Reliance on IRB and non-IRB models to balance historical data with emerging risks Wednesday 26th November - Credit Risk Importance of a flexible risk pricing setup, particularly under CRR3 and SA floor requirements Incorporating forward-looking features and overrides, including climate-related risk drivers Addressing technology infrastructure constraints to meet the high demands of credit models
Overcoming data quality and availability challenges to ensure accurate and timely credit risk assessment Leveraging AI and machine learning to enhance credit scoring, fraud detection, and predictive modeling 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
Enhancing credit scoring models with AI-driven predictive analytics for more accurate risk evaluation Leveraging machine learning to detect fraudulent activities and mitigate financial crime risks 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
Adapting to increasingly complex regulatory requirements to ensure compliance and risk mitigation Leveraging advanced data management solutions to maintain accuracy and transparency in reporting Implementing AI-driven tools to enhance regulatory monitoring and fraud detection Strengthening governance frameworks to proactively address emerging compliance risks
Harnessing machine learning algorithms to predict and manage credit risk with unprecedented precision Using predictive models to enhance loan underwriting and reduce default rates Exploring the role of behavioral data in forecasting creditworthiness and future risks Integrating AI-driven insights with traditional models to create a more comprehensive risk strategy
Modeling credit risk before the foundations of model risk management were established Can extensive regulatory guidance for credit risk models be simplified by adopting more generalized principles for measuring and managing model risk levels? Can unpredictability be mitigated by incorporating a certain level of conservatism? How can we maintain predictability and transparency with advanced modeling approaches?
Embracing digital-first risk models to assess creditworthiness in a virtual economy Integrating real-time data and automation to accelerate decision-making and risk mitigation Leveraging AI to redefine traditional risk parameters and enhance credit assessments Adapting to new consumer behaviors and digital lending trends reshaping credit risk landscapes
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
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
Introduce participants to the latest UN-issued demographic forecast through 2100, focusing on regional and country-specific trends. Examine the social, geopolitical, and economic impacts of demographic shifts. Invite views from the audience on near-term impacts and potential policy responses. Discuss methods to understand unfolding changes, including historical examples and expert opinions.
Implementing Conduct into Credit and Operational Risk Management
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
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
Developing robust crisis management frameworks to respond swiftly to operational disruptions Creating comprehensive business continuity plans that ensure minimal downtime and service continuity Implementing regular testing and simulations to assess the effectiveness of crisis response strategies Establishing communication protocols and leadership structures to guide decision-making during crises
Utilizing blockchain technology to ensure data integrity and secure transactions in the face of evolving cyber threats Implementing AI-driven solutions to detect anomalies and potential security breaches in real time Leveraging machine learning algorithms to continuously improve threat detection and response capabilities Integrating these technologies into a comprehensive cybersecurity strategy to strengthen defenses and minimize risks
Utilizing data analytics to identify and assess operational risks, enabling proactive risk management Leveraging predictive analytics to anticipate potential risks and improve decision-making in real time Integrating data-driven insights into employee training programs to enhance awareness and risk mitigation strategies Using analytics to track training effectiveness and continually refine risk management approaches across the organization
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
Expectations on how banks should identify, measure, monitor and manage ESG risks Plans to address ESG risks in the short-, medium- and longterm ESG scenario analysis – feedback from public consultations
