In a rapidly digitizing world, lack of access to credit can hold people back from being empowered and also reinforce inequity. In this situation, we need to focus on and resolve two fundamental questions.

  • How can the unbanked and underbanked get access credit and lending opportunities?

  • How can we bring credit access to “thin-file” customers without significantly increasing non-payment risk?

This can be possible through alternative data, which goes beyond traditional methods and takes a more holistic view of a user’s behaviour and relationship with credit. The creditworthiness of a customer basically means the willingness of the person to repay the loan. Traditional credit-scoring methods followed by credit bureaus focus mainly on the willingness of the borrower to repay. Alternate data takes into account the willingness and the ability of the borrower to repay the loan.

It usually includes multiple sources of information, like telecom usage and history, mobile transactions, bill payments history, e-commerce, spending patterns and more.  These sources fall outside the traditional assessment parameters when considering credit scores.

It gives lenders access to a far wider range of variables/information assets, compared to standard creditworthiness tests, thus allowing them to make better lending decisions.  Alternate data sources are often supported by AI and Machine Learning (ML)-based decision-making systems, which benchmark the data received to generate a more holistic credit score for a potential consumer.

Let’s take a look at why alternate credit scoring is the need of the hour.

Reduces the barriers for newcomers to credit

The current credit system disadvantages newcomers massively. With changing societal dynamics, a lot of people are relying on credit to build their lives and businesses. Financial institutions will find it harder to sustain in the long run if they continue to rely on methods that penalize newcomers. Alternative data sources help NBFCs and banks in credit risk assessment with greater accuracy, even for individuals who do not have an established credit history.

It also ensures that people who need credit the most get easy access, e.g., “thin-file” customers like university students and those who have just joined the formal workforce. Alternate data makes it possible for historically underbanked sections and economically less well-off groups to avail credit and digital loans to improve their living conditions. And, in a world where international travel is increasingly common, alternate data can open access to credit to people who have just moved across borders.

Provides financial inclusion

The current credit scoring systems make access to credit difficult for varied segments in society including women, traders and the underprivileged.

Access to credit can be the lifeline especially  for those segments attempting to advance in life, both professionally and personally, to feel more empowered and financially independent. Any kind of loan—education, homes, business or personal—plays a significant role in the lives of those who are trying to get access to borrowing opportunities.

Alternate data can help ensure that the processes are more inclusive, especially for those who would otherwise find it difficult to maintain a good credit score and access credit. It can also help dispel the common myth that low credit scores with the current scoring method always means high risk. A combination of alternate and traditional bureau data will help improve the new generated credit score of the potential borrower. This will also ease things for the digital lenders judiciously lend their money.

By harnessing alternate data, the opportunities for banking institutions are seamless, not just for their business but also for greater economic empowerment.

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