📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of AI for Fraud Detection in BFSI, from foundations to a certified capstone project.
Domain
• Card-present, card-not-present, account takeover and synthetic identity
• Authorised push payment and social-engineering fraud, where the customer authorises the loss
• Insurance claim and application fraud, and how their signals differ from payments
Real Time
• Latency budgets and feature availability at authorisation time
• Streaming feature stores and consistency between training and serving
• Fallback behaviour when the model or a feature source is unavailable
AML
• Transaction monitoring, typology rules and the alert-quality problem
• Sanctions and PEP screening, name matching and transliteration failure
• Suspicious activity reporting duties and investigator workflow
Identity
• KYC, document verification and liveness checks
• Device fingerprinting and behavioural biometrics
• Mule account detection and network-based account linkage
Supervision
• Supervisory expectations for model governance in regulated institutions
• Explaining declines and account restrictions to customers and ombudsmen
• Tuning, back-testing and independent validation of monitoring systems
Earn government-registered certification in AI for Fraud Detection in BFSI
e-Certificate and e-Marksheet issued on successful completion.
