When I ask a lender for credit, there are two questions the lender would like answered: is Paul willing to pay and is he able to pay? Most lenders will try to answer these questions by taking data about me and applying some techniques from mathematics to my data.  They do this to estimate the likelihood of me not paying (a technique often called credit scoring) and to estimate how much disposable income I have (often called an affordability assessment). While the mathematicians have made advances over the years in estimating willingness and ability to pay, it is the availability of more data about all of us that has driven the improvements in lenders’ ability to decide who is both willing and able to repay their credit.  

We all go through our lives leaving a trail of data in our wake.  Some commentators have called this ‘data exhaust’. This has always been the case, but in advances in technology and our appetite to use it mean that more of our everyday behaviour is captured by computers and we are all giving off more exhaust than we used to.  Our use of social media is a good example of new behaviours generating new data.  

But it’s not all about social media.  Many of the firms we deal with are generating their own data – recordings of customer service calls, visits to a firm’s website, preferences expressed through these sites, delivery histories…etc.  It all says something about us as individuals.  But of course, it needs to be relevant.  

Social media data could fill holes in the data available to credit scoring particularly where there is little other information about a person eg young people who have yet to build up a credit history.  It may be of more use in countries where robust credit history based systems do not exist or there is no mature credit bureau. 

It is worth noting, however, that social media data is not widely used for the assessment of credit risk at the moment and is not used at all by us.  While some of these new data sources (whether they’re external to or internal to an organisation) might be useful in predicting credit risk, we feel that the impact will be marginal. 

We should not forget that there are rules governing how data can be used in credit decisions.  When considering new sources of data we need to answer a number of questions: has the person that the data belongs to given their consent for the data to be used?  Who owns the data and can the data be used for the purposes of credit scoring?

Consideration should always be given to what data should be used and for what purpose. 

For the majority of UK adults, the predictive power of credit account data, supplemented with bank account transaction data remains the strongest basis for credit decisions.  They provide lenders with high quality, consistently structured data with clear and reliable provenance.  In simple terms, data about what people do (or have done) when using credit is currently a more powerful predictor of future credit behaviour than what they say they do (or have done) in other parts of their lives.

Nonetheless, major organisations across all industries have taken data seriously for a long time. Many are using social media and website visits to understand what people are interested in and how they interact with organisations. Organisations are always looking for new data to help them better understand people, not just when it comes to credit decisions.