Digital footprints: changing faceof lending landscape

In this digital age, business processes are witnessing makeovers every other day. The efficacy of the age old brick and mortar concepts are being challenged by fast moving technologies which are lighter on time and cost. Globally, the banking space including the shadow banking segment, has also been ushering in effective FinTech driven ecosystems that have enhanced delivery quality in terms of customer delight and business volumes.

Analysis of creditworthiness of a prospective borrower is one, if not the most important, activity in the entire life cycle of loan underwriting. On an individual level, a lending entity like a bank or a Non-Banking Financial Company (NBFC) typically relies on traditional data like repayment history of the borrower, periodic income source and size - proof of such incomes being realised in the bank account, self declaration of other assets and liabilities, numbers from the borrower etc.

The Credit Information Companies (CICs) or credit bureaus are the repositories of credit information which they collect from banks, credit card providers, NBFC, HFCs and other financial institutions. Each loan details and the service history are captured in a virtual dossier identified by the Permanent Account Number (PAN) of the borrower. In India, such CICs are currently four in number - TransUnion Credit Information Bureau Limited, Equifax Credit Information Services, Experian Credit Information Company and CRIF High Mark Credit Information Service. While the traditional data is largely dependent upon the credit manager, an upcoming trend is of using ‘Alternative Data’ which is basically the digital footprint left by the customer and mined by the FinTech companies.

What is the spread of digital data?

Over the years, the consumers have been getting used to internet based online shopping wherein the commodities and services alike can be bought from sellers and service providers. Digital footprints go beyond social media and can be a transactional SMS on the customer’s mobile number when he hails a cab, orders a pizza, buys an insurance policy or books a movie ticket. It can also be the total amount spent on talk time and internet. Analytics algorithm can interpret these scattered data and throw light on the lifestyle preferences of the borrower-to-be in terms of assignable numbers.

LoanTap Credit Products Pvt. Ltd., an RBI registered NBFC which operates a FinTech platform for various loan products, does not consider traditional data to be sacrosanct andrests its credit evaluation practices predominately on alternative data. Satyam Kumar, Co-Founder, LoanTap, informed BE, “We have developed our proprietary algorithm that sources and scrapes relevant information online and evaluatesmultiple data points to provide accurate creditworthiness of a candidate. We also conduct AI-based retrospective tracking to further enhance our evaluation.”

A bank statement provided by the customer also tells a long story about the money management skill and behaviour of the customer. Traditionally, a credit manager checks the timely credit of salary and other incomes (interest, rent etc) if any, which the customer has declared as his cash inflows to substantiate his repayment capabilities. But companies like LoanTap uses the same bank statement and puts pre-defined scores against parameters like monthly average inflow versus outflow in the account, average balance maintained and perhaps the dates with highest average balance. A high salary earner may be a habitual splurge in some wasteful expenses like online gambling. Analytics helps to identify this underlying dispersion and factor in the same in the evaluation matrices. In addition to this, duration in a job and regular residential accommodation are also considered as important parameters. Too many job-hops or continuous staying in a bachelor’s pad or as a paying guest earns bad scores in spite of a high pay.  

In addition to online lending companies, FinTech companies like CreditVidya have ventured into designing applications for scraping alternative data and build up a credit model for a lender. Abhishek Agarwal, CEO and Co-Founder, CreditVidya, said, “Our mission lies in reimagining the credit ecosystem for greater financial inclusion.” His company claims that a vast amount of unstructured data available through digital footprints is being effectively harnessed to help lenders make granular risk assessments of loan applicants. It has enabled a 15% higher approval rate for bureau-scored applicants, compared with traditional methods of underwriting. The scoring platform ensures 33% lower delinquencies for the same level of risk and also helps customers who are without a credit history.

Traditional credit bureau procedure puts a -1 or no history (NH) score to a person who has never availed credit. As a result, the challenge here is accurate assessment of his ability and intent to repay an unsecured loan. FinTech models offer a solution for this as well. Data from mobile phone records, prepaid top-ups, mobile bill payments and mobile browsing or app download history can be used to assess consumer risk and determine the creditworthiness of such customers. Lenders can use the output of their credit scoring to offer unsecured, small ticket, short-term credit at a much lower cost. Vodacom, a mobile service provider in Tanzania, has partnered with First Access, a for-profit social business focussed on data analytics using prepaid mobile data to predict credit risk for consumers who have never had a bank account or a credit score. First Access offers an instant risk scoring tool for low-income customers by leveraging demographic, geographic, financial and social network data from a subscriber’s mobile records. The digital footprint based credit scoring model has also helped in changing the product features and by introducing new products which are cut-to-size, according to the borrower’s requirements. mPokket, a digital platform gives loan to students and the ticket size is as low as Rs. 500.

On the institutional front also, innovation has been the order of the day. Merchant Cash Advance, a product introduced by Capital Float needs mention. Businesses today are actively using card payment devices to offer a convenient shopping experience to their customers. Point-of-sale machines not only offer the merits of cashless transactions; they can become instruments for availing working capital finance. Capital Float offers working capital loans based on the credit/debitcard sale an get the repayment sourced directly from the multiple point-of-sale (POS) card machine vendors such as Pine Labs, Mswipe, ICICI Merchant Services, MRL Posnet, Bijlipay and thereby help merchants access this customised working capital solution.

The profile of the Indian borrower is ever changing. There has been a quantum jump and with the advent of FinTech, loan products have also have come out from the clutches of one-size-fit-all notion. Part of the spike in borrowing can be attributed to the new generation of borrowers who are tech-happy and mostly have an urban base. Millennial, irrespective of gender, have a wish list which they want to tick off and borrowing helps them to pre pone those dreams. All these have actually extended the product basket and created an alternative lending landscape in the country. NBFCs need to realise the immense value of alternative data and make investments in technology and analytics to develop advanced credit scoring models that leverage both traditional and non-traditional data sources.

—  The author is the Chief Manager (Legal & Risk Management) Peerless Financial Services Ltd. The views expressed are of the author.

Saptarshi Roy Bardhan