Retail loans – the only remaining bright spot on the books of Indian lenders so far – have begun their downward slide. Experts say that it was only a matter of time before the developments on the unemployment front and successive collapse of large banks took its toll on the consumer loan segment. A liquidity crunch on the corporate side invariably affects retail loans, given the impact on incomes cutting across socio-economic classes. In its latest report, India Ratings and Research have warned about an increase in gross NPAs in several retail loan segments from credit cards to personal loans. For lenders, this means that 35.3% of their total credit exposure is now under threat.
Minimizing risk and staving off NPAs is a key priority for banks and NBFCs. While they have relied on credit bureau data to approve credit applications in the past, there is now a focus on using external benchmarks to determine credit worthiness and effective interest rate payable based on borrowers risk profiles. This means that lenders will now be able to charge a premium on loans for customers.
In September 2019, the RBI ordered NBFCs and banks to link floating rate retail and business loans to an external benchmark of their choice, scrapping the Marginal Cost of Fund Lending Rate (MCLR) based system in use earlier. Credit bureau data will form the basis of interest rate revisions for borrowers and will include a spread component (margin) charged by the bank. Among other planned measures is the formation of a Public Credit Registry (PCR) which will be a common database drawing credit scores from all credit information companies in the country.
How can lenders mitigate credit risk with credit bureau data?
Credit bureaus are valuable sources of trustworthy information. Here’s how this powerful tool can become useful for lenders.
Plugging gaps in credit data
NBFCs and banks can get a complete 360-degree view of a prospective borrower’s existing profile. This makes it possible to run an objective risk-return assessment. Credit bureaus are also tapping alternative sources for credit scoring data such as online bill payments, employment history, social media, etc. Combined with lending lifecycle automation tools like Finezza, lenders can improve the accuracy of their credit decisions substantially.
Standardisation of credit scoring
Across different segments, lenders use different data points when evaluating the credit worthiness of a borrower. This limits their ability to see the bigger picture in terms of the overall credit behaviour. It is also possible that internal or third-party data sources used are not updated frequently. With credit bureau data, lenders can optimise their underwriting processes by leveraging industry-standard risk management frameworks, which are on par with global best practices.
Verifying self-attested data
In the case of MSMEs or self-employed individuals, lenders often have no way to verify proof of income and assets owned by the applicant. This means that they have to take the borrowers declarations at face value, increasing their exposure to risk. In the absence of established credit history, lenders can leverage alternate credit scoring – which is now being offered by many new generation credit reporting agencies – to improve decision quality and target underserved segments.
Reliability of data
Combining credit bureau data with traditional demographics – income, age, etc – can remove inaccuracies and help lenders get vital insights into the creditworthiness of borrowers that would have otherwise been impossible. Credit bureau information can be used by lenders to validate and justify credit decisions from all possible angles to hedge risk and reduce the chances of default.
The cost of purchasing customer data from third-party sources may be low initially. However, poor decisions made on the basis of such data can have huge cost implications for lenders. Data from different sources also need to be cleaned to remove any formatting errors. This allows it to be interpreted correctly. The time it takes to manually clean data from various sources and make it fit for use can be quite long. This adds to the time lag between loan origination and decision-making.
With credit bureau data, lenders can access standardized data which improves the speed with which lending decisions can be made. It is also far more accurate and reliable, compared to non-standard sources. This reduces overall operating costs for lenders.
Early warning advantages for lenders
It is important for lenders to identify these following warning signs:
Identify high-risk customers
Credit bureau data can reveal valuable insights into customer spending behaviour – such as overlimit credit cards and increased loan inquires – to identify potential borrowers who may at risk of default. In the case of corporate loans, credit bureau data can help borrowers restructure loans with borrowers, rather than risk liquidation of assets under bankruptcy proceedings. For lenders, timely intervention can avoid a total write-off.
Understand the ‘point of no return’
Data included corporate financial reports can potentially be masked to hide the true financial standing of a borrower. There is usually a small window of opportunity during which it may be possible for lenders to recover a non-performing loan. Credit bureau information can provide vital clues regarding delinquency, misappropriation or fraud, helping risk managers at NBFCs take timely remedial action.
Understanding the psyche of borrowers
Credit behaviour is a subset of the borrower’s state of mind. In a financial crisis, a borrower is likely to experience stress, fear, and depression which influence his ability to repay his debt. Lenders in some Western countries are experimenting with psychometric credit scoring and correlating it with credit bureau data to derive insights into the willingness of borrowers to pay back a loan.
A responsible attitude towards debt is a critical factor in loan repayments. Credit bureau data can also alert lenders about possible attempts by borrowers to evade attempts by creditors to collect on past due payments.
To build a comprehensive picture of a borrower’s credit worthiness and control asset quality, lenders need to evolve a data-centric approach. One of the key pillars of this strategy is leveraging credit bureau data-based risk modeling and credit decision-making to improve returns amid an uncertain market environment.
There are some platforms that can help lenders in these regards. For example, Finezza is a software and platform that comes with secure APIs and data pipelines that can be easily integrated into different banking systems. One unique feature that makes Finezza stand out among the rest is that it has every financial institute’s security needs factored in with its data-access controls.