| Attribute | Detail |
|---|---|
| Format | Online (e-LMS) |
| Level | Advanced |
| Duration | 8 Weeks |
| Certification | e-Certification + e-Marksheet |
| Fee | ₹5499 / $59 |
| Tools | Python (Pandas, Scikit-learn) TensorFlow PyTorch NetworkX (Graph Analysis) XGBoost & Random Forest SMOTE (Imbalanced-learn) RNNs / LSTMs Explainable AI (XAI) |
About the AI for Fraud Detection in BFSI: Navigating Financial Integrity Course
As digital transactions in India skyrocket through UPI and mobile banking, the battlefield of financial security has fundamentally shifted. In 2026, static rules are no longer enough to stop sophisticated bad actors. To protect assets and maintain institutional trust, the BFSI sector is turning to autonomous fraud detection powered by AI.
The AI for Fraud Detection in BFSI Course dives deep into the mechanics of financial crime and the AI systems designed to stop it. You will learn to manage high-velocity transaction data, build robust feature pipelines, and deploy models that can distinguish between a legitimate customer and a fraudulent attempt in milliseconds.
The goal is not to replace compliance officers or risk analysts. It is to build professionals who can design, deploy, and govern AI systems that make financial institutions genuinely fraud-resistant.
Aim
AI in fraud detection sits at the intersection of:
- Digital India's explosive UPI and mobile banking growth
- Increasingly complex threat vectors including mule accounts and synthetic identities
- RBI and global regulatory demands for advanced AML and counter-financing tools
- The need for precision AI that reduces false positives without blocking legitimate customers
Program Highlights
Most fraud detection training is either too generic or too theoretical. This course is built specifically for the Indian BFSI context — covering UPI scams, RBI compliance, and India-relevant datasets — while integrating cutting-edge techniques like graph-based fraud ring detection and Explainable AI for regulatory reporting. The capstone project demands both a working model and a compliance-ready explainability report, reflecting the real-world expectations of financial institutions.
Course Curriculum
• Use unsupervised learning for anomaly detection
• Build real-time transaction monitoring pipelines
• Detect insurance fraud using vision and text analysis
• Apply graph-based AI to uncover fraud rings
• Deploy secure, scalable models in banking environments
• Implement Explainable AI for regulatory reporting
• Design end-to-end fraud classification solutions
Module 1 — AI & Financial Integrity Foundations
- History of financial fraud: from traditional methods to digital-age scams
- The mathematics of risk: probability and statistics for fraud
- AI fundamentals for BFSI professionals
- Overview of the Indian regulatory landscape (RBI, DPDP Act)
Module 2 — Data Engineering & Feature Pipelines
- Handling imbalanced datasets where fraud is a needle in a haystack
- Feature engineering: identifying red flag variables in transaction logs
- Data preprocessing for high-velocity financial streams
- Synthetic data generation using SMOTE for model training
Module 3 — Model Architecture & Algorithm Design
- Unsupervised learning for anomaly and outlier detection
- Supervised learning for fraud classification using XGBoost and Random Forest
- Deep learning for sequential pattern recognition using RNNs and LSTMs
- Graph neural networks for fraud ring identification
Module 4 — Training, Hyperparameter Optimization & Evaluation
- Tuning models for high recall to ensure no fraud is missed
- Optimizing thresholds to balance security and customer experience
- Evaluating models using Precision-Recall curves and AUC-ROC
- Cross-validation strategies for imbalanced financial datasets
Module 5 — Deployment & MLOps
- Building real-time inference engines for transaction approval
- Monitoring for concept drift as fraudsters evolve their tactics
- Secure model deployment within banking firewalls
- Logging, auditing, and rollback strategies in production
Module 6 — Ethics, Bias & Responsible AI
- Preventing algorithmic bias in credit and fraud scoring
- Explainable AI (XAI): providing reasons for flagged transactions to regulators
- Data privacy and the DPDP Act (India) compliance
- Ethical design principles for high-stakes automated financial decisions
Module 7 — Industry Integration & Case Studies
- Case study: detecting UPI-based phishing scams in India
- AML (Anti-Money Laundering) patterns in global banking
- Insurance fraud detection using computer vision and text analysis
- Real-world project: building a credit card fraud classifier
Module 8 — Capstone: End-to-End AI Fraud Detection Solution
- Define a fraud detection problem in a BFSI context
- Select and justify the appropriate AI and graph-based methodology
- Build an integrated model that detects anomalies and classifies fraud type
- Deliver an explainability report for compliance and regulatory review
Tools, Techniques, or Platforms Covered
Python (Pandas, Scikit-learn) TensorFlow PyTorch NetworkX (Graph Analysis) XGBoost & Random Forest SMOTE (Imbalanced-learn) RNNs / LSTMs Explainable AI (XAI)
Real-World Applications
The skills from this course are directly applicable to public and private sector banks, fintech startups, insurance providers, and NBFCs. Graduates are prepared to lead fraud prevention teams, design real-time transaction monitoring systems, build AML compliance pipelines, and deploy the next generation of financial security infrastructure across India's rapidly expanding digital economy.
Who Should Attend & Prerequisites
This course is particularly suited for:
- Banking professionals looking to transition into financial security roles
- Compliance and risk officers wanting to understand the mechanics behind AI tools
- Data analysts seeking specialized roles in the high-paying BFSI sector
- Students aiming for a career in financial technology and fraud prevention
- Fintech professionals building transaction monitoring and risk scoring systems
- AI practitioners entering banking, insurance, or regulatory technology
Certification
