Alternative approaches to money lending for the unbanked (Fintel scoring)

August 30th, 2019 by · Leave a Comment

This Industry Viewpoint was authored by Martin Brezina - business development @ telco big data monetization solution Instarea

There is a huge and largely untapped potential in the banking sector: the unbanked. Because many people need a loan sometime. But not everyone can be granted one by a reputable lender.

Very often when a potential borrower enters a bank branch with an intention to request a loan they are turned away. Sometimes this is because of the bad credit and missed payments, but often simply because a bank cannot reliably calculate the credit risk score. Could be, for instance, that the customer never had an account, or if they did, they rarely used it. This is a real problem, you see, almost one third of the adult population (think around 1,7 billion), does not have access to banking or mobile money accounts. Most of them are, not surprisingly, in the developing world.

When a customer does not have a bank account of any type, banks have no way to evaluate the credit risk score, on the basis of which they can offer a loan. In such cases, banks either refuse the applicant or give them an exceedingly high interest rate to balance for the risk. That even in the case when the lender has no history of missed payment or holds a steady and reasonably paid job (albeit paid in cash). But risk is risk, understand; and there is little to do about it.

Or is there.

What if I were to tell you there is a way. The solution is simple: do not rely on archaic credit scoring systems. There are plenty of alternative data sources, which can evaluate customer’s credit worthiness as well, if not better, as traditional methods. This could tap into mostly invisible unbanked citizens, non-banking businesses or many potential young entrepreneurs (and have thus significant impact on economic performance of the aforementioned countries, where access to capital impedes small business growth).

One way to go about it is to use data from telecommunication operators (telcos). Mobile data can provide a lot of insight through, what is referred to as FinTel scoring. Big data from telcos can make credit scoring models more predictive and accurate than regular bank’s credit history data ever could. The case can be made for both: the unbanked citizens, as well as, long-standing bank customers.

The trick here is in volume, depth, and spread of telcos’ big data. Information like, top-up history, call and sms usage, typical locations of the phone or even phone ownership regularly informs the model. When analyzed correctly this data is indicative of financial skills, income, consumption profile or even life habits.

This is only the beginning. As per McKinsey, the consulting company, banks and loan providers can move beyond simple lending. Big data provided by Telcos could help customers make better financial decisions (through use of targeted alerts). But they could also refine their marketing and communications in a way that would intrigue distinct segments or allow providers to offer the right noncredit products, such as savings or insurance.

The benefits of FinTel scoring

In the scenario discussed here, lenders would be able to profitably serve vast unbanked (or underbanked) populations and can help (developing) societies to achieve an elusive, but important, full financial inclusion.

Another motivation is, competition. It, famously, does not wait. Some nontraditional lenders are already using the data they have spun off from their core business. Banks, however, have a considerable advantage in that they know the lending business’ in-and-out. Better, indeed, than utilities or direct-sales companies.

And there is a huge marketing potential for lenders, as well. By partnering with telcos, banks could, on the basis of customers’ risk profiles, send credit solicitations to those that might qualify for loans. This means a lot more traction and a notable potential to improve the profits.

How is it done, in practice?

A customer walks into a branch of an unnamed bank, asking for a loan. Upon entering customer’s information into the bank’s system, nothing comes up. But the teller knows what to do. He asks the customer for his details and to sign a consent for external FinTel risk scoring. Then the teller puts everything into the system which sends the details via telcos’ API (Application Programming Interface) to customer’s telecommunication provider. Few moments passed, and the teller knows the score and is able to offer the customer a loan with favorable conditions and appropriate interest rates.

FinTel scoring API monetization

APIs are a new potential stream of revenue for telcos and a new and useful way to externally monetize their data. APIs, put simply, let telcos open up access to their data while maintain security and control.

APIs let telcos’ backend communicate with banks’ frontend without having to know the exact values that go into the model. Everything is calculated in a backend and so bank teller only sees the final risk score or other agreed upon attributes.

What can go wrong?

It is true using FinTel scoring is not without its problems. Contrary to traditional credit scoring, where banks usually rely on a tiny stream of data collected monthly, telco data are a bit more voluptuous. This creates obvious challenges, but, as with many other things, one’s greatest weakness is also one’s greatest strength. And this amount of data allows for a more refined and reliable predictions. Nevertheless, it is essential to model the risk score correctly with proper rigor, and that is why telcos and their partners have to put in some work.

Another potential challenge is privacy and consents. For a FinTel risk scoring an individual’s consent has to be collected. This can be done from both sides. Either telcos gather consents approving use of customer’s data for credit risk scoring, or a bank can easily do that when a customer comes to request a loan, which reduces the strain on the telco.

Conclusion

There is a huge potential for FinTel risk scoring. It is a win-win situation and if a model is and API requests are developed properly it can bring value for telcos and banks alike.

Imagine, upon receiving an API call, telco’s backend can churn out the result momentarily. Just what a bank needs. On the other hand, FinTel API scoring can create new value to telcos and a new monetization potential. Just what a telco needs.

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Categories: Big Data · Financials · Industry Viewpoint

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