Programme Parameters
Graduate / Intermediate
3 Weeks (30 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
AI for Fraud Detection in BFSI focuses machine learning on the adversarial problem of financial crime across banking, payments and insurance. You learn to build models for transaction fraud, claims fraud and identity abuse, using supervised methods on labelled fraud and anomaly detection for the unknown. The course tackles what makes BFSI fraud hard — extreme class imbalance, adaptive fraudsters, real-time scoring and the cost of false positives — and the regulatory and explainability demands of the sector. You finish able to build a defensible BFSI fraud-detection model. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course applies AI to fraud detection in banking, financial services and insurance (BFSI) — transaction, claims and identity fraud, with real-time, imbalanced-data modelling.
📋 Course Objectives
1. Build transaction- and claims-fraud models.
2. Apply anomaly detection to unknown fraud.
3. Handle extreme class imbalance.
4. Score fraud in real time.
5. Meet BFSI explainability and regulation.
👥 Who Should Enroll?
• Fraud and financial-crime analysts
• Data scientists in banking and insurance
• Risk and payments professionals
• Students of financial analytics
🚀 Key Learning Outcomes
• The ability to build BFSI fraud detection.
• A financial-fraud analytics project.
• A cost- and regulation-aware approach.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Fraud Typologies Across Banking and Insurance
• 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
Scoring Inside the Payment Window
• 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
Financial Crime Beyond Fraud
• 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
Onboarding and Authentication Risk
• KYC, document verification and liveness checks
• Device fingerprinting and behavioural biometrics
• Mule account detection and network-based account linkage
Regulatory Expectations and Model Control
• 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
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | Scikit-learn |
| Covered Tool / Platform | Jupyter Notebook |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Hugging Face |
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