Financial Crime Prevention in a Digital Era: Becoming more efficient and effective

Summary

Financial crime prevention in a digital era: becoming more efficient and effective

Financial Crime Prevention is under increasing regulatory scrutiny, requiring banks to enhance efficiency while staying compliant. However, traditional approaches remain costly, labour-intensive, and inefficient. This session starts by outlining the key trends and challenges observed in Financial Crime Prevention before exploring how financial institutions can leverage advanced models, automation, and AI-driven solutions to improve both detection and alert-handling processes while keeping regulators on board.

Anti money-laundering: from static rules to artificial compliance officers

The landscape of AML detection is transforming. In this talk, we’ll share our journey at Zürcher Kantonalbank, where traditional rule-based systems are enhanced with advanced AI techniques. By incorporating statistical models tailored to individual client behavior, unsupervised machine learning for complex detection gaps, and supervised learning to prioritize or resolve alerts, we’ve moved step by step to improve our approach to AML. With large language models (LLMs) we are currently exploring new possibilities for quality control in alert handling.

Presenters

• Zaid Siddiqi, Director Risk Advisory at Zanders
  Zaid Siddiqi is a Director at Zanders with more than 10 years of experience in quantitative risk management. He has supported multiple Swiss and Dutch G-SIBs, D-SIBs as well as mid-sized banks with the development and/or validation of models in Financial Crime Prevention, credit risk, Asset and Liability Management (ALM) and stress testing. For many years, he has conducted research at Zanders on the application of Machine Learning in risk management. He has led multiple projects for G-SIBs related to the application of Machine Learning models in credit risk as well as in Anti-Money Laundering (AML). Zaid holds an MSc in Econometrics and an MSc in Quantitative Finance from the VU University in Amsterdam.
• Dr. Alexander Rudyk, Head of Analytics Credit Risk, Zürcher Kantonalbank
  Alexander Rudyk currently leads the credit risk modeling team at Zürcher Kantonalbank. Previously, he was responsible for the implementation of anti-money laundering (AML) models at the bank. During his tenure in this role, Zürcher Kantonalbank successfully transformed its AML infrastructure through a multi-year initiative, significantly enhancing both coverage and alert quality. Alexander holds an MSc in Applied Mathematics from ETH Zurich and a PhD in Management from the University of St. Gallen.
• Richard van ‘t Klooster, Data Scientist for Non-Financial Risks, Zürcher Kantonalbank
  Richard van ‘t Klooster is a Data Scientist in the Analytics Non-Financial Risks team at Zürcher Kantonalbank. He has prior experience in finance, working in client analytics and internal audit, where he applied machine learning models for outlier detection and churn prediction, contributing to enhanced risk identification and operational efficiency. His current focus lies in leveraging advanced analytics and data-driven solutions to address non-financial risks, covering AML and financial crime prevention. Richard holds a BSc in Aerospace Engineering and an MSc in Management of Technology from Delft University of Technology.

Introduced and moderated by Prof. Dr. Walter Farkas, Board Member Swiss Risk Association & Professor, Program Director MSc UZH ETH Quantitative Finance.

Agenda

17:45 Arrival & Name Tag Collection
18:00 Welcome and Introduction (Prof. Dr. Walter Farkas)
18:15 Project presentation (Zaid Siddiqi & Alexander Rudyk)
19:00 Panel with all the speakers and Q&A (Zaid Siddiqi, Alexander Rudyk, Richard van ‘t Klooster)
19:25 Conclusion (Prof. Dr. Walter Farkas)
19:30 Networking Drinks
20:30 End

Chapter Event

As chapters evolve, synergies are found and the focus of our topics change, we will combine chapter “Risk Analytics and Models” and Stress Testing and Scenario Generation” into the new chapter “Analytical Methods”. We explore analytical approaches to risk management, with a focus on practical applications. Our chapter provides practitioners with a stage to share their latest ideas, tools and methods.