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.

• 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.

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.

• 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.

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.

• 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.