Banking Risk Management Summit 26-27-28 November 2025
Posted on 2025-11-26 09:00:00
Posted on 2025-11-26 09:00:00
Chief Model Risk Officer Banco Santander Denmark
Head of Enterprise Risk & Credit Risk Management Rietmu Banka, Latvia
Head of Provisioning and AI Model Validation Nordea, Denmark
Head of Model Risk Management & Validation Commerzbank, Germany
Director, Global Head of Business Intelligence Unit | Anti-Financial Crime ,Deutsche Bank
Product Owner Credit Risk Model Validation ING, Netherlands
Head of Risk Analytics Luminor Bank, Lithuania
Group Chief Credit Officer Quintet Private Bank
Senior Analyst Swedbank, Sweden
Director – UK Head of Credit Societe Generale, UK
Nationwide, UK Head of Retail IRB Modelling | Risk Mo
Risk Manager Financial Markets Rabobank, Netherlands
Head of Personal Banking Denmark Risk Management Nordea, Denmark
CRO SEB, Latvia
Head of Investment Risk UBS, Switzerland
Operational Resilience Scenario Planner Lloyd's Banking Group, UK
Managing Director Operational Risk Barclays
Vice President - Operational Risk Citi, Hungary
Head Group Regulatory Compliance Officer Raiffeisen Bank , Austria
Director | Operational Risk & Cyber Security Control UBS, Poland
Reliance on IRB and non-IRB models to balance historical data with emerging risks . 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
Angel MenciaOvercoming 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 .
Edgars SedovsModel Risk Management of AI Models
Jens Jakob Baltzer RasmussenEnhancing 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 .
Ratul AhmedAdapting 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 .
Christopher NasonHarnessing 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 .
Michiel van LunsenModeling 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?
Darius GrinvaldasEmbracing 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 .
Fernando BlauzwirnDefining AI risk protection objectives to ensure safe and ethical deployment . Exploring strategies for robust AI governance and accountability . Assessing AI models for fairness, transparency, and compliance . Integrating AI governance into broader risk management frameworks .
Ulf HolmbergIdentifying key counterparty credit risk factors to enhance risk assessment and mitigation . Implementing effective strategies for exposure measurement and collateral management . Strengthening risk governance through regulatory compliance and stress testing frameworks . Leveraging technology and data analytics to enhance real-time risk monitoring . Integrating counterparty credit risk management into broader enterprise risk strategiesstress-testing .
Aymeric ChauveLeveraging 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 .
Zsolt JaczkoUtilizing non-traditional data sources such as transaction history and social behavior to improve credit scoring models . Integrating alternative data to enhance risk assessment, especially for underbanked and underserved populations . Leveraging machine learning to analyze unconventional data and predict creditworthiness more accurately . Exploring emerging trends in fintech and data innovation to expand financial inclusion and reduce credit risk .
Carl DensemImplementing Conduct into Credit and Operational Risk Management
Marie Leth ChristensenUnderstanding 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 .
Karlis DanevicsExploring cutting-edge fraud detection technologies, such as AI and machine learning, to identify suspicious activities . Implementing real-time monitoring systems to detect and prevent fraud before it impacts operations . Developing a multi-layered approach to fraud prevention, combining data analytics, employee training, and customer awareness . Strengthening internal controls and creating a fraud response plan to minimize operational losses and protect customer trust .
Dirk EffenbergerDeveloping 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 .
Jack SmartUtilizing 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
Steve PortwayUtilizing 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 .
Márton NagyDeveloping 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 .
Stefan ZimaEstablishing robust internal controls and auditing processes to detect and prevent operational errors and fraud Implementing employee training programs to raise awareness and reduce the risk of non-compliance . Leveraging automation and data analytics to enhance accuracy and minimize human error in critical operations . Developing a culture of accountability and transparency to ensure adherence to compliance standards and internal policies .
Grzegorz Dlugajczyk