4 min read.Updated: 05 Feb 2017, 11:46 PM ISTRajiv Raj
Nearly 335 million credit-worthy Indians do not qualify for a traditional credit score
The access to formal credit has become one of the central topics within the larger discussion on financial reforms in India. Despite policy efforts towards driving inclusion into the formal financial system, India is home to hundreds of millions of unbanked adults.
Within the system, the majority remain ‘new-to-credit’, while another sizeable portion consists of ‘thin file’ profiles, implying that they have a very nascent or sporadic history of formal financial transactions.
Traditional credit score agencies such as TransUnion Cibil (Credit Information Bureau of India Ltd) typically rely on repayment history on loans and credit cards to determine the creditworthiness of an individual. As a result, nearly 335 million credit-worthy Indians do not qualify for a traditional credit score.
This issue is exacerbated when we look at small-to-midsize businesses, which work on shorter credit cycles and are far more sensitive to the cost of capital.
Indians have grappled with accessing credit at reasonable rates for decades. Despite attempts at financial inclusion, millions of individuals remain dependent on expensive, informal sources of credit at exorbitant rates of interest, even though they have the ability and willingness to repay loans.
However, lenders operating in India are facing a challenge underwriting the young aspiring population that lacks credit history, and cannot fulfil traditional loan application requirements.
More than 50% of new-to-credit applications are rejected each year.
The World Bank has for a long time championed the cause of financial inclusion. It recently endorsed the use of non-financial payment data, such as utility and mobile bills, e-commerce transactions, internet browsing patterns and social media behaviour, in the credit origination processes earlier this year, as a powerful tool for driving financial inclusion in emerging markets.
Also known as ‘alternative data’, this information is becoming increasingly relevant to credit underwriters.
This data, when subjected to analysis, can provide reliable insights into consumer behaviour and willingness and ability to repay loans, making it an extremely important tool in credit underwriting and a means of expanding the customer base for lenders.
Alternative data can help lenders assess risk that is credit bureau independent and increase acquisition responsibly while reducing the cost of underwriting.
However, the use of alternative data is not without its challenges. The primary challenge lies in convincing banks and other lending agencies that alternative data can be used.
Banks and other lending agencies are unable to validate the power of alternative data unless they are experimental enough to collect such data for their own customers, which can indeed have a significant impact on profitability through a variety of platforms that address fraud, and provide far more granular credit decisioning insights.
Another issue that needs to be examined is the consistency and quality of data available. In an emerging market such as India, digital footprints are restricted to a fairly small part of the overall population with borrowers from lower socioeconomic backgrounds and rural areas being left out of the fray.
As opposed to more developed economies, India has a lower level of internet penetration at 35% only, with a majority of subscribers using the internet for entertainment. In this scenario, the richness of data available poses a challenge.
For example, if an individual does not transact digitally or does not interact on social media platforms, the lack of data can make it challenging to accurately analyse their creditworthiness. This is similar to credit bureaus not finding enough data to score customers on loans or credit card transactions.
Even as a relatively new concept in the credit ecosystem, alternative data has proven to be an efficient tool in developing accurate credit profiles, particularly for the urban millennials. According to one report by McKinsey & Co., new alternative data models have cut credit losses in experimental forays into lower-income segments by 20-50% and doubled their loan application approval rates.
The quantum of data available on potential clients aids banks, and other lenders too, to be cognizant when approving loans—thereby diminishing their chances of bad investments.
Across the emerging world in countries such as Brazil, Chile, Ghana and Mexico, innovative financial institutions are realizing the potential of alternative data, for example, prepaid mobile data, such as—frequency of top-ups, usage patterns, call duration and location of callers —to assess consumer preferences and these have translated into disbursement of short-term consumer loans and credit cards.
While lenders in India have largely come to realize the potential of alternative data, they are yet to realize the depth of its implications.
We have come to recognize that the next wave of consumption will be enabled through credit, and so it becomes important to reassess our methods of credit risk assessment.
The use of alternative data can also vastly improve the entire credit ecosystem—from origination to disbursal—thanks to innovative data and opinion mining, imagery analytics, machine learning and artificial intelligence techniques.
The retail sector, in particular, stands to gain immensely.
As India moves towards becoming a ‘data-rich’ environment, the day is not far when alternative data analytics and machine learning will make ‘one-click’ loan approvals a reality.
Rajiv Raj is co-founder and director, CreditVidya.
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