Determining Creditworthiness: Evaluating Standards

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Automation is affecting not just the automotive industry’s manufacturing sector but also its financing wing. While manufacturers are busy innovating prototypes that would contest today’s pioneer models of driverless vehicles, the lending business is also on the process of maximizing data to improve its lending dynamics.

One of such move is spearheaded by Ford Motor Credit, the financial arm of Ford Motor Company which specializes in determining consumers’ creditworthiness.

Just last week, Ford Motor Credit was reported to go beyond the traditional means of evaluating creditworthiness, prompting other players in the industry to make the same leap.

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This nontraditional means refers to the use of alternative data that is unconventionally used in determining creditworthiness but may still provide insight into a borrower’s capacity to pay.

Using alternative data is especially helpful for borrowers with thin credit histories or no history at all.

For example, a borrower applying for an auto loan but carry no credit cards or personal loans may encounter difficulties in proving to his or her lender that he or she can pay back the loan. In such case, the lender may use the applicant’s utility bill or rent payments to determine his or her creditworthiness, a strategy not standard in the traditional model.

A confirmation long overdue

Automotive News reported of alternative data’s availability and potential utility for years now. Ford Motor Credit’s adoption of the method only confirms this. In fact, they resolved that the new way is even better than what the captive finance company has previously used.

This was determined after ZestFinance, a credit-decisioning technology platform, along with Ford Credit, each used samples from Ford Credit’s previous accounts to know which method provides a better evaluation of borrower risk.

ZestFinance made use of machine learning to create risk models and place borrowers in a spectrum, labeling them as superprime, prime, nonprime or subprime. Meanwhile, Ford Credit used its traditional methods of risk assessment. Each methodology’s results were then compared to borrowers’ actual payment history.

The study found out that ZestFinance’s results came closer to actual payment performances compared to that of Ford Credit’s.

Sweeping change

After the fact, Ford Motor Credit said that this does not necessarily mean car buyers will have easier credit terms. Given that those borrowers who initially seemed risky are now more acceptable, chances are others who initially seemed more acceptable can now also seem more risky.

This shift from traditional to alternative data by one of the biggest credit crunchers is seen to signal a sweeping change across the whole auto lending industry. But isn’t it about time?

The whole lending industry is slowly integrating tech to process and dispense its products.

The large trove of data conventionally untouched by the traditional way of evaluating risk is the very pool used by advanced analytics software to produce better, more reliable conclusions – there isn’t just no other way forward.

And it’s not just in auto financing, you can see parallel steps being taken in the mortgage industry and other credit financing networks.

Ford Credit’s change is just a single step of a massive, dynamic evolution that spans industries beyond the auto lending business.

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