounting deficiencies, AML deficiencies, tax violations, labor standards, workplace safety, and consumer privacy issues, have become 2 to 10 times more frequent since 2010. Conversely, there has been a notable decline in violations related to employee compensation, public safety, banking, and environmental issues. However, progress remains limited in addressing anti-competitive behavior, discrimination, and whistleblower retaliation. Employees’ approach toward integrity Although a majority of employees (58%) take a principled approach to integrity, there remains a substantial proportion (42%) who may compromise these standards under certain conditions.  In this dichotomy, the report shows that potentially compromised employees have a more negative view of their organization’s compliance environment. They are nearly three times more likely to say that unethical conduct is ignored within their teams, and more than five times more likely to say that unethical conduct is ignored within their organization’s supply or distribution chain. Leaders' integrity dilemma An unethical mindset towards career or pay is predominant in the upper echelons of organizations, with 67% of board members admitting they would consider unethical actions for their own benefit compared to only 25% of employees. Moreover, 47% of board members and 40% of senior management have observed actions within the past two years that could damage their organization’s reputation if made public, yet no internal response was taken. This lack of action highlights a critical gap in ethical oversight and accountability. What breeds misconduct The survey identifies several root causes of integrity incidents globally, including failure of financial processes and controls (27%), lack of internal resources to manage compliance and integrity activities (27%), employees not understanding policy and requirements (26%), and lack of appropriate tone from senior leadership (25%). Equally significant, 45% of global respondents who reported integrity incidents attribute them to poor leadership tone or management pressure. This issue is compounded by the apparent reluctance among leaders to address misconduct.  Such factors contribute to an environment conducive to misconduct, emphasizing the need for robust controls, resources, and leadership commitment to foster a culture of integrity. High cost of low integrity Misconduct is an unpleasant reality, surfacing even within the most ethical organizations. Corporate infractions come at a high cost—not just in resources spent on internal investigations and remediation but also in fines and penalties paid to government regulators. For instance, recent research indicates that corporate fraud shaves approximately 1.6% off a company’s equity value each year. In monetary terms, that equates to US$830 billion in 2021 alone.  But the costs extend beyond the financial. A top-down, all talk, no walk mentality erodes trust both within the organization and in the public eye, placing the company's reputation and financial health in jeopardy. Building an integrity-first culture Embracing the following integrity-first approaches — which put the right programs in place to drive behavior to create a strong culture and a strong belief in their commitment to integrity — can help organizations keep pace with evolving regulations and increasing societal expectations: Lead from the top. Integrity can’t be built or sustained with all talk and no action. Organizations need to focus on preventing and addressing misconduct by starting from the top. Moreover, leaders need to listen and practice what they preach to instill integrity further down the line. Words alone won’t inspire integrity; it demands actionable leadership. Design and implement a structure to execute strategy. To prevent unethical actions from the top down, organizations must implement robust governance structures within their integrity programs and strategies. Breaking down silos is also crucial to encourage a 'speak-up' culture against any misconduct. Strengthen a culture of integrity across the organization. Organizations must recognize that integrity is a collaborative endeavor, not merely a stand-alone function. Embedding compliance directly into operations—from new business development to vendor payments—transforms corporate policies into actionable workflows.  Boost awareness, training and communication. The report indicates that fewer than 47 percent of management teams frequently communicate to their employees the importance of behaving with integrity. Making the rationale behind policies crystal clear fosters a resilient organization capable of thriving in both good and bad times. Create a virtuous circle of integrity. In times of rapid change and difficult market conditions, maintaining, let alone enhancing, corporate integrity can seem daunting. But it is precisely in these challenging times that integrity must not only be preserved but also prioritized. Roderick M. Vega is the Forensic and Integrity Services Leader of SGV & Co.This article is for general information only and is not a substitute for professional advice where the facts and circumstances warrant. The views and opinion expressed above are those of the author and do not necessarily represent the views of SGV & Co.

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18 June 2024 Christiane Joymiel C. Say-Mendoza and Joseph Ian M. Canlas

Responsible AI: Transforming risk management in the Philippines

As the digital age continues to evolve, artificial intelligence (AI) is rapidly becoming a cornerstone of innovation and efficiency. In 2021, the Philippines launched the National Artificial Intelligence Roadmap, which prioritizes inclusive, resilient, and sustainable development. Furthermore, the country’s President believes that AI can uplift the lives of the nation’s citizens, drive enterprise productivity, and increase the Philippine economy’s competitiveness. According to a recent study from IBM’s Institute for Business Value, three out of four CEOs think that organizations with the most advanced generative AI (GenAI) are at an advantage, with nearly half already utilizing GenAI to guide their strategic decisions. As organizations expand their AI adoption, it is imperative that they adhere to Responsible AI practices, which promote the ethical, transparent, and beneficial use of the technology. AI adoption in the Philippines The country’s AI adoption is evident across multiple sectors, each harnessing its capabilities to enhance operations and manage risks. Financial institutions. Some local universal banks are leveraging on AI for risk assessment, fraud detection, and customer service, utilizing solutions provided by tech giants such as Microsoft. Healthcare. Some healthcare platforms are leveraging AI for medical data analysis, improving patient care, and expanding telehealth services. Telecommunications. Local telecom companies employ AI for network optimization, customer service enhancement, and predictive maintenance. E-commerce/Retail. Online marketplaces and retailers utilize AI-driven recommendations and predictive analytics to refine customer experiences and operational efficiency. AI's impact on risk management AI is revolutionizing risk management by offering enhanced data analysis, predictive capabilities, real-time risk assessments, and advanced cybersecurity measures. These technologies enable businesses to identify and respond to risks with unprecedented speed and accuracy. However, the integration of AI into risk management is not without its challenges. Concerns around data privacy, algorithmic bias and fairness, transparency, and regulatory compliance must be addressed to ensure the responsible use of AI. Data privacy and security. AI systems rely on data. There's a risk that sensitive customer or business information could be exposed, particularly if appropriate cybersecurity measures are not in place. Algorithmic bias and fairness. AI systems are only as good as the data they're trained on. If the data is inaccurate, incomplete, or biased, it can lead to unreliable or discriminatory decisions. Lack of transparency. Complex AI models may lack transparency, making it challenging for stakeholders to understand how decisions are made. If the reason behind a decision by AI can't be explained, it can lead to legal and ethical implications. Regulatory compliance. The legal environment for AI is complex, fluid, and still developing. Companies can face risks relating to non-compliance with data protection regulations and other industry-specific laws. Navigating AI risks with responsible practices Responsible AI covers transparency, fairness, accountability, ethical use, privacy protection, reliability, safety, sustainability, inclusivity, and governance. To integrate Responsible AI into risk management, companies can adopt the following best practices: Ethical framework development. Create a comprehensive ethical framework that aligns with regulatory standards and industry-specific best practices. Data governance and privacy protection. Implement data governance practices to ensure data privacy and transparency in AI models. Transparency and explainability. Make AI outputs understandable and provide justifications for AI-generated decisions. Bias detection and mitigation. Conduct thorough bias assessments to identify and mitigate biases in AI models. Human-AI collaboration. Augment human expertise with AI, promoting collaboration through accessible interfaces like visualizations and interactive dashboards. Examples of Responsible AI in action Banks. Major local banks are incorporating AI in risk management, with a focus on fraud detection. Responsible AI usage involves stringent data protections and privacy measures. Telecommunications. Local providers use AI to manage infrastructure risks and predict outages. Ensuring responsible AI usage means preventing wrongful service denials. E-commerce. Some platforms employ AI for product recommendations, with a responsibility to avoid discriminatory biases. Health Tech. Certain local companies use AI for disease diagnosis, requiring the protection of sensitive health information. The trajectory of Responsible AI in the Philippines The future of Responsible AI in the Philippines includes broader AI adoption across sectors, enhanced regulations, and workforce upskilling, among others. With the Philippines set to propose the creation of a Southeast Asian AI regulatory framework to the ASEAN in 2026, Responsible AI could become a standard in business operations. As AI becomes more pervasive in the country’s business landscape, its impact on society will be profound, shaping the future of work, influencing broader socio-economic development, and driving positive change. It is therefore imperative for organizations to embrace Responsible AI principles in risk management and collaborate with stakeholders to navigate the opportunities and challenges presented by local AI-driven innovations.  Christiane Joymiel C. Say-Mendoza and Joseph Ian M. Canlas are Business Consulting Partners of SGV & Co. This article is for general information only and is not a substitute for professional advice where the facts and circumstances warrant. The views and opinions expressed above are those of the authors and do not necessarily represent the views of SGV & Co. 

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10 June 2024 Maria Kathrina S. Macaisa-Peña

Leveraging GenAI to transform the finance function

The evolution of artificial intelligence (AI) has been remarkable, beginning with the conceptualization of neural networks in 1943 and progressing to the birth of machine learning (ML) in 1959 and the advent of deep learning in 2006. This trajectory led to the era of Generative AI (GenAI), which emerged around 2017. GenAI refers to the subset of AI that focuses on creating new content, from text to images, by learning from vast datasets. This leap forward enables machines to not only interpret data, but also to generate original outputs that can mimic human creativity and reasoning. As the technology becomes more sophisticated, consumers are increasingly integrating large language models (LLMs), an application of GenAI, into their daily lives. From asking virtual assistants for weather updates to receiving personalized recommendations, the comfort and confidence in using such technology are on the rise. This adoption signifies a shift in the public's perception of AI, viewing it as a reliable and integral part of modern living. Part of this shift can be seen in how GenAI is revolutionizing strategic business thinking, enabling businesses to unlock new revenue sources, achieve productivity gains, and innovate existing business models, ultimately leading to value creation. In particular, firms and departments dealing with finance and accounting can leverage GenAI to enhance data entry and reconciliation, enrich forecasting and analysis, and fortify risk management. GenAI applications in the finance function The finance function is pivotal in supporting optimized enterprise decisioning – and rethinking enterprise structures through the lens of GenAI is key to unlocking a spectrum of new possibilities for value creation. Knowledge management and decision support in particular are among the most potent use cases for scaled AI capabilities.  GenAI can enhance an organization's data value by asking better questions, optimizing multi-variable choices, and enabling actions at scale. In addition, GenAI can write code on demand to extract information from data sources, create reports with appropriate data visualization, and provide persona-based analysis. It also enables conversations with virtual agents for a deeper understanding of results.  In monthly financial reporting cycles, analysts would traditionally spend hours writing code to extract data from various sources, compiling it into spreadsheets, and then painstakingly creating visualizations. GenAI allows them to input their requirements, after which the AI writes the necessary code on demand, pulling information from databases, cloud storage, and even real-time market feeds. The data is not just tabulated – it's transformed into compelling visual reports that highlight key financial metrics and trends. Moreover, GenAI can provide persona-based analysis, tailoring insights to the specific needs of each stakeholder. The CFO receives a high-level overview emphasizing strategic implications, while line managers get detailed breakdowns relevant to their departments. Content creation, a repetitive and complex task, has also been redefined by GenAI. Finance teams can leverage GenAI to assist in generating various analytical documents. Variance reports, budgets, and forecasts are produced with a level of detail and accuracy that was previously unattainable. GenAI sifts through historical data, identifies anomalies, and presents findings in a clear, concise manner. Moreover, GenAI can extend its capabilities to responding to common queries from colleagues or clients. Instead of drafting individual responses, finance professionals can rely on GenAI to provide accurate and contextually relevant answers, freeing up their time for more strategic tasks. GenAI has become an essential collaborator in meetings and project planning as well. It helps document discussions, distilling them into actionable items and comprehensive plans.  Last but not the least, perhaps the most transformative application of GenAI within the finance function is in forecasting. A GenAI model can take in vast amounts of historical financial data and current market trends to predict future performance with remarkable accuracy. It identifies patterns that might elude even the most experienced analysts and uses natural language processing to incorporate insights from news articles and external data sources. This ability allows organizations to anticipate market movements and adjust their strategies proactively. Whether in terms of revenue, expenses, profit, or cash flow, forecasts can provide more than just numbers — they can become strategic tools that inform decision-making at the highest levels. Realizing GenAI advantages  To fully realize the advantages of GenAI in finance and accounting, companies need to enhance their finance and accounting functions with innovation intelligence, invest in infrastructure and develop talent in AI while putting proper governance and controls in place. Amidst the possibilities and efficiencies that AI can create for the finance function, blind optimism and hype around this disruptive technology can have a counterproductive impact on a business that is unaware of its risks. To avoid this, companies can take the “innovation intelligence” approach through implementing planning, education and an agile test and learn strategy. Another critical determinant of an organization’s success will be how they enhance their comprehension of and refine their data infrastructure. Companies should have a tech stack with a solid foundation and support from experts to ensure their legacy data and technologies are unimpeachable before adding any GenAI applications on top of existing systems. Based on the EY 2023 Financial Services GenAI Survey, 44% of leaders identify access to skilled resources as a barrier to GenAI implementation. Part of the solution is to deploy upskilling programs that can equip the current workforce with the necessary skills in an increasingly AI-centric world. The human role of AI implementation is just as important as technology infrastructure. The GenAI imperative in the finance function Incorporating GenAI into finance is not just an option – it has become an imperative for long-term value creation. However, while it brings significant gains, it is also crucial to be mindful of potential risks. While aligning GenAI across the organization will be essential to unlock greater value, organizations must consider how GenAI can be used not only to transform the finance function, but also to redefine the future of business decision-making. Maria Kathrina S. Macaisa-Peña is a Business Consulting Partner and the PH Finance Fields of Play Leader of SGV & Co. This article is for general information only and is not a substitute for professional advice where the facts and circumstances warrant. The views and opinions expressed above are those of the author and do not necessarily represent the views of SGV & Co. 

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03 June 2024 Jan Ray G. Manlapaz and Mary Andrea T. Bacani

Building efficient and resilient supply chains with GenAI

In the wake of the global pandemic, businesses remained focused on advancing their artificial intelligence (AI) supply chain pilot projects into fully functioning applications. Companies are turning more to AI for demand planning and procurement within their supply chains, and are also investigating its potential for streamlining processes and enhancing efficiency in final-stage delivery. However, the rapid emergence of Generative AI (GenAI), brought to prominence by ChatGPT, has dramatically shifted perceptions about the capabilities of AI. GenAI is adept at producing new content that includes images, text, audio, or video, drawing from its training data. This technology isn’t new, but recent developments have streamlined its use and enhanced its practical value. As funding flows into this technology, leaders are swiftly assessing how it affects their operations and business structures, aiming to capitalize on its benefits. For those who diligently and strategically engage with innovation while maintaining an awareness of its limits — rather than impulsively chasing trends — GenAI can serve as a dynamic collaborative partner and a force multiplier in fortifying supply chains.What might have once been considered fictional is now part of serious conversations. AI applications are already being put into practice in real-world scenarios throughout the entire supply chain. These are made possible by GenAI's capabilities to organize and sort information based on visual or textual inputs, rapidly assess and adjust strategies, plans, and the distribution of resources in response to live data, produce various types of content on-demand, leading to quicker reaction times, summarize vast amounts of data while highlighting essential insights and patterns, and quickly help retrieve relevant information and deliver immediate responses, whether through voice or text.While it does have its limitations, GenAI provides a multiplier in what technology and humans can achieve together in building efficient and resilient supply chains, whether in planning, sourcing, making or moving. PlanningGenAI streamlines engagement across technology-driven planning activities. Modern GenAI applications are also capable of proposing multiple strategies in case of unforeseen complications. The area of risk management stands out as particularly promising, especially in anticipating risks that supply chain planners might not have previously contemplated. Numerous organizations are leveraging AI to sift through extensive historical sales data, market movements, and other factors to construct real-time models of demand. In addition, GenAI enables the formulation of ideal inventory quantities, manufacturing timetables, and distribution strategies to efficiently satisfy consumer needs.AI can assist in orchestrating production and timetabling by taking into account elements such as changes in customer orders, production capacity, resource availability, and the priority of orders. Similar to its capabilities in forecasting demand, GenAI can devise production plans, scheduling sequences, and efficiently allocate resources to reduce bottlenecks and optimize production efficiency. Currently, AI can be utilized to scrutinize historical data, market dynamics, climatic trends, and geopolitical occurrences, among other information sources, to pinpoint potential risks within the supply chain. Rather than relying on preset dashboards, for instance, GenAI can be prompted to generate on-the-spot risk evaluations, simulate various scenarios, and craft strategies for risk mitigation to aid planners in proactively overseeing and lessening risks.SourcingBeyond negotiating, GenAI offers a chance to enhance supplier engagement and oversight, providing guidance on subsequent actions. These useful tools can quickly pull information from extensive contracts, potentially helping prepare for discussions about contract renewals. In managing suppliers, companies can utilize natural language processing to derive insights from supplier communications and various data points. It can support the supervision and analysis of supplier interactions, pinpoint potential problems, and foster stronger supplier partnerships.Moreover, GenAI can assist in the process of choosing suppliers by evaluating a broad spectrum of supplier data and producing insights. By considering aspects such as supplier performance, capabilities, pricing, and risk assessments, GenAI algorithms can offer suggestions or rankings to support well-informed decision-making. MakingGenAI is revolutionizing the supply chain by significantly accelerating the journey from concept to commercialization, even when it involves new materials. Organizations are educating algorithms on their proprietary data and then employing AI to uncover methods to enhance productivity and efficiency. Predictive maintenance is yet another area where GenAI can pinpoint which machinery or production lines are at risk of malfunctioning and when, thereby enhancing overall equipment effectiveness (OEE) — a critical metric in manufacturing.In product design, GenAI can rapidly generate and assess numerous design alternatives based on set criteria, drastically accelerating the innovation cycle. This approach can be applied to a wide range of design challenges, from engineering new components for industrial machinery to creating consumer goods that are more efficient, robust, or visually attractive. Informed by data from factory machinery, GenAI models can also devise new maintenance strategies that align with predicted failure times of equipment. This enables manufacturers to fine-tune their maintenance timetables to intervene only when necessary, minimizing operational interruptions and expenses while also prolonging machinery lifespans.In addition, GenAI can be used to unearth new materials and refine existing ones. By analyzing extensive data on material characteristics and experimenting with various combinations, it can recommend new materials with specific desired traits or enhance the properties of current materials. This innovation could lead to the development of materials that are more efficient, sustainable, or durable for manufacturing purposes.MovingAlthough GenAI application in the field of logistics isn't new, the generative aspect introduces new levels of adaptability. For example, it can be used for route optimization for reduced fuel usage, the prioritization of specific shipments, or integration of various factors into an accessible platform. GenAI can optimize global trade by assessing a wide range of factors, such as tariffs, customs rules, trade agreements, and shipping expenses, to propose the most effective and economical routes and strategies. This helps businesses to maneuver through intricate global trade networks, ensuring compliance while cutting costs. Additionally, GenAI can improve the design of logistics networks by considering elements such as warehouse locations, transportation links, and demand patterns to generate efficient configurations. This results in shorter delivery times, decreased expenses, and heightened service quality.One of the significant challenges in logistics is real-time routing, which GenAI can address by constantly refining and enhancing delivery or collection routes in response to evolving conditions such as traffic, weather, and delivery priorities. This leads to heightened efficiency, lower fuel usage, and greater customer satisfaction.Realizing value with GenAIGenAI is a potent instrument with its own set of constraints, but it should not be mistaken for a strategy in itself. Organizations must focus on the business benefits and establish a roadmap, guided by the following steps:Focus on domain-wide transformation. Identify use cases with significant potential, aiming to create an integrated ecosystem that complements traditional business practices and unlocks new opportunities.Coordinate and collaborate. Discuss the broader implications of using GenAI and pinpoint the competencies needed across various departments, extending beyond just the technical roles.Maintain an open mindset while being mindful of risks. Launch exploratory pilot projects to gain insights, secure early successes, and work towards a model that can be expanded and adopted on a larger scale.Utilizing AI in supply chain management can help organizations become more resilient and sustainable while transforming cost structures. With recent developments that make AI easier to use and more effective in realizing value, organizations must evaluate how its advances can impact their sectors. Jan Ray G. Manlapaz is a Consulting Partner and Mary Andrea T. Bacani is a Supply Chain and Operations (SCO) Senior Manager of SGV & Co. This article is for general information only and is not a substitute for professional advice where the facts and circumstances warrant. The views and opinions expressed above are those of the author and do not necessarily represent the views of SGV & Co.

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27 May 2024 Randall C. Antonio

How GenAI can accelerate business transformations

Given the fast-paced nature of digital evolution, businesses are increasingly turning to innovative technologies to stay ahead of the curve. Generative Artificial Intelligence (GenAI), which refers to AI algorithms that generate outputs based on existing data, has emerged as a transformative force that can revolutionize operations like product development, customer engagement, and software programming. However, integrating this technology into business processes requires strategic planning and careful execution. The EY 2023 Work Reimagined Survey shows that 84% of employers are expecting to implement GenAI within a year. Additionally, 33% of employees and employers see potential benefits for productivity and new ways of working. GenAI’s transformative capabilities are expected to augment human work and increase efficiency, which will have long-term effects on the global business landscape.The technology can transform business processes and unlock new levels of creativity and efficiency. To help ensure the success of GenAI implementation, this article will share five key strategies to effectively harness the power of GenAI in business transformation.Anchor everything to the enterprise strategyBegin by clearly defining business objectives and assessing how GenAI fits into the organization’s broader strategy. Identify specific areas where implementing an AI solution can drive value, such as streamlining operations, enhancing creativity, or personalizing customer experiences. By aligning AI initiatives with strategic goals, businesses can ensure that resources are allocated efficiently and that AI investments deliver measurable, tangible returns. Prepare quality data The success of GenAI will depend on the quality and diversity of data used to train models. It encompasses algorithms that leverage upon neural networks to generate new data that resembles the patterns found in the inputs it has been trained on. Access to relevant and high-quality data is therefore crucial for training and validation. Organizations must invest in data collection, cleansing, and augmentation processes to ensure that AI systems are trained with accurate and representative datasets. Additionally, diverse training data will be imperative to capture a wide range of scenarios and edge cases. This can improve the robustness of AI models, help mitigate biases, and ensure fair outcomes. Collaborate with the right teams The effective implementation of GenAI requires collaboration among multidisciplinary teams, highlighting the need for partnerships between AI specialists, data scientists, domain experts, and business stakeholders. Involve a cross-functional team in the decision-making process, blending technical expertise with business acumen and ethical considerations, to create a balanced and forward-thinking AI strategy.By fostering a collaborative ecosystem, organizations can leverage diverse perspectives and domain knowledge to develop AI solutions that address real-world challenges. Cross-functional teams should work together iteratively, from ideation to deployment, to ensure that AI solutions are aligned with business needs and user requirements. Apply responsible AI Ethical and responsible AI practices are paramount in today's data-driven world. Prioritize transparency, fairness, and accountability throughout the AI lifecycle. Implement measures to mitigate biases, ensure data privacy, and establish mechanisms for explaining AI-generated outputs. Bias in particular often manifests in ways that harm certain parts of the population. When the data that is used to train a model does not accurately reflect the group it is intended to serve, it can create imbalances in the model's outcomes. For example, imbalances could stem from a lack of diversity in the types of data collected. However, there are other types of imbalances that may compromise the precision of the GenAI model without negatively affecting a particular group. Although preventing such imbalances entirely is challenging, the development team must investigate potential sources of imbalance and seek ways to reduce it. By embedding ethical considerations into AI development processes, businesses can build trust with stakeholders and mitigate potential AI-deployment risks. Learn, adapt, and improve continuously AI implementation is a journey, not a destination. Embrace a culture of continuous learning and adaptation, where feedback loops drive incremental improvements. Monitor the performance of AI systems in real-world environments and gather insights from user interactions. Furthermore, use this feedback to refine AI models, optimize algorithms, and adapt strategies according to evolving business dynamics. Learning and growing from the project should be treated as an essential component of an AI endeavor, instead of a last-minute consideration. Foster an environment of ongoing education, prompting those involved to thoughtfully evaluate all triumphs and challenges. By staying agile and responsive, organizations can harness the full potential of GenAI to drive innovation and secure a competitive advantage. Transforming in the long-termWhile previous technological advancements mostly focused on automation, GenAI can also assist with complex cognitive functions like predictive analytics, machine learning, and natural language processing. Also, its use-cases encompass a diverse range of industries, occupations, and tasks. For example, the case study Generative AI at Work showed that customer service agents could resolve 13.8% more customer inquiries per hour with the help of GenAI tools.The successful implementation of GenAI requires a holistic approach that encompasses strategic alignment, data excellence, collaborative engagement, ethical considerations, and continuous learning. By adopting these key strategies, businesses can unlock new opportunities, drive operational efficiencies, and stay ahead in today's digital economy. Through concrete, actionable steps, GenAI can boost efficiency and innovation, reshaping today’s ways of working.  Randall C. Antonio is an AI Technology Consulting Principal of SGV & Co.This article is for general information only and is not a substitute for professional advice where the facts and circumstances warrant. The views and opinion expressed above are those of the author and do not necessarily represent the views of SGV & Co.

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20 May 2024 Aaron C. Escartin

The evolving role of financial controllers

The financial controller role has transformed dramatically, with emerging technologies and advanced data analytics, along with the growing importance of environmental, social, and governance (ESG) considerations, introducing a fresh perspective to company planning. The traditional duties of financial controllers, which used to focus on historical financial reporting and regulatory compliance, now demand a broader range of skills and responsibilities.Their responsibilities have broadened beyond basic bookkeeping – they are now expected to adopt a proactive and visionary mindset, taking on the role of strategic business overseers. Modern controllers must be well-versed in a variety of competencies; they must excel in accounting and be capable of managing data, participating in strategic corporate conversations, and acting as reliable counselors. Their role has progressed into one that focuses on directing and ensuring the achievement of value, positioning them at the core of financial strategy.The increasing need for real-time and predictive financial reporting has been a major catalyst for this shift, with the role now including elements of financial planning and analysis (FP&A). Though controllers used to focus on internal transactional duties, technological advancements and evolving business expectations are pushing the role to become more extroverted.Incorporating ESG factors into fiscal planningAs ESG factors gain prominence in corporate planning and risk evaluation, it is essential for controllers to weave them into the fabric of financial forecasting and disclosure practices. This integration should be in harmony with the company's sustainability objectives and effectively communicated to all stakeholders.Some organizations are now appointing ESG-specific controllers, positioning the controllership role at the vanguard of this pivotal strategic initiative. With the growing need for verified ESG reporting, controllers are well-placed to spearhead this domain within their companies. This marks a considerable shift from previous times when compliance with statutory or similar regulatory reporting might not have been at the forefront of many corporate controller agendas.Familiarity with non-financial reporting standards, such as those set by the Global Reporting Initiative (GRI) and the Sustainability Accounting Standards Board (SASB), is becoming indispensable. These standards provide a blueprint for evaluating and disclosing sustainability efforts, tasks that now fall under the purview of financial controllers.In a globally intertwined business environment, the challenge lies in ensuring adherence to a variety of regulations across different markets while keeping financial management practices consistent with both international benchmarks and local mandates. Controllers are expected to perform thorough due diligence and maintain a comprehensive international outlook to protect the company's cross-border activities.The controllership should embrace a "glocal" (globally local) operational framework, capitalizing on centralization to enhance value while also ensuring that compliance, resilience, and risk management are supported at the local level. This new model for controllers aims to strike a balance between shared services and business partnership roles, with compliance functions remaining centralized. To adapt effectively, controllers must integrate strategies that utilize technology and data to streamline and standardize processes, all while upholding a local presence that supports compliance and risk oversight.In the era of digital finance, the sheer amount and velocity of financial data add unprecedented complexity to the task of ensuring precision in financial reporting. Controllers have the critical responsibility of guaranteeing that financial statements are free of material misstatements and reflect a true and just representation of the company's financial status. The rapid evolution of technology and regulatory frameworks demand financial controllers to dedicate themselves to continuous learning, enabling them to anticipate trends and challenges by adapting their knowledge and practices to stay relevant and efficient.Expanding the financial controller roleThe expanding role of the financial controller now encompasses a more prominent role in strategic decision-making processes, including steering investment approaches, navigating risks, and pinpointing growth opportunities. They are emerging as pivotal figures in formulating business strategies, charged with navigating their companies through market volatility with a decisive grip on fiscal instruments.Moreover, they must master sophisticated financial software platforms that not only simplify financial processes but also unlock the potential for detailed data analysis. Controllers must become fluent in the language of technology, providing a nuanced perspective on the financial well-being of the company, and facilitating predictive insights. They should approach their role with an open mind and curiosity, ready to embrace new tools, functionalities, and technologies. At the same time, they must exercise discernment to choose technologies that are appropriate for their organization and specific circumstances.Controllers must cultivate a dual expertise: a deep grasp of financial principles coupled with skills in data analytics. With these capabilities, they can translate intricate data into clear insights, formulate corporate strategies, spur innovation, and promote ethical leadership. By nurturing sustainable business operations and maintaining the integrity of financial disclosures, controllers establish themselves as vital consultants within their organizations, equipped to manage the intricacies of today's business landscape.From traditional bookkeepers, financial controllers can become "value articulators" – guardians of value delivery who evaluate the financial outcomes of investments. Today's controllers transcend transactional duties, embracing data and technology with a forward-looking mindset crucial for steering sound decisions, ensuring regulatory adherence, and propelling the organization towards resilience and expansion. Preparing for the future of controllershipTo navigate the evolving landscape of controllership and prepare for its future, financial controllers must proactively refine their expertise and adapt to new challenges. A commitment to continuous professional development is essential, with a focus on acquiring knowledge in data analytics and mastering advanced financial software platforms. Controllers should immerse themselves in the latest fintech innovations, selecting tools that align with their company's specific needs. This discernment will ensure they remain competitive, leveraging automation and predictive analytics to drive business success.Additionally, understanding and integrating ESG principles into financial strategies is becoming increasingly important. Controllers should become well-versed in non-financial reporting frameworks, enabling them to align financial strategies with sustainability goals and communicate these efforts effectively to stakeholders.In our interconnected global economy, maintaining awareness of international regulations is paramount as well. Controllers must develop strategies that ensure compliance across various markets while harmonizing financial management practices, safeguarding company operations across borders. Cybersecurity vigilance is another critical area. Financial controllers must prioritize financial data security, implementing robust data governance measures and staying informed about the latest cybersecurity best practices to protect the company's financial information and reputation.Finally, controllers should actively engage in strategic business discussions and investment decisions. By doing so, they position themselves as chief value officers and vital business partners, contributing significantly to the company's strategic direction and value creation. This strategic business involvement ensures that controllers are not just number crunchers – but key players in shaping the future of their organizations. Aaron C. Escartin is a Global Compliance and Reporting (GCR) Tax Partner of SGV & Co.This article is for general information only and is not a substitute for professional advice where the facts and circumstances warrant. The views and opinions expressed above are those of the author and do not necessarily represent the views of SGV & Co.

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13 May 2024 Bonar A. Laureto

Climate resilience: Innovations in Philippine businesses

Due to escalating climate challenges, Philippine businesses must redefine resilience by navigating risks and capitalizing on emerging opportunities. The previous article explored the foundational principles of climate resilience, emphasizing the imperative for Philippine businesses to adapt and thrive amid climate threats. The discussion highlighted how understanding and managing both physical and transition risks are crucial, alongside strategic shifts towards sustainability that bolster growth and help secure a competitive advantage. This article explores how leading companies are leveraging their proactive strategies to improve their market position and drive long-term value, as shared by these companies at the recently held SGV Knowledge Institute event entitled Climate Convergence: Actions Toward a Resilient Future. Energy Development Corporation (EDC): Proactive risk managementEDC's risk management strategies, born from firsthand experiences with climate-related disasters, illustrate the importance of preparedness and adaptive operations. Their structured approach not only safeguards against immediate risks but also builds a foundation for resilience, showcasing how businesses can thrive amid environmental uncertainties.When Super Typhoon Yolanda hit EDC's geothermal power plants in Leyte, it took them four months to restore generation capacity. In response, the company invested over ₱350 million in resilience measures to typhoon-proof its Leyte plants. Concurrently, EDC reinforced its dedication to climate change mitigation by committing never to build, develop, or invest in coal-fired power plants. In addition, EDC launched the Net Zero Carbon Alliance framework that aims to help its partners achieve carbon neutrality. JG Summit Holdings, Inc.: Systematic climate hazard mitigationJG Summit Holdings, Inc.’s strategy to assess and enhance resilience against projected climate hazards showcases their approach to safeguarding assets. Specifically, they initiated a pragmatic strategy to progressively enhance resilience across its portfolio. The conglomerate has also launched initiatives to integrate climate risk intelligence into its strategic businesses processes. Using a data-driven approach, it leverages the latest climate science and granular Philippine-specific data to thoroughly assess its facilities' exposure to climate hazards. Concurrently, the conglomerate conducts vulnerability assessments on select business-critical facilities to evaluate their ability to withstand extreme weather events, shaping retrofitting strategies, refining maintenance protocols and emergency response plans, and establishing necessary backups and redundancies applicable across their portfolio. Central to these efforts is capability building, with significant investments in training risk managers to interpret and utilize climate data at scale. SteelAsia Manufacturing Corp. (SteelAsia): Pioneering green steel productionSteelAsia’s journey toward a net-zero future by 2050 demonstrates a transformative approach to decarbonization and managing transition risks. By integrating advanced technologies and prioritizing the use of recycled materials, SteelAsia is reducing its carbon footprint and aligning itself with global demands for sustainable building materials. These solutions include using recycled scrap steel and electric arc furnace (EAF) technology powered by renewable energy, allowing SteelAsia to reduce its emissions intensity (ton of CO2 produced per ton of steel) by 87% compared to the industry-standard Blast Furnace-Basic Oxygen Furnace method. By adopting the cleanest technologies and learning from global advancements, SteelAsia has emerged as a global leader in green steel production, achieving one of the lowest emission rates in a traditionally hard-to-abate sector. In addition to direct emissions reductions, avoiding the cycle of exporting scrap only to import finished products enables SteelAsia to significantly cut emissions along the entire supply chain and deliver steel to its customers more quickly and efficiently. Compared to global competitors, SteelAsia offers dual benefits: their locally produced green steel reduces customers’ embodied emissions and ensures shorter wait times.Nickel Asia Corporation (NAC): Reimagining mining with sustainabilityNAC is actively enhancing its environmental protocols by adopting sustainable mining practices, such as obtaining Science Based Targets initiative (SBTi) certification and implementing comprehensive emission management strategies. These initiatives demonstrate NAC’s commitment to reducing its ecological footprint while maintaining profitability, setting a benchmark for sustainable practices in the mining sector.They tackled one of mining’s main emissions source — fuel used in operations and mineral transport — by investing in low-emission technologies like hybrid excavators that improve fuel efficiency and cut fuel costs. These efforts will have reduced an estimated 35,000 tCO2e in Scope 1 and 2 emissions by 2025, merging sustainability with operational efficiency.BDO Unibank, Inc.: Leading with sustainable financeThrough its Sustainable Finance Framework, BDO supports projects that offer environmental and social benefits, aligning investment with sustainable growth. This proactive approach addresses the financial aspects of climate resilience and emphasizes the financial sector’s role in fostering a sustainable future. Since 2010, its Sustainable Finance Desk under the Institutional Banking Group has financed projects that pursue energy efficiency, pollution prevention and control, and sustainable management of natural resources and land use.In particular, BDO has directed a significant portion of its business lending — 34% — toward environmental and social projects. Its ASEAN sustainability bond program, the largest of its kind in the Philippines, raised PHP52.7 billion for 39 projects encompassing renewable energy, roads & basic infrastructure, affordable housing, food security, and other green and social initiatives. Additionally, BDO has issued USD150 million worth of green bonds that finance seven renewable energy projects across wind, biomass, and hydro. More recently, BDO introduced a USD100 million blue bond program, the first of its kind in the country, dedicated to financing projects that enhance bulk water supply and improve wastewater management.SGV & Co. (SGV): Walking the talkSGV is at the forefront of managing its climate risks and spearheading solutions that empower its clients to enhance their management of climate risks and opportunities. The firm has taken decisive action to reduce its emissions, with a particular focus on power consumption, the primary source of its emissions. By transitioning to renewable energy sources under the Department of Energy’s Green Energy Option Program (GEOP), the firm has made significant strides in cutting down emissions related to electricity. This program enables consumers to switch from conventional energy supplies to renewable sources within the country. Part of the Firm’s portfolio of initiatives includes producing thought leadership reports and articles on sustainability and relevant regulations surrounding it, as well as crafting the annual SGV Sustainability Report and Beyond the Bottom Line publications.SGV has further strengthened its capabilities to confront climate-related challenges by establishing a robust climate risk advisory team composed of climate science, geology, and engineering professionals. This strategic development equips the firm to analyze projected climate hazards, develop localized climate hazard information, and perform in-depth vulnerability assessments across assets and portfolios — overcoming a major hurdle in crafting effective climate resilience strategies for its clientele.Advancing the country’s sustainability journeyToday, Philippine companies are not only safeguarding their future – they are actively shaping the narrative of sustainable development within the country. As we can see from the above examples, businesses, in close cooperation with Government, are pivotal in steering the country toward a resilient, sustainable trajectory.In a rapidly evolving business landscape, further shaped by the pressing imperatives of climate dynamics, trailblazing entities can offer blueprints for action. Through innovative approaches to the intertwined risks and opportunities of climate change, companies can find new ways to gain a competitive edge in an economy increasingly defined by sustainability.  Bonar A. Laureto is a Sustainability Services Principal of SGV & Co.This article is for general information only and is not a substitute for professional advice where the facts and circumstances warrant. The views and opinions expressed above are those of the author and do not necessarily represent the views of SGV & Co.

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22 April 2024 Ana Katrina C. De Jesus

Achieving transfer pricing certainty

Global tax reforms are leading to double taxation risks, which are significantly altering businesses' approaches to transfer pricing certainty and operational transfer pricing needs. Other key concerns include inflation, increased focus on enforcement by tax authorities, environmental, social, and governance (ESG) pressures, advancements in transfer pricing related technologies, such as generative artificial intelligence (GenAI) and transfer pricing dispute resolution tools, and changes in supply chain.EY recently released the results of the 2024 International Tax and Transfer Pricing Survey, which sampled 1,000 senior tax and finance professionals from large companies in 47 jurisdictions across 19 industries. The survey, which was conducted by an independent provider, highlighted the increasing need for businesses to implement robust transfer pricing policies given new international tax risks. This article will tackle the concerns, challenges, and considerations that were identified by surveyed tax and finance professionals.The role of transfer pricingHistorically, transfer pricing has followed a linear approach that comprises planning, implementation, compliance, and controversy. With the changing landscape, tax and transfer pricing professionals need to engage more strategically with the broader business, considering the rapid increase in double taxation risks and external pressures. Tax audits are expected to increase and intensify, with transfer pricing identified as a top risk area. Tax authorities are now going beyond traditional, functional interviews by processing more details about taxpayers’ global operations, including how tax obligations and business activities align in specific jurisdictions.The data accessed by tax authorities in this evolving tax controversy landscape can be utilized by GenAI and related technologies, allowing them to conduct the audit and process data more effectively. Public regulatory filings, social media profiles, news articles and intellectual property registrations are some sources of information that tax authorities can analyze to evaluate risks and challenge a taxpayer’s position. In the Philippines, the Bureau of Internal Revenue (BIR) identified the creation of a Transfer Pricing Office as a priority program for 2024. The new office will be expected to monitor compliance with transfer pricing documentation requirements, including the preparation and maintenance of local files, master files, and country-by-country reports (CbCR). These will be done pursuant to the minimum standards of the BEPS Action Plans as basis for strategic decision making and managing tax compliance risks. Consequently, the BIR will be keeping a keen eye on cross-border transactions to ensure fair and accurate allocation of costs and profits. The need to standardize data to manage tax controversyTraditional transfer pricing operations are labor-intensive, especially those focused on compliance. Reconciliations and adjustments should ensure that intercompany pricing policy continuously occurs throughout the year, and not just by the end. Gathering the required data for fact-finding, such as financials, taxes, and supply chain information for open years, becomes a challenge when sourced from multiple systems and jurisdictions. This is particularly evident in tax audit cases because taxpayers are expected to respond within a limited period.Increased technology adoption can enable traditional operations and compliance functions in this changing landscape. Taxpayers must plan ahead to harness the power of data, systems, and technology. Investing in data strategy system improvement and advanced operational transfer pricing technology or partnering with a service provider who has built these capabilities can facilitate transfer pricing certainty.Likewise, tax and transfer pricing professionals may resort to GenAI tools to align the group’s transfer pricing policies while identifying and addressing tax risks. This will revolutionize how professionals prepare, analyze, and present data to ensure that their positions are clear, defensible, and easily understood. With the rollout of Pillar Two and disclosure of CbCR in a number of tax jurisdictions, businesses must standardize their internal data to efficiently manage tax authority controversy and Pillar Two calculations. The Philippines has recently accepted the invitation from the Organization for Economic Co-operation and Development (OECD) to join the Inclusive Framework on BEPS, but the country has yet to see local adoption of the Pillar Two rules. To be proactive, tax and transfer pricing professionals should start standardizing their internal transfer pricing data.Transfer pricing certainty through dispute resolution programsTransfer pricing certainty can be realized through various factors, such as increasing interest in advance pricing agreements (APA), mutual agreement procedures, and other dispute resolution programs by tax administrations. In some tax jurisdictions, the International Compliance Assurance Program (ICAP) is considered as a pre-filing and dispute resolution mechanism. Moreover, the ICAP coordinates between a multinational enterprise (MNE) group and multiple tax administrations through the effective use of transfer pricing documentation, including the MNE group’s CbCR to improve multilateral tax certainty.In the Philippines, there have been discussions on the upcoming release of the APA Guidelines to fortify transfer pricing implementation. APA is a mechanism where the tax authority and the taxpayer would agree in advance on the appropriate set of criteria (e.g. transfer pricing method, comparables and appropriate adjustments) to ascertain the transfer prices of controlled transactions over a fixed period of time. Tax and transfer pricing professionals foresee that unilateral APAs will be useful to manage transfer pricing related controversy. How can professionals prepare for the new reality?Tax and transfer pricing professionals highlight escalating concerns regarding double taxation and broader tax and legislative changes, emphasizing the emergence of a new era where businesses are seeking more certainty in their transfer pricing positions.Some measures that companies can take include: focusing on transfer pricing certainty, mapping out future and current dispute resolution mechanisms, centralize processes around standard data to decrease risk, prepare for increasing application of data to define the company’s transfer pricing approach. These measures, naturally, will require the cooperation and support of the company’s C-level executives. Realizing transfer pricing certaintyTax and Finance departments should prioritize transfer pricing certainty through standardized data, modified processes, and technology adoption to facilitate dispute resolution. Tax and transfer pricing professionals should collaborate more closely with the C-suite in making business decisions to enhance certainty from the outset of any business changes and to effectively navigate the evolving regulatory environment.Internally, tax and transfer pricing policies should align with the organization’s broader public image. Externally, pre-filing and dispute resolution programs should be considered.Preparation is also key. Taxpayers that invest in modern transfer pricing approaches will be adequately equipped to engage with tax authorities in future controversies and better support their positions. Finally, tax and transfer pricing professionals must recalibrate and adapt their strategies in response to the changing global tax and economic landscape. Ana Katrina C. De Jesus is a Tax Principal of SGV & Co.This article is for general information only and is not a substitute for professional advice where the facts and circumstances warrant. The views and opinion expressed above are those of the author and do not necessarily represent the views of SGV & Co.

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