digital lending india

What is the creditworthiness of a customer?

Creditworthiness, typically measured through a credit score (a number between 300 and 900), is an assessment of how likely you are to pay back the loan. Four agencies in India provide their proprietary credit score (and detailed credit reports)—CIBIL, Experian, Equifax and CRIF HighMark. The higher the score, the better the digital lendersconfidence in you (but your scores may be different with different bureaus).

All the bureaus are mandated by RBI to provide you with at least one free credit report annually through their respective websites. Several intermediary agencies also provide free credit reports by partnering with these bureaus.

How are credit scores calculated for credit risk assessment?

All financial institutions share data with the credit bureaus, who in turn calculate your credit score using proprietary algorithms. At a high level, the score is dependent on several parameters including:

  • Payment history: Have you made payments on time or have you defaulted?
  • Credit enquiries: How many times have you enquired for credit applied for loans?

  • Credit mix: What is the balance between secured and unsecured loans? Do you have a lot of outstanding debt already?

  • Credit utilization: How is your debt increasing over a period of time? Are you taking on more debt? Are you utilizing your available credit limits too much?

A good credit score is the gateway to a smooth financial life, for access to easy and affordable digital loans and other credit opportunities.

Are there other factors beyond score that matter?

Depending on the institution, there can be factors beyond the credit score that act as significant input for loan underwriting. Banks formulate their own internal benchmarks around acceptable scores and utilize additional data for their approvals. For example, they may refer to your income levels, your employment history, your bank statements (to assess your spending and saving patterns), and their in-house policies and models for credit-risk analysis.

Irrespective of these policies, traditional risk-assessment methods penalize customers who do not have a credit history or are “new to credit”. Anyone that the credit bureaus do not have enough data on, tends to pay higher interest rate on their loans.

Many institutions have started leveraging “alternative data” now. But what is it?

If you are a low or no bureau score customer, getting a loan becomes a tedious exercise. However, lately, many institutions have started using an alternate approach to bring better and cheaper credit access to this segment too.

Alternate data usually includes multiple sources of information, like telecom usage and history, mobile transactions, bill payments history, e-commerce, spending patterns and more.  It gives banks access to a far wider range of variables/information assets, compared to standard creditworthiness tests, thus allowing banks and lenders to make better lending decisions. Even for customers with mature credit histories, this helps drive better confidence, optimal interest rates and terms all around.

A combination of alternative data and traditional bureau data is helping in improving the overall credit risk assessment frameworks. It is also helping in assessing (better) the likelihood of credit default associated with the inaccuracy of traditional data.

How are credit scores calculated using alternate data?

These data sources are supported by Machine Learning (ML)-based decision-making systems, which benchmark the data received to generate a more holistic credit risk assessment for a potential consumer. A credit score derived from alternate data incorporates many new factors:

  • Financial ability: The customer’s ability to pay from their bank account(s)—denoted by income, balance and saving trends. Higher the ability, better the result.

  • Past non-banking credit history and payments: These offer significant insights into customer behaviour. For example, a post-paid mobile bill indicates a small risk taken by the telecom company on you, and the repayment behaviour therein can be suggestive of your overall financial habits.

  • Non-banking transactions and assets: Wallet and UPI transactions are now becoming proxy for banking spending patterns/transactions. Alternate data also includes information about a customer’s assets (investments) and risk coverage and helps in understanding how well-organized a customer is financially.

  • Recent negative incidents: Probably the last thing a customer would want is a bounced cheque or penalties around minimum average balance (MAB). Lenders are quite wary of such incidents and may not easily extend credit as a result.


In today’s India, as millions of people look to the future post-COVID, alternate data-based credit can help unlock the potential of a young, educated and aspirational workforce eager to get ahead in life.

About the author

Amit Das is the Founder and CEO of Think Analytics, a data science company at the crossroads of smart data insights, financial inclusion and product innovation. Das is an IIM-Bangalore graduate with about two decades of experience in management consulting, and strategic analytics advisory.

Think Analytics is the parent company that launched Algo360 —  India’s first alternate data credit score in 2016. The product has made over 40 mn lending and credit score decisions for financial institutions.

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