OverviewCurriculum

Banking, Financial Services and Insurance(BFSI) Analytics

Overview

BFSI Industry is the leader in utilizing power of analytics and creating opportunity. Not surprising, >30% of current analytic opportunities are from this sector. This program builds a rock solid and fool proof understanding of analytics of 5C’s (Compliance, Competition, Complexity, Customization and Circumvention) in BFSI industry and positions you to make most out of the opportunity & prepares oneself to be business ready for the upcoming stream of disruptive innovation in this industry.

Course Objectives

  • Practical usage of Decision Science in respective domain.
  • Prepare individuals to contribute in their organization’s journey towards data driven innovation.
  • Learn about domain relevant tools and techniques.
  • With financial industry being the largest consumer of analytics, the program designed to open up unlimited possibilities in an individual’s career.

Key Features

  • Encapsulating analytics of 5C’s (Compliance, Competition, Complexity, Customization and Circumvention) in BFSI for a solid learning.
  • Eminent academicians and practitioners as course advisors and faculties.
  • Program accredited and certified by Analytics Society of India.
  • Case study based approach.
  • Course pedagogy comprises of assignment()s and hands on project.
  • Non-disruptive weekend program.
  • Spans across 60 hours of 100% in classroom knowledge sharing.
  • V Connect – Acts as a platform to connect with industry experts and get mentored.
  • Route ╬▒ – Your club to connect and progress.

Curriculum

Overview

  • Overview of Analytics.
  • Overview of analytics in BFSI.

Fundamentals of Statistical analysis

  • Types of data, Central Tendencies, Central limit theorem, Probability distribution, Histogram, Hypothesis Testing, Sampling, Confidence Interval, Correlation.
  • Processes of data quality check, missing data treatment/data imputation, data transformation.

Customer analytics in BFSI

  • Segmentation & Acquisition, Cross Selling of Liability and Asset products.
  • Dynamic Pricing & Reward analytics in Banking & Insurance.
  • Customer Attrition prediction, Customer / Agent retention analytics, Sales Incentives.

Risk, Fraud and Compliance analytics

  • Risk modeling, Credit scoring model, Application scoring model.
  • Fraud Analytics (Banking transactions & Insurance claims – Traditional and Algorithmic).
  • Delinquency prediction, Basel Norms.

Survival analytics

  • Survival analytics in insurance – Life Tables, KapMeier estimates, Predictive hazard modeling.
  • Customer attrition, Loan foreclosure.

Analytics in Securities and Investments

  • Asset allocation optimization, Stock/Forex forecasting.
  • Discounted Cash flow, Profitability Ratio, Value at risk(VaR).
  • Algorithmic trading.
  • Derivatives – Brownian motion, Pricing options and BlackÔÇôScholes formula.

Analytical techniques for practical usage

  • Practical application of following analytical techniques using financial case studies for above topics using R:

– Linear & Logistic regression
– Decision Trees
– Time Series
– Machine Learning – Random Forest
– Custom Algorithms
– Classification & Clustering (KNN, K-Means)
– Optimization