Training course on Big Data Analytics for Banking

Module 1: Introduction to Big Data in Banking

Definition, characteristics (Volume, Velocity, Variety, Veracity, Value)

The role of big data in transforming financial services

Case studies from global banks

Module 2: Big Data Ecosystem and Tools

Data warehouses, data lakes, and cloud platforms

Tools and technologies (Hadoop, Spark, NoSQL databases)

APIs and real-time data integration

Module 3: Data Management and Governance

Data quality, cleaning, and preprocessing

Metadata and master data management

Regulatory compliance (GDPR, data localization, financial data protection)

Module 4: Risk Management with Big Data

Credit risk modeling with alternative data

Market and liquidity risk analytics

Stress testing using high-frequency and granular datasets

Module 5: Fraud Detection and Financial Crime Analytics

Detecting anomalies with machine learning and network analysis

Real-time fraud detection in payments and card transactions

Anti-money laundering (AML) applications of big data

Module 6: Customer Analytics and Personalization

Customer segmentation and behavioral profiling

Predictive models for churn and retention

Cross-selling and tailored product recommendations

Module 7: Big Data in Compliance and Regulatory Reporting

SupTech and RegTech applications for regulatory compliance

Automating KYC and transaction monitoring

Using big data for stress testing and systemic risk analysis

Module 8: Predictive and Prescriptive Analytics in Banking

Time series forecasting for interest rates, FX, and demand

Decision optimization using prescriptive analytics

AI-powered lending and investment strategies

Module 9: Visualization and Communication of Insights

Dashboards for risk and business intelligence

Tools: Tableau, Power BI, Python visualization libraries

Best practices for communicating analytics to executives

Module 10: Emerging Applications of Big Data in Banking

Blockchain and big data convergence

Open banking and API-enabled ecosystems

AI-driven robo-advisory and digital lending platforms

Module 11: Ethical and Security Challenges in Big Data

Bias, fairness, and responsible use of customer data

Data privacy and cybersecurity risks

Building customer trust in data-driven banking

Module 12: Practical Workshop and Capstone Project

Hands-on exercise with large financial datasets

Designing a big data analytics solution for a banking problem (risk, fraud, or customer engagement)

Presenting findings and business/policy implications