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December 19.2025
3 Minutes Read

FRTB's 2027 Delay: A Challenge or an Opportunity for Banks?

FRTB implementation delay concept: hand using ATM, close-up.

Understanding FRTB: A Necessary Update for Modern Banking

The FRTB, or Fundamental Review of the Trading Book, is a pivotal reform initiated by the Basel Committee on Banking Supervision (BCBS) aimed at rectifying vulnerabilities within the market risk framework, which were starkly highlighted during the 2008 financial crisis. FRTB's primary goals include establishing a more robust framework through improved methodologies for risk sensitivity, enforcing clear limits between trading and banking books for diverse regulatory treatments, and enhancing the methodologies used for risk measurement. Notably, the reform replaces the traditional Value at Risk (VaR) with Expected Shortfall for calculating potential extreme losses, offering a more accurate picture of risk exposure.

The 2027 Timeline: Implications of the Delay

The European Union’s postponement of the FRTB's mandatory implementation until January 1, 2027, arises from the need to level the competitive field with jurisdictions like the U.S., which have also delayed their adoption. Financial institutions now have additional time to adapt their systems to comply with new data standards, aligning with a shift towards more granular, real-time data reporting. However, this delay also brings forth uncertainties, as operational challenges loom large. Banks must remain adaptable to evolving regulations while simultaneously incurring costs to adjust their systems to meet these new demands.

Spain’s Banking Sector: Adapting to Changes

In Spain, the Bank of Spain will oversee the adaptation process during this period of delay. Spanish banks can expect to experience several consequences: increased capital absorption in trading portfolios, a reinforcement of risk systems, and a rise in compliance costs. The adaptation to FRTB presents both challenges and opportunities for these institutions, enabling them to bolster resilience and competitiveness against global counterparts.

Recommended Actions for Spanish Banks

To successfully navigate the transition, Spanish banks are advised to undertake several key actions: revamp their risk infrastructure, enhance capital calculation methodologies, and either validate their internal models or transition to the standardized approach as necessitated by the FRTB. It is crucial for these banks to document and report their impacts to supervisory authorities, maintaining transparency throughout the adaptation process.

Leveraging Technology for Compliance: SAS Solutions

With the challenges posed by FRTB, companies like SAS offer integrated solutions to facilitate compliance under the Comprehensive Risk Regulation (CRR III). Their tools focus on various aspects such as risk calculation, data management, and regulatory reporting. The SAS Risk and Finance Workbench streamlines the orchestration of automated calculations, while their Market Risk Management software provides both granular calculations and stress testing features, ensuring compliance and enhanced risk management capabilities.

Future Trends and Predictions: The Global Landscape

As the implementation of FRTB continues to evolve, the international banking community must brace itself for potential shifts in capital requirements and regulatory expectations. The ongoing dialogue among regulatory bodies, particularly in the EU, U.S., and U.K. regarding FRTB compliance, is vital for maintaining a level playing field for banks globally. Observations from Deloitte highlight that the EU’s push for gradual reform and potential relief measures could pave the way for banks to adapt more economically while still aligning closely with global standards.

Conclusion: The Path Forward

While the delay in the FRTB implementation offers a reprieve for many financial institutions, it underscores the need for proactive measures to ensure compliance and operational efficiency. Financial entities must not only focus on adherence but also view the changing landscape as an opportunity for strategic benefits. As the full implications of FRTB unfold, embracing innovative technologies and recalibrating risk management strategies will be crucial for sustaining competitiveness in an increasingly regulated environment.

In an era where artificial intelligence and advanced analytics are becoming essential in banking operations, institutions that prioritize a learning culture around AI applications will likely emerge as leaders in compliance and risk management.

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12.19.2025

Navigating the Future: Key Changes to Solvency II and AI's Role

Update Understanding the Upcoming Changes in Solvency II The insurance industry is facing significant changes with the impending updates to Solvency II set to take effect in 2027. This regulatory framework, implemented to bolster the resilience of insurance companies across Europe, has been an essential part of maintaining market stability since its inception in January 2016. The revision outlined under Taxonomy 2.10 aims to enhance requirements around capital, technical provisions, and reporting standards. It will require insurance companies to adapt their technological and operational frameworks to ensure compliance, particularly around governance and emerging risks. The Importance of Taxonomy 2.10 Taxonomy 2.10 introduces vital changes that include the creation of tailored categories for smaller non-complex entities and adjustments to pension provisions. These adaptations are designed to ensure proportionality and sustainability, which are becoming increasingly important in today’s economic landscape. For instance, the new governance criteria will integrate ESG (Environmental, Social, and Governance) considerations, reflecting the industry's shift towards sustainable finance. Implications for the Spanish Insurance Market In Spain, where the insurance market is characterized by significant group concentration and international players, these changes will necessitate a comprehensive strategic response. Insurance firms that proactively adapt to the new regulations will not only maintain robust solvency ratios but also capitalize on long-term investment opportunities. The allowance for regulated investment in sustainable ventures not only aligns with broader environmental goals but is expected to draw considerable long-term funds into Spain's clean energy and infrastructure sectors. Technological Adaptation Dimensions The migration of data management and compliance systems, like SAS’s transition to Viya, illustrates a crucial response to the regulatory demands of Taxonomy 2.10. By modernizing their technology infrastructures, insurance firms can enhance reporting accuracy and data processing capabilities to meet the new standards efficiently. SAS's advanced analytics provide insurers with tools to better assess risk and manage compliance proactively, effectively preparing them for future challenges. Risk Management and Investment Strategies As insurers adjust to the evolving Solvency II framework, they should consider aligning their investment strategies with the updated regulatory environment. Emphasizing long-term equities and stability can create a buffer against market volatility. Moreover, insurers should ensure that their risk management practices are not only compliant but also forward-thinking, factoring in climate and ESG risks that could significantly affect the long-term viability of their portfolios. The Path Ahead: Strategic Preparation for Insurers As we approach the implementation of these changes, the pivotal question lies in how insurers will prepare their organizations for this regulatory evolution. Adopting flexible governance structures and investing in robust technology can streamline operations, mitigate risks, and potentially enhance market positions. The firms that harness these regulatory updates as opportunities rather than obstacles will likely thrive in an increasingly complex and demanding insurance landscape. The upcoming Solvency II review is more than just a regulatory change; it’s an opportunity for the insurance sector to strengthen its foundations while innovating for sustainability. The success of this transition will be measured not only by compliance but also by the ability to leverage technological advancements and strategically align with global sustainability efforts.

12.18.2025

How AI Learning Can Revolutionize Corporate Fraud Prevention

Update The Silent and Costly Capital Leak In today’s corporate landscape, few financial drainages are as unnoticed yet damaging as procurement fraud. According to the Association of Certified Fraud Examiners (ACFE), this type of fraud ranks as the second most common among corporations globally. Organizations estimate that they lose an average of 5% of annual revenues to such practices. This translates to a staggering $50,000 lost for every million dollars billed, often due to corruption, inflated pricing, collusion, or duplicate payments. Alarmingly, the average fraud case takes about 12 months to be detected and results in median losses of approximately $117,000. That’s a whole year of deception before any inquiries arise! The Achilles' Heel: The Procurement Chain Recent data from the Anti-Fraud Technology Benchmarking Report 2022 reveals that 41% of organizations monitor fraud risk specifically in procurement, second only to fraudulent payments at 43%. This high statistic isn’t coincidental; the procurement process is rife with opportunities for abuse from supplier selection to invoice approvals, often lacking in oversight and suffering from excessive trust. A toxic mix of bureaucracy, multiple spending authorities, and audits conducted post-payment creates the perfect environment for such fraud. Worse yet, this issue often involves internal collusion, meaning employees and suppliers work in tandem. It doesn’t just hit the bottom line; it undercuts the organizational culture and erodes trust among teams. Why Traditional Analytics Are Not Enough Many companies rely on basic analytical tools that depend solely on historical data. This means fraud is often discovered only after it occurs, making recovery nearly impossible. Furthermore, these systems frequently generate countless alerts without prioritization, causing audit teams to spend time on false positives rather than focusing on genuine threats. A Paradigm Shift: From Detection to Prevention Examples from organizations, such as a Belgian electrical network operator, demonstrate that prevention is indeed possible. By implementing SAS's analytics solutions, the company was able to evaluate risks in real-time, reduce fraud, and refine its procurement processes. The result was less waste, better control, and a unified view of its operations. Conversely, a U.S. government agency lost over $300 million from contracting fraud. Advanced analytics applied to their extensive data, which included 17,800 suppliers, 25,000 employees, and 700,000 payments, uncovered employees with financial interests in supplier companies, ghost invoices, and payments to non-existent employees. Had these issues been detected sooner, potential savings could have exceeded $16 million. Data-Driven Integrity The most effective approach combines data-driven integrity with continuous monitoring, using artificial intelligence, machine learning, and predictive analytics. For instance, SAS has developed over 80 pre-configured models capable of detecting anomalous behaviors, triangulations between employees and vendors, or invoices that bypass internal controls, all contributing to an efficient "Smart Audit." By integrating information from human resources, accounting, and suppliers, it becomes possible to spot patterns of collusion, phantom purchases, or suspicious increases in order sizes. Unlike the traditional "pay then pursue" method, this model allows for fraud prevention before any money changes hands. Beyond Auditing: Embracing Transparency The message is clear: in an era where trust and transparency are as valuable as revenue itself, merely auditing is insufficient; proactive measures must be taken. Procurement fraud doesn’t just drain financial resources; it undermines organizational culture, destroys reputations, and can result in millions in penalties and lost trust. Embracing advanced analytics is no longer a technological luxury; it is a corporate survival strategy. In a data-driven age, integrity must also be analyzed. Final Thoughts on AI’s Role in Preventing Fraud Adding layers of sophistication through AI learning tools can guide companies away from financial pitfalls. Businesses eager to shield themselves from procurement fraud must adopt platforms that utilize machine learning to stay ahead of evolving tactics used by fraudsters. This advancement not only improves their ability to detect issues in real-time but enhances operational efficiency and reinforces trust within their teams. Call to Action In conclusion, understanding the dynamics of procurement fraud is crucial to safeguarding financial integrity. Companies should seriously consider investing in advanced analytical solutions and AI tools that can provide predictive insights and facilitate a proactive stance against fraud. By doing so, they not only protect their resources but also build a fortified culture of trust and confidence.

12.18.2025

Creating Effective AI Solutions for Financial Crime: The Human Element Matters

Update Designing Human-Centric AI Strategies for Financial Crimes The fight against financial crime is evolving rapidly with technology at the forefront. As AI becomes more integrated into financial systems, it’s vital that these tools are crafted with the understanding that technology should serve humanity and not the other way around. This notion was highlighted during the recent discussions surrounding the Grace Hopper Celebration (GHC), emphasizing a people-oriented approach when developing financial crime analytics. Embracing Non-Obvious Thinking for Emerging Threats Caitlin Estrada's insights into the SIFT framework by Rohit Bhargava underscore a crucial point for financial institutions: to succeed, they must first create space for observation and focus on the innovative twists required to detect evolving threats. Cybercriminals are incessantly adapting, leveraging emerging technologies to outmaneuver defenses. In this environment, curiosity should not just be encouraged; it must be harnessed as the cornerstone of effective risk mitigation. Collaboration: A Key Ingredient in Effective Systems At the intersection of technology and human expertise lies the collaborative environment necessary for combating financial fraud. It’s not just about deploying sophisticated algorithms; it’s about fostering a culture where risk analysts can share insights with product managers and engineers. This interconnected approach, highlighted by Utsha Sinha, demonstrates that fraudulent activity detection systems thrive in environments devoid of silos; they flourish in cross-functional teams that integrate diverse expertise. Sustainability in Engineering: A Core Principle Tanaya Salunkhe’s emphasis on sustainable engineering highlights an important shift in mindset. No longer is sustainability seen as a secondary priority; for contemporary financial institutions, it must be a foundational element in real-time fraud detection. By enhancing the efficiency of systems, banks and financial corporations can lower costs and environmental impact simultaneously. Automating model training and employing lightweight architectures will become essential methodologies for future fraud prevention. Data Privacy in an AI-Driven World As more data permeates the financial sector, ensuring robust cybersecurity measures becomes paramount. Engaging cybersecurity at the human level, as Neelima Kumar brings attention to, is essential for comprehending and mitigating financial crime risks effectively. Financial institutions must instill confidence in their ability to secure sensitive data while complying with evolving regulations. AI’s Cartography of Financial Crime: A Road Ahead Emerging AI technologies present vast opportunities for financial crime compliance. As detailed in reference articles, the growing utilization of AI and machine learning enhances anti-money laundering (AML) capabilities. Financial institutions can now leverage AI to detect intricate patterns in financial transactions, diminishing false positives while increasing the speed and accuracy of investigations. This is an exciting time, as data analytics blend with human insight to unearth and counteract complex threat landscapes. Conclusion: A Call to Action for Financial Institutions As we look to advance the interplay between AI technology and financial crime detection, it’s crucial to keep human values at the core. Proactively engage teams across various specializations, prioritize ethical AI practices, and ensure that systems are transparent and accountable. The future of finance rests not solely on algorithms but on their capacity to embrace the full spectrum of human experience and insight. Now is the time for institutions to take bold steps towards revolutionizing financial crime compliance.

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