Data company FICO is experiencing changing demand for credit scoring as banks find new ways to use analytics in meeting new regulations. Jane Cooper speaks to chief executive Will Lansing.

The buzz of big data has swept the banking industry, but there are many companies that were doing it long before it was even known by that name.

FICO is one such company, which has been using data to predict behaviour since the 1950s. Its FICO Score determines the fate of many lending decisions in the US: consumers with a high FICO Score can borrow more at a lower rate, while those with a low score will do their best to improve it.

In the past, companies were only prepared to pay for data analytics for big-stake decisions, such as mortgages and car loans, according to Will Lansing, chief executive of FICO. “If the stakes weren’t big enough, they would wing it,” he says. These days, however, the cost of analytics has come down, the precision has gone up and there is an appetite across most industries – such as telecoms and retail – to understand and predict consumer behaviour.

FICO Score

The 'score' is probably what FICO is best known for and it still accounts for a large portion of the company’s business. “The FICO Score is one-quarter of our revenue and three-quarters of our profit,” says Mr Lansing. And even outside the FICO Score business, the same intellectual property sits behind many of FICO’s other solutions. The FICO Score “is very important” to the overall company, Mr Lansing adds. 

Large financial institutions use the FICO Score to assess credit risk, but there are a number of start-up lenders that are using different methods to assess creditworthiness. Some lenders are using algorithms to assess an individual’s social connections on sites such as Facebook, LinkedIn and Twitter, and may even do a risk profile based on the content of an individual’s postings. FICO, however, does not use social media in its FICO Score. “The value of social media [for credit scoring] is probably overrated,” says Mr Lansing.

For lenders that target the unbanked – and people without a credit history – unconventional data sources may be appropriate, but for large banks that are dealing with the mass market, social media data would only cover a small proportion of their customer base.

Assessing risk

Assessing the credit risk of an individual has a long history, but more recently banks have needed to assess the risk of individuals from the perspective of anti-money laundering (AML) and know your customer (KYC) regulations. At a recent FT Live conference in London, this was one of the issues raised: it is easy to assess individuals and give them a credit score, but it is not as easy to do it from a sanctions perspective. So, can FICO’s intellectual property be applied to AML and KYC? Yes, says Mr Lansing.

In January 2015, FICO acquired Tonbeller, a company that specialises in financial crime prevention and compliance. Through the acquisition, FICO is now equipped to address the demand in the banking industry to comply with AML and KYC.

Tonbeller’s offering is a natural extension of FICO’s Falcon fraud prevention solution, says Mr Lansing. It is not about giving a binary yes-no answer, but rather a score based on a number of data points that says how far a transaction is from the norm. A financial institution can then look at the score and compare that with its own risk parameters to decide whether to allow the transaction or not.

Cyber security

“The same idea of scoring for behaviour can apply in the cyber security realm,” says Mr Lansing. While the areas of AML and cyber security solutions are coming together, so too are the roles within banks. Many heads of fraud at banks are now also the heads of financial crime, and so they are increasingly looking for solutions across the board.

Risk assessment is not the only use of predictive analytics and customers’ data can be used in other ways. But its use by financial institutions – and other companies – is seen by some as irritating or invasive. “There’s always going to be people who do it with a sledgehammer. When done well it should not be annoying, should have respect for privacy and should be a spot-on offer,” says Mr Lansing.

He adds that in the US there is a split in the business models of how data is used. Either a service is given for free so that the customer’s data can be used for lead generation or they can charge the customer and the data is not shared. “We will see customers choose different models,” says Mr Lansing.


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