Borrowing has always been fuelling the growth of developing economies as corporates and even retail borrowers’ take out loans to fund expansion or other such big purchases.

In India, lending by banks and NBFCs have multiple levels of checks and balances before sanctioning the loan. A rise in NPAs (Non-Performing Assets), is making it harder for borrowers to get a loan.

There are multiple stages discussed below which the borrower has to go through during the loan application stage, right from applying for a loan to submitting his income proofs. One of the most critical stages in the lending process is credit underwriting, which involves assessing the borrower’s creditworthiness.

In this stage, the lender checks the borrower’s credit score, current debt levels, any financial frauds they have committed in the past, income, credit length, etc.

Problems with the lending system in India

After an in-depth analysis, the lender judges the borrower’s ability to repay the loan amount they have applied. Underwriting is a cumbersome process that takes a few days, even sometimes weeks or months.

The borrower’s credit score is given priority by the lenders as it tells a lot about your money management habits in the world of traditional finance. The maximum credit score in India is 900, and one has to have a credit score of at least 700 to be eligible to get the loan.

Also Read: GuavaPass co-founders new alternative lending startup Jenfi lands US$6.3M led by Monk’s Hill

If you have a credit score below 500, then there are high chances that you will face difficulty in getting a loan. This is why many borrowers are denied credit by established banks and other financial institutions in India.

Due to this a crucial loan concept called the ‘intent to pay’ and the ‘ability to pay’, are disregarded as abstract since there is no better way to measure them.

Tapping into ‘Ability to Pay’ information

Income data is not available in credit score reports given to lenders by credit bureaus like Experian, TransUnion CIBIL, Equifax, CRIF Highmark, etc. The credit score only gives you the historical repayment habits of the borrowers, thus only reflecting their willingness or intent to pay.

But, what about his ability to pay?

Income data can be extracted from APIs based on payroll data to determine the borrower’s ability to pay. Assessing the ability to pay is equally important as assessing the intent to pay. This is because, let’s say, both you and India’s richest man have a credit score of 850, indicating a very high intent to pay.

However, both your income levels are completely different and thus your ability to pay will also be different. We need income proof documents to assess this ability to pay that can be taken from payroll APIs.

Fintech to the resuce

Fintech companies have changed this dynamic completely with innovation in risk assessment in the lending process. To provide the best services to their customers, fintech companies are automating the entire underwriting process with data fed into machine learning algorithms that are super quick.

Thus, there is better transparency and no human intervention, as decision-making is data-driven and free from bias. This new technology-driven lending landscape is here to stay and rule going forward. Traditional banks are now collaborating with fintech players to harness this limitless potential of data and technology to use that data to make informed decisions.

Also Read: Matching-making for loans: Why online lending platform Lendela has set its eyes on Asia

Fintech companies are now unleashing the potential of payroll data with open banking via APIs. The lenders are now getting this data and other employment information to make informed decisions on the loan application to curb NPA frauds.

This confidential data sharing is pre-authorised by the borrower thus retaining privacy. It also helps the lenders design a repayment schedule based on the borrower’s payroll.

When the lender asks for banking data from the borrower, there are chances that the data will not be recent. On the contrary, the payroll API-based data is precise and updated to the latest minute. This accuracy and reliability of data help in credit underwriting decisions.

The lead conversion ratio is much higher in cases where payroll data is involved compared to borrowers’ banking data. Many studies conducted show that borrowers are more comfortable sharing their payroll-related information instead of their banking-related data.

Innovation in the payroll space is booming, proving it as the next frontier in the fintech landscape. Payroll attached APIs are fast, secure, reliable, efficient, and help FinTech innovators explore other use cases that deliver value to customers.

Tartan is pioneering user consent-driven payroll data exchange in India, which plays different roles in people’s daily lives behind the scenes, making their financial lives easier.

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