Programme Parameters
Graduate / Intermediate
4 Weeks (40 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
This intensive 5‑day lab‑focused program equips finance, technology, and security professionals with a comprehensive understanding of how AI combats fraud in modern FinTech and banking ecosystems. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This intensive 5‑day lab‑focused program equips finance, technology, and security professionals with a comprehensive understanding of how AI combats fraud in modern FinTech and banking ecosystems.
📋 Course Objectives
1. Put AI Enablement techniques to work on real datasets and case studies.
2. Produce a reproducible, portfolio-ready project you can cite in a thesis, paper, or job application.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in AI Enablement
• R&D engineers and working professionals applying AI Enablement in industry
• Academics and educators building research or teaching capacity in AI Enablement
🚀 Key Learning Outcomes
• A portfolio-grade AI Enablement deliverable you can defend and extend.
• 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
Day 1 – Fundamentals of Fraud in FinTech and Banking
Explore common fraud types including identity theft, payment fraud, and money‑laundering. • Identify digital transaction and API vulnerabilities within modern banking systems. • Trace the evolution from rule‑based detection to AI‑driven solutions.
Day 2 – Introduction to AI and Machine Learning in Fraud Prevention
Understand supervised vs. unsupervised learning and key algorithms. • Apply AI to real‑time transaction monitoring using behavioral biometrics. • Review case studies such as JPMorgan’s credit‑card fraud reduction.
Day 3 – Advanced AI Techniques for Anomaly Detection
Implement autoencoders, isolation forests, and graph‑based models. • Leverage NLP for anti‑money‑laundering sentiment analysis. • Integrate blockchain and federated learning for secure data sharing.
Day 4 – Implementation and Ethical Considerations
Deploy models via APIs and cloud services such as AWS Fraud Detector. • Mitigate bias, manage false positives, and ensure regulatory compliance. • Adopt best practices for data privacy, model auditing, and scaling.
Day 5 – Emerging Trends & Future Outlook
Explore quantum‑resistant AI and generative‑AI fraud simulation. • Assess AI’s role in DeFi, cross‑border fraud prevention, and zero‑trust architectures. • Identify career pathways and ongoing research opportunities.
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | AWS Fraud Detector |
| Covered Tool / Platform | Autoencoders |
| Covered Tool / Platform | Isolation Forests |
| Covered Tool / Platform | Graph Neural Networks |
| Covered Tool / Platform | spaCy |
| Covered Tool / Platform | NLTK |
| Covered Tool / Platform | Blockchain frameworks |
Frequently Asked Questions
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