Fullerton India unleashes the power of Analytics to drive customer engagement & increase efficiency

Fullerton India now has effective cross-sell, upsell
& credit scorecards and a better penetration of their products by using SAS Analytics.

Fullerton India is one of the leading NBFC’s in the market with a portfolio ranging from personal loans to salaried and self-employed individuals, working capital loans, loans for commercial vehicles and two-wheelers, home improvement loans, loans against property in the urban markets to loans for livelihood advancement, housing finance and financing of various micro enterprises in the rural geographies.

Started in 2007, Fullerton India Credit Company Ltd. reaches its market by connecting with millions of customers, be it in cities, towns or villages, right at their doorstep. The Company has successfully and strongly established itself in the country’s broad financial landscape with a network of over 526 branches, serving over 1.5 million customers and with strength of over 10,000 employees (as on 30th June 2017).

Having a base of ~1.4mio accounts corresponding to 1.3mio rural customers and 1.8 lakh accounts corresponding to 1.7 lakh urban customers, Fullerton India needed structure and high -level of accuracy to their data analysis to select the right target customers and make data driven strategic decisions. With SAS analytics and modelling in place, Fullerton India is now able to process huge data with greater speed and reliability for insights that support faster, better business decisions and detects and prevents defaults.

In an era of increasing importance of data and analytics, especially in the financial services industry, SAS provides an optimal, robust and technically advanced solution which enabled Fullerton India to effectively allocate effort and generate tangible value for business through analytics.
Bikramjit Ganguly – Fullerton India Credit Company Limited

Bikramjit Ganguly
Chief Information & Digital Officer

Better Credit Risk Assessment and risk based pricing:
Building credit scorecards to either accept or reject credit applications based on customers’ scores needs an analytical approach. Providing credit to a “high risk” customer runs the risk of late payments, default payments etc. which involves significant effort and cost. Fullerton India built application scorecards using SAS solutions to take key decisions related to loan disbursement including risk based pricing of individual loans. The right use of analytics also resulted in the increase of customer satisfaction through reduced TAT (average 8 days from >15 days). The credit granting process became streamlined with processing happening quicker than ever. Result - a reduction in loan processing costs to the lender contributes to a lower cost of credit to the benefits of consumers.

Lower Churn of customers:
Gating models have been developed using SAS to identify the residual profitability of the customers who are at risk of leaving Fullerton India. Since acquiring new customers is more expensive than managing the existing ones, these models helped in reducing costs to run the business. The benefits have been immense – enabled retention of ~40% customers with average ticket size of ~INR 3 lakh.

Ease of use and scalability:
The propensity models built by Fullerton India using SAS analytics support larger datasets, as these models are “enterprise” grade. It has resulted in 6x increase of profit by INR ~550 lakh . The analytical models are highly scalable and provide greater ease of use. The complexities lie in optimized pre-compiled procedures ("procs"), a user needs to enter the input/output parameters to build specific logic on data analysis.

Processes have been automated and systems have come into place. This allows departments to dedicate time for analysis, which would otherwise go in comprehending large spreadsheets. The report development time has largely reduced from 5 hours to 15 minutes with the help of information churn model build using SAS Enterprise Guide.

Improved data structure and processing:
Fullerton India built a holistic view of their customers’ data comprising of demographics, behaviour and bureau data. The information related to a customer’s credit scores, credibility, transactional models, demographics, etc. was comprehensively available in one place making it easy to be referred to at the time of customer contact, in turn facilitating more accurate decisions. Focusing on the right type of customer, helps Fullerton India skim the cream and optimize their investments.

Fullerton India

Challenge

  • Appropriate credit risk assessment for providing loans
  • Fraud detection & prevention
  • Identify right customers for targeting any cross sell/upsell opportunity
  • Reduce time to build comprehensive reports
  • Minimize customer churn rate
  • Building and managing complete life cycle of customers

Solution

SAS® Enterprise Miner

SAS® Enterprise Guide

SAS® Base SAS

Benefits

  • Ease of use and scalability
  • Lower Churn of customers
  • Better Credit Risk Assessment
  • Improved data structure and processing
The results illustrated in this article are specific to the particular situations, business models, data input, and computing environments described herein. Each SAS customer’s experience is unique based on business and technical variables and all statements must be considered non-typical. Actual savings, results, and performance characteristics will vary depending on individual customer configurations and conditions. SAS does not guarantee or represent that every customer will achieve similar results. The only warranties for SAS products and services are those that are set forth in the express warranty statements in the written agreement for such products and services. Nothing herein should be construed as constituting an additional warranty. Customers have shared their successes with SAS as part of an agreed-upon contractual exchange or project success summarization following a successful implementation of SAS software. Brand and product names are trademarks of their respective companies.

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