Regulatory expectations – increasing supervisory focus on model governance and responsible AI practices. Expansion and growing use of models, especially those leveraging AI and Machine Learning techniques. Evolution of model tiering methodologies to capture the specific risks and complexities of AI-driven models. Evolution of model risk assessment methodologies to ensure adequate evaluation of AI/ML model behavior and performance. Definition of an AI risk appetite, establishing clear boundaries for acceptable levels of model uncertainty
Embedding a Data-First Mindset The Role of Risk Culture Mastering Transformation: Effective Change Risk Management Adapting to Evolving Regulations: Compliance Without Stifling Growth Leveraging Alternative Data: Enhanced Insight and Financial Inclusion Adapting to evolving regulatory requirements while maintaining strong risk governance and compliance Utilizing alternative data sources to improve risk insights and expand financial inclusion
The latest Failure to Prevent Fraud legislation and managing the Compliance extra-territoriality risk in a global organisation
Understanding how geopolitical risks and developments can impact financial markets Assessing a portfolio’s exposure to geopolitical risks (historical vs forward looking analysis) Mapping geopolitical events and investment actions Understanding the long-term consequences of secular political shifts on financial markets Utilizing AI-powered risk modeling to improve early warning systems and loss forecasting Automating credit decisioning processes to enhance efficiency and reduce operational costs Integrating alternative data sources to expand financial inclusion and refine risk assessments
This session examines due diligence challenges in managing high-risk relationships and transactions, exploring techniques for in-depth integrity reviews and the implications of rising misinformation.
What are the key elements of a robust Lombard Risk Management Framework? How to build a Strategic Lombard Risk Model Framework? How to effectively apply the Lombard Risk Management Framework during times of uncertainty and market stress?
Leveraging big data analytics to enhance risk assessment and credit decisioning Integrating real-time data insights to improve portfolio management and loss forecasting Utilizing AI-driven models to refine credit scoring and mitigate default risks Strengthening data governance frameworks to ensure regulatory compliance and reporting accuracy
History doesn’t repeat itself, but it rhymes: Drawing on the past to anticipate changes in the future Stress-testing in turbulent times: Navigating disruptions to energy, trade, and capital flows amid rising global tensions Shielding balance sheets and forming strategies: Staying agile and aligned in a rapidly shifting geopolitical landscape
Introduction Simplified taxonomy Main CCR metrics RISK MITIGATIOn (legal and margining) Making a « ccr » credit decision Conclusion and q&a
Principles for trustworthy AI and AI culture AI governance, tackling regulatory ambiguity, considering model risk management framework, establishing an AI governance mesh Challenge of inventories: How to bring AI use cases and model inventory together?
AI Gold Rush – Innovation outpacing Governance From Local Failures to Systemic Risk : What's changing Drivers of Systemic Fragility in AI-integrated Economies Principles for building Resilient and Diversified AI Ecosystem
Understanding key geopolitical risks impacting the banking sector today Effective strategies for identifying and mitigating geopolitical threats in an evolving global landscape Building resilience to navigate geopolitical crises and minimize operational disruptions Preparing banks for future geopolitical challenges and ensuring long-term risk management
Transforming a traditional lending street Design principles that will reduce time to cash drastically Examples of the new lending journey's Required cultural change
Supervised ML in transaction monitoring introduces risks like label reliability, drift, interpretability, bias, and compliance challenges. AI-driven CDD reviews for low-risk customers face issues with drifting labels and regulatory compliance. Fairness concerns in TM and CDD arise when AI models influence transaction monitoring and risk ratings without transparent processes
Managing risk frameworks effectively during banking entity closures or run-offs. Strengthening liquidity management and crisis response mechanisms. Adapting internal controls to meet new regulatory requirements such as DORA transposal. Building organizational agility to navigate uncertainty and maintain operational resilience
Transition matrices are widely used in credit risk, but they present challenges such as missing transitions that require ad-hoc fixes and complex dependencies that make stress adjustments difficult. How to estimate the rate intensity matrix from observed data Using neighboring elements to infer missing transitions Applying transparent stress adjustments by modifying rating intensities directly
Developing comprehensive third-party risk management frameworks to assess and monitor vendor relationships Implementing due diligence processes for selecting and onboarding vendors, ensuring alignment with security and compliance standards Establishing ongoing monitoring and performance evaluation mechanisms to identify and address potential risks Creating contingency plans and clear exit strategies to manage disruptions or failures in third-party services
