If you’ve ever applied for a loan from a bank, an NBFC, or a fintech, you may remember the meticulous paperwork involved and successful completion of all formalities. The lender lets you know, eventually, if you are eligible for a loan or not!
So, how exactly do banks and other financial institutions really assess it? These decisions are usually based on two assessments—your ability to pay and your willingness to pay (as measured through your creditworthiness).
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 lender’s confidence 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?
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 inquiries: 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?
Are there other factors, beyond the score, that matter?
Depending on the institution, there can be factors beyond the credit score that act as a significant input to their decision criterion for giving a loan. 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 a higher interest rate on their loans.
Many institutions have started leveraging “alternate 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.
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, such as financial ability, past non-banking credit history and payments, recent negative incidents, non-banking transactions, and assets, etc.
by, Amit Das is the Founder and CEO of Think Analytics