Rigid FICO Score Leaves an Opening for FinTech Companies
Image courtesy of Forbes

Rigid FICO Score Leaves an Opening for FinTech Companies

Will machine learning lead to a new credit score?

As unemployment numbers surge upwards, so do loan requests. Traditionally, the metric used by the banking institutions is the FICO score, abbreviated from Fair Isaac Corporation that pioneered the score-based credit profile. However, in the face of FinTech ingenuity, FICO seems too rigid and outdated. 

Is It Time to Leave FICO Behind?

Introduced in 1989, FICO quickly established itself as a standard for determining a customer’s risk profile when it comes to issuing credit and mortgage loans. Since then, a three-digit number became decisive in arbitrating people’s financial fates. If the number goes under 660, the likelihood of getting a mortgage loan goes drastically down, to almost certain denial.

Before FinTech, if you were rejected based on your low FICO score, you had another alternative – subprime loans. Unfortunately, they come with a higher interest rate to account for the greater risk involved. Even a seemingly tiny percentage can incur tens of thousands of dollars more expenditure if the loan is set for decades.

Interestingly, FICO’s risk-predicting power is not simply based around the income-to-debt ratio, but it draws from a wide range of sociological observations about the negative impact of certain life events:

  • Marriage with joint accounts
  • Retirement
  • Starting a business
  • Divorce
  • Career change
  • Death in the family 
  • Medical emergency

However, we all understand that such a score would be based on the averages, which means that the FICO score leaves many people out of the loop, despite being perfectly capable of repaying their debts.

FinTech Precision

No matter how many times the FICO score algorithm is updated, it still relies on broad generalizations. As we enter the age of machine learning, a credit score can be determined based on hundreds of factors for that specific borrower, instead of whole groups of people. Furthermore, the type of risk should match the type of credit the borrower is asking for.

More precise scores would only be possible with advanced data modeling. Recently, Estonia-based FinTech firm askRobin raised almost $2 million to bring financial services in the form of reasonable loans to the masses. They employ precisely such credit profiles thanks to machine learning algorithms sleuthing through every piece of data relevant to a customer’s ability to repay a specific line of credit.

It seems that the COVID-19-derived crisis serves as an equalizer when it comes to access to affordable credit. Both developed and undeveloped nations are positioned to benefit from FinTech’s flexibility and precise metrics.

Do you welcome the push to refine our methods for determining credit risk factors? We’d like to know what you think in the comments section below.

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