Nearly 80 per cent of India’s population does not have a credit record, and as a result they don’t have access to a full range of financial services. There is a pressing need for financial institutions to have better and more diverse information to assess credit worthiness and to be able to grant first time credit to this underserved and unserved population.
At the same time individuals generate vast amounts of digital data as they go about their day to day lives online. If well informed, they can be empowered to provide permission to use this data to access credit and other life improving financial services.
Using multiple digital data sources with advanced machine learning algorithms provides a unique opportunity for the under-served yet creditworthy population to have access to credit for the first time.
Why choose a non-traditional data based credit scoring system?
Traditional data currently used by financial institutions have very limited information about the identity, character and capacity to pay of most of the Indian population. The lack of any previous credit record is a major bottleneck in extending credit to these customers.
However, many of this unserved market, such as the emerging middle class, are now digitally connected and active users of smartphones, the internet and social networking platforms.
Non-traditional data based credit scores aim to fill this gap and benefit both the borrowers and financial institutions in India by measuring the borrower’s character and willingness to pay back using data emanating from natural digital behaviors.
These new age credit scores can be highly reliable, allowing financial institutions to approve up to 30 per cent more applicants and provide results real-time for fast and cost efficient processing.
How does the non-traditional data based credit score work?
Alternative data based credit scores predominantly uses digital data to analyse people’s character and capacity to pay.
It is an objective numerical value which measures the probability of default for an applicant. It is easy to interpret, easily integrated with existing application scorecards and has the potential to become a market standard in the years to come.
From this financial institution are able to design risk strategies and innovative financial products that are profitable and also provide financial access to millions of people.
How non-traditional data based credit scoring aims to bridge the gap?
For emerging markets, the process of determining credit is different from that in developed markets where the vast majority of citizens have records with credit bureaus and information in databases accessible by financial institutions. In India the scarcity of reliable data on an individual’s past credit behavior makes a large part of middle class citizens in developing countries either “no hit” or “thin file” (an industry term to denote no record or little information in a credit bureau).
The use of a non-traditional data based credit scores can finally address this societal challenge. They can be instantly deployed to various origination platforms for pre-screening of applicants or used in conjunction with traditional data in the core credit decisioning process.
How will it benefit the lenders?
With the growth of India’s emerging middle class and their growing usage of smartphones and the internet, many different players are opting to use this data to not only augment current credit decision data but more and more to be the primary source of information used to independently drive credit decisions.
●Traditional lenders such as banks and NBFC’s intending going into unserved and underserved markets
●New age Fintechs looking to open up new avenues of lending to individuals and SME’s
●E-commerce firms looking to verify and create financing options for merchants and online customers
The use of non-traditional data is expected to drive the next phase of growth for India’s Financial Services Sector, by bringing in millions of creditworthy borrowers in the view of the mainstream financial service providers, and by supporting digitisation of credit processes and verifications to enable faster, cheaper and informed decision making.
The author is Country Director, India, Lenddo