How can data sharing enhance credit risk modelling?

Data sharing can enhance credit risk modelling in multiple ways. Open Banking provides a much deeper understanding of an individual.

Until now credit scoring has been built on the use of historical data. And with the advent of sharing statement information and transactional information this can provide an enhanced view of an individual’s income and expenditure for risk teams.

Current account information can also be beneficial to gain a view of affordability. This adds value to understanding an individuals capacity to afford credit services. Overlaid with savings, pensions and other financial elements, banks can get a comprehensive view of a person’s credit risk and financial behaviours in a much more detailed view than previously. This can benefit customers by more accessible and inclusive lending decisions but also benefit businesses by better credit risk modelling.

 

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Data sharing can enhance credit risk modelling in a variety of different ways. If we look at the way credit scoring today is worked it has been based predominantly on the use of historical data. With the advent of sharing of statement information, for example transactional data that provides a new and very rich source of data that can be used to enhance the understanding of a customer’s income and expenditure.
We also take information now on current account behaviour but it is very summarised information and from that we can actually infer a customer’s affordability so their income and expenditure and capacity to afford a new loan or a new credit card.
The transactional information that will come to market as a result of open-banking will provide access to a much deeper, richer source of information that provides a more comprehensive view of the way you spend your life. How you manage your finances, whether it be committed expenditure or discretionary expenditure and where you get your income from. That is incredibly valuable in terms of understanding more about your capacity to actually afford certain goods and services. So by merging credit information with statement information we can provide a whole new fresh insight on a consumer’s capacity to consume financial goods and services.
If you then extend that into areas such as other complimentary information, such as the value of your house, the value of your car, that starts to build up again a picture of the assets you have available to you and what value they have. If you then add into that savings information and maybe even your pension you get a much more rounded view of an individual’s financial circumstances and how they can utilise and invest their money more wisely.
So I think it is really open to a whole range of new opportunities for organisations generate greater insight on the back of people’s everyday financial behaviour but to generate new products and services which can benefit consumers, help them manage their finances in a better way and generate benefit for them as part of their everyday lives, simply by helping get a better understand of what they can actually afford and making an informed judgement on the basis of that.
It should also help organisations as well minimise the risk associated with making loans. There is a view that being able to get access to things like transactional data and then being able to match that to credit information, maybe savings data and pension data would enable you to get a much better understanding of the risk associated with providing a mortgage to that individual or maybe a credit card. Being able to monitor that behaviour throughout the life of a customer gives you opportunities to both minimise the risk when their financial habits change and they might go to debt but also to look at opportunities are available in the future in terms of new products and services they may wish to consume.
So for example you may have recently had your first child which has been illustrated in terms of your transactional behaviour in your bank statements. That in itself could provide opportunities to provide new products and service to that customer and also help them income plan for the future in terms of the investments required to support a family. So that’s a great illustration of how transactional data could be used to actually forward plan an individual’s spending and financial consumption. So improve the quality of their lives and make sure they don’t get into debt and then they can plan effectively and manage their lives effectively.