How analytics and big data can accelerate Customer Experience performance for lenders



There has been a lot of discussion in recent years over the potential gains that analytics and big data can offer financial service organisations. However, even more recent advances in machine learning and deep customer analytics in particular offer credit providers capabilities that can have an enormous impact on a number of key areas of their business. Chief among these is the customer experience. However, it can also generate exponential gains concerned with the lending process itself.


Enhancing credit risk modelling outcomes with optimised data at the core


By becoming data-centric, lenders can understand loan applicants to a much more sophisticated degree. While historical means of testing for a loan applicant’s creditworthiness are of course still essential, customer analytics technology equips lenders to dive deep into each customer’s profile to a granular level. 


Another benefit of integrating with deep customer behaviour analytics technology is that lenders can gain access to all-new data points. These new creditworthiness analysis yardsticks provide lenders with a more detailed view of the kind of personal financial management and likelihood to keep up with repayments that customers exhibit. 


Another advantage is that technology-led credit risk modelling allows for faster reporting and more efficient regulatory compliance. Richer data and its use also positions lenders better to receive stronger feedback from regulators with regard to Treating Customers Fairly regulation and to recover debts from bad debtors. Big data and deep analytics helps lenders to know customers better and therefore, it provides greater insights with regard to the right approach to take to defaulter customers. For instance, a customer who simply misses a payment and one that is experiencing financial difficulty require two completely different approaches.


Integrating new data point analytics for more precise lending decisions


For instance, one type of data point that new technologies can incorporate into a modelling process revolves around “credit invisibility”: loan applicants that don’t use a credit card or other credit facilities, and so are deemed high-risk. As a result of the lack of data with which to ascertain their creditworthiness, lenders tend to reject their application. In such scenarios, it is lose-lose: the customer has a negative experience and doesn’t get the loan that they are looking for and the lender loses out on new business.


An example of a way to improve analysis of this type of loan applicant is to take into account historical home rental payments. Traditional lending models do not take account of rental payments whatsoever. For loan applicants who don’t have a good credit score simply because they don’t use credit facilities but are excellent when it comes to repaying debt, a data point such as rental payments can make the difference between receiving a positive or negative loan application response. After all, if a loan applicant has successfully made monthly payments of their rent – likely their biggest single outgoing payment each month – for a period of years without any default, it is a strong indicator that they are creditworthy.


Improving default rate control


With the right technology to gather and interpret it, more good data equates to more informed decision-making. The result for lenders is that they can make loan decisions with more confidence of repayment and, crucially, control default rates with greater precision.


After an initial loan application, machine learning techniques enable lenders to keep track of customers’ credit profiles, constantly updating them based on how data points may change. For instance, if a customer typically makes repayments at the beginning of a month and suddenly makes three in a row on the deadline day before a default would kick in, machine learning can update their profile as higher risk than before.


Accelerating the customer experience


The seemingly unstoppable rise of fintech in recent years has, in large part, been down to leading fintechs’ ability to transform the financial services customer experience. Nimble digital challenger banks and fintech lenders offer fast online onboarding and loan application processes. They avail of more modern, technologically-driven communication channels, and often more dynamic, responsive and data-driven cultures.


This enhanced experience has acted as an extremely strong unique selling point, giving them a competitive edge over their traditional finance counterparts’ typically slow and antiquated processes. Lenders today can look to these fintechs for inspiration and see the enormous potential in transforming their own customer experience.


A process that is faster and with fewer “red tape” hurdles to jump over immediately improves the customer’s experience. This can reap a number of important secondary benefits. Higher customer satisfaction is directly associated with improved client retention, recommendations via social media and word-of-mouth, and a greater likelihood for repeat custom.


Lenders can take their business to the next level with AI-driven analytics and machine learning technology


The advantages of harnessing the power of data and customer analytics are clear. The right technology integration can lead to considerable benefits for both loan applicants and lenders. Applicants benefit from greater customer experience and a more accurate loan decision. And lenders benefit from greater control over default rates and a more detailed, precise view of their customers’ credit profiles. Moreover, it can generate higher client retention rates and provide a vital advantage over competitors reliant on traditional processes.



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