Financial Analytics (for MBA)

Course Duration : 50 hrs+ Case Study
(10% discount for students)

About Financial Analytics

Analytics plays an important in the financial services domain. Applications of analytics are plenty – credit scoring, claims processing, hedging, portfolio analysis, and customer analytics to name a few.
Building models for credit scoring to identify risky customers, identifying fraudulent transactions using pattern detection, identifying cross-sell and up-sell opportunities – these are all examples of application of analytics in the financial services sector.

Course Overview

This course focuses on the following key areas of Financial Analytics:

  • Introduction to Analytics
  • Various Analytics tools and their use
  • Role of Analytics in Fianance
  • Learning R, the popular Analytics tool
  • Fianancial Analytics using R

What We Offer

Hands on experience through practical assignments
Real time industry problems and solutions (Case studies/Projects)
Industry Experienced Faculties
Exclusive Study Material
Interview preparation and placement assistance
State-of-Art Labs with latest Infrastructure

Introduction to Analytics

Chapter-1: Introduction to Analytics

1.1 Key concepts of analytics
1.2 Simple yet powerful techniques
1.3 Draw insights from data
1.4 Where the analytics used
1.5 Some real time examples


Chapter-2: Understanding difference between Analysis and Reporting

2.1 Analysis versus Reporting
2.2 Basic and Advance Analytics
2.3 Conducting an Analysis- Things to consider
2.4 Building an Analytic Team


Chapter-3: Analytical Approaches and Tools

3.1 The evolution of Analytic Approaches
3.2 The evolution of Analytic Tools
3.3 Categories of Analytic Tools
3.4 Some popular Analytical Tools (R/SPSS/SAS)
3.5 Comparison between Analytical Tools


About Financial  Analytics

Chapter-4: The Role of Analytics in Finance

4.1 A Brief History of the Evolution of Analytics
4. 2 Why Is Analytics Important to the Finance Field?
A. Business Model Business to Business Business to Consumer Business to Employee B. Changing Role of Financial Department C. Business progress D. Integrated Analytics E. Role of Data warehouse 4.3 Financial Analytics Defined
4. 4 Uses of Financial analytics


About R Analytics Tool

Chapter-5: Getting Started with R

5.1 Learning objectives
5.2 Download and Install R and R Studio
5.3 Working in the R Windowing Environment
5.4 Install and Load Packages


Chapter-6: Basic Building Blocks in R

6.1 Learning Objectives
6.2 R as a Calculator
6.3 Work with variables
6.4 Understand Data Types
6.5 Store Data in Vectors
6.6 Call Functions


Chapter-7: Advanced Data Structures in R

7.1 Learning Objectives
7.2 Create and Access Information in Data Frames
7.3 Create and Access Information in Lists
7.4 Create and Access Information in Matrices
7.5 Create and Access Information in Arrays


Chapter-8: Reading Data into R

8.1 Learning Objectives
8.2 Reading CSV Files
8. 3 Understanding Excel is not easily accessible in R
8.4 Read Data files from other Statistical Tools


Chapter-9: Basics of Programming

9.1 Learning Objectives
9.2 The Classic “Hello World” Example
9.3 Basics of Function Arguments


Chapter-10: Data Munging

10.1 Learning Objectives
10.2 Repeating Matrix Operations – the apply function
10.3 Repeating List Operations
10.4 Combining Datasets
10.5 Joining Datasets


Chapter-11: Making Statistical Graphs

11.1 Learning Objectives
11.2 Using Datasets for creating Graphs
11.3 Making Histograms, Bar graphs, Line graphs, Scatterplots, Boxplots etc with Base Graphics
11.4 Creating Line plots
11.5 Control colour and shapes
11.6 Add themes to graphs


Chapter-12: Special Packages in R

12.1 Plotly package to generate dynamic graph
12.2 Swirl package to learn R
12.3 RPivottable package to generate Pivot table
12.4 plyr package to split and combine data


Chapter-13: Basic Statistics

13.1 Learning Objectives
13.2 Drawing numbers from Probability Distributions
13.3 Summary Statistics-Mean, Variance,SD,Correlation


Chapter-14: Linear Models

14.1 Learning Objectives
14.2 Fit simple Linear models
14.3 Exploring the Data


Financial Analytics using R

Chapter-15: Financial Data Analytics using R

15.1 Define the Goal of Your Analysis
15.2 Collect and Manage the data in R
15.3 Building the Predictive Model
15.4 Evaluate and Critic Model
15.5 Present result and Documentation
15.6 Deploy Model

Financial(Credit Card related) Data Analytics of a Bank

Context Fraud and fraud detection is an important problem that has number of application in diverse domains. However in order to investigate, develop, test and improve the Fraud Detection Techniques one needs the detailed information about the domain.
The main purpose of Banks is the generation of synthetic data that can be used for Fraud detection research. Statistical and a Social Network Analysis (SNA) of relations between merchants and customers were used to develop and calibrate the model.
Content YWe ran BankSim for 180 steps (approx. six months), several times and calibrated the parameters in order to obtain a distribution that get close enough to be reliable for testing. We collected several log files and selected the most accurate. We injected thieves that aim to steal an average of three cards per step and perform about two fraudulent transactions per day. We produced 594643 records in total where 587443 are normal payments and 7200 fraudulent transactions. Since this is a randomized simulation the values are of course not identical to original data.

By analyzing that data we will do folowing predictions
A. Frequency of Age Category
B. Frequency of Fraud Category
C. Frequency of amount of Fraud Age wiseD. Frequency of Amount of Fraud Gender wiseF. Predictive Model to find out the factors affecting the Fraud and Predicting the Fraud

Tania Chakraborty

In-house Faculty (R & SAS)

Tania, with a background in engineering, have 3+ years of hands on working experience on various Analytics tools, mainly SAS & R. She played a major role in the student data analysis of two entire countries, Dominica & St. Kitts, on a popular student management software “openSIS”.
Other than that she has worked on various other data analysis projects like, Data Analysis on US Economic Indices, Twitter Sentimental Analysis, GDP rates etc.
Simultaneously with project work, she provides training on Big Data analytics using Hadoop and R, Base SAS & Advanced SAS. She has already educated over hundred high profile MNC professionals on Data Analytics. She is the most junior but most appreciated faculty of our team.

Debajyoti Chakraborty

In-house Faculty (R & SAS)

Debajyoti, a Statistical Analyst, Member of Actuarial Society of India, Analytics Trainer on Statistical Softwares - SAS,R,Ms Excel with basic query language knowledge on SQL. Graduate in Statistics with Maths and Computer Science as other subjects. Having over 3yrs of work experience as Data Analyst. In Statistical Analyst role he has worked on multiple industry projects including dashboarding and analytics implementation for Retail and Healthcare projects. Also, as an Actuarial Analyst he assisted in Claim Analytics. As an Analytics Trainer, he is providing Analytics training to Industry Professionals and Academic Students on Statistical Software packages - SAS, R, MS Excel (Beginner to Advance) and SPSS, and overseeing Data Analysis projects undertaken by students and knowledge sharing for successful completion of projects on time.

Anindya Kundu

In-house Faculty/Consultant (SAS, Python,R,SPSS)

Anindya Kundu is into qualitative and quantitative analytics consultancy for more than half a decade. He has been involved in both analytics as a service and analytics product development projects. Anindya is a data obsessed person who loves generating insights from large quantities of data - clean, process, harness data to get hidden truth. He uses his SAS/R/SPSS/Python tool implementation capability to analyze data, and also perform automation. He is involved in analytics innovation, specializing in product development for population health management, health economics, insurance and mortgage, healthcare analytics and transportation – supply chain management.  He was also extensively involved in functional development of CRM (Customer retention module) application tool for a Fortune's Best 100 Companies. He has received his post graduate certification from IIM Ranchi.