About Course

Overview

The Analytics for Retail Banks course is designed to help professionals in the banking and financial services sector harness the power of data analytics to improve decision-making, customer experience, and business growth.

Retail banks today generate massive amounts of data from transactions, digital interactions, and customer profiles. By learning how to analyze this data, professionals can identify customer behavior patterns, mitigate risks, prevent fraud, optimize operations, and design personalized banking services.

This program combines statistical analysis, machine learning concepts, and banking case studies to provide a hands-on understanding of data-driven strategies in the retail banking domain.


Course Details

  • Duration: 6–8 weeks (online or classroom)

  • Learning Format: Online / Blended with case studies

  • Prerequisites: Basic knowledge of banking operations and statistics

  • Tools Used: Excel, SQL, Python, R, Power BI/Tableau

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What Will You Learn?

  • Understand core analytics concepts in the context of retail banking
  • Apply statistical and machine learning models to banking problems
  • Design and implement fraud detection and risk management strategies
  • Use customer data for segmentation, targeting, and personalization
  • Leverage visualization tools to make data-driven business decisions

Course Content

Module 1: Introduction to Retail Banking Analytics

  • Overview of retail banking operations
  • Importance of data analytics in modern banking
  • Data sources in retail banking (transactions, CRM, social media, etc.)

Module 2: Data Management in Banking

Module 3: Customer Analytics

Module 4: Risk and Fraud Analytics

Module 5: Marketing and Sales Analytics

Module 6: Operational Analytics

Module 7: Advanced Banking Analytics

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