Banking
NEW PLATFORM USES BIG DATA TO HELP BANKS LEND INTELLIGENTLY
A new cloud-based credit scoring service has launched in London to help consumer lenders improve loan quality and acceptance rates through the use of big data.
Founded three years ago, Big Data Scoring is a tool for use by banks and financial institutions to determine the creditworthiness of individuals based on data available online. It does this by tapping into the broadest sources of information from across the internet, analysing all publicly available information and online behaviour. This intelligent research aims to bring lending institutions into the digital information age, allowing lenders to make informed and more responsible credit decisions.
Credit scoring millennials and non-UK nationals has always been challenging for lenders: their lack of credit history means that lenders find it difficult to assess how they manage their finances. This means lenders often get it wrong, either offering too little or too much credit. The former means missed opportunities for institutions, while the latter can expose applicants to bad debt. Young people often find themselves stuck in this vicious cycle, with more than one million having been refused credit.
This lack of knowledge is reciprocated by the consumers themselves. A recent YouGov study, commissioned by Big Data Scoring, found that a third of people under 30 (33%) don’t know what a credit score is while almost half (47%) understood the concept of a credit score, but didn’t know their own. The poll revealed that this generation are more likely to have a cash ISA (34%) than a credit card (29%); a third have no debt at all (33%) and a quarter (25%) had been declined a financial product or loan. The problem of too much or too little credit causes stress for young people, with one in ten (11%) fearing they will never be able to pay their debt back.
Non-UK nationals also struggle to get credit when they move to the UK. There are over seven-and-a-half million foreign nationals living in Britain and that number is increasing. This is a sizeable market that financial institutions have difficulty accessing – despite the majority being in full-time work. Many other countries operate on a system where borrowers begin on the median (with an average credit score), however in the UK you begin with a poor score and have to build it up. For immigrants and the young this means you start off in a bad position.
Big Data Scoring proposes that the solution to this problem is intelligent research of a loan applicant online, to build a clearer profile which helps to assess their creditworthiness. There is a wealth of information about us online, from our social media profiles to our online behaviour – for example, how long we spend reading the terms and conditions on an application has some bearing on how cautious we may be with our finances. We can harness this data to get clearer insight into people’s financial lives. Used in conjunction with existing scoring methods, this results in more accurate scores – meaning greater opportunity to lend and a reduction in risk.
Erki Kert, CEO at Big Data Scoring, commented: “Young people have come into adulthood with an almost immediate online presence, unlike previous generations. We have seen that this can be as much, sometimes more, of a barometer of their creditworthiness as the traditional approach applied to older people.”
“Increasingly, banks are starting to use this alternative source of information to support their credit checking; it should eventually become an industry standard.”
“Banks and lenders in the UK have a duty to lend responsibly and sensibly, yet they can’t always do this because many are still relying on outdated systems of credit scoring. In essence this is luddite banking, despite financial institutions having more data at their fingertips than ever before.”
“Too much credit is a consequence of the current failings when it comes to credit scoring young people and highlights the need for action to ensure people are given what they need but also what they can afford.”
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