Validating Financial Crime Risk Models
Description
This book is a comprehensive guide to the validation of models used in fraud detection and anti-money laundering (AML) compliance for financial professionals. Focusing on the critical aspects of data quality, transaction monitoring, and customer risk rating, this book equips readers with the knowledge and tools to ensure that their models meet regulatory standards and effectively mitigate financial crime risk.
As financial crimes become more and more sophisticated, there is a growing need for a specialized resource that addresses both the technical and regulatory challenges financial managers are facing . Offering a framework, tools, and case examples as well as incorporating the role of AI, this book helps readers ensure that their models are robust, reliable, and capable of detecting and preventing financial crimes effectively.
More details
Person
Chandrakant Maheshwari is Lead Model Validator, USA, with over 20 years of experience in banking and finance specializing in model validation, regulatory compliance, and AML analytics. For the past decade he has focused exclusively on Anti-Money Laundering model validation, and is one of the key authors of the ACAMS CAMS7 certification coursework. A published author with Elsevier, and a frequent speaker at conferences, he is recognized for advancing the practical application of AI and machine learning in financial crime risk management.
Content
Chapter 1: Introduction to Financial Crimes Detection Modeling.- Chapter 2: Overview of Financial Crime Models.- Chapter 3: Model Validation within the Financial Crimes Risk Assessment Framework.- Chapter 4: Data Quality and Input Analysis.- Chapter 5: Customer Risk Rating for Financial Crime Compliance.- Chapter 6: Analytical Approaches for Validating Customer Risk Rating (CRR) Systems.- Chapter 7: Transaction Monitoring Models and Their Validation.- Chapter 8: Analytical Approaches to Evaluate and Validate Transaction Monitoring Systems.- Chapter 9: Validating Sanctions Screening Models in Financial Institutions.- Chapter 10: Validating the Tuning Process of Financial Crime Risk Models.- Chapter 11: Fraud Risk Assessment Foundation for Model Development and Validation.- Chapter 12: Transaction Focused Fraud Detection Models: A Taxonomy and Validation Perspective.- Chapter 13: Output Validation of Fraud Detection Black Box Models.- Chapter 14: Ongoing Performance Monitoring of Fraud Detection Models.- Chapter 15: Foundations and Workflows of Large Language Models.- Chapter 16: Validation of Generative AI Models in Financial Crime Compliance.- Chapter 17: Validation of Fine Tuned Generative AI Models in Financial Crime Compliance.