Best Practice Case Study: How HSBC won Consumer Credit Team of the Year

As with many, a major difficulty for HSBC during 2020 was the effects of the pandemic – with the business having to continue to lend despite the operational challenges of branch closures, call centre relocations and temporary unemployment caused by lockdowns. According to the entry, it was able to continue lend responsibly because of three key improvements.  

Firstly, it puts this down to Increasingly making use of automation in its decision-making process through strategy enhancements, improved data feeds and ongoing optimisation of its pre-approval and pre-eligibility framework. This has seen credit referral rates reduce from 30% to below industry levels at less than 10% in 2020. 

This, according to the firm, has provided increased volumes of affordable lending through improved customer advocacy as a result of the speed of the decision-making process, which in turn reduced application cancellation rates. It also supported its move to becoming a digitally focused business.  

Second on the list of the improvements that have been made was to the data, with HSBC UK becoming one of the first users of the credit reporting agency Experian’s affordability block of data in live decisioning. This allowed the firm to continue the process of lending to individuals who were not affected by the Covid pandemic.

The new data also enabled it to develop strategies that could identify those who were vulnerable and impacted by a loss in income, allowing it to be more surgical in providing lending solutions specifically to their customers’ needs.   

This, combined with increased use of internal transaction data and advanced analytics modelling techniques, means it’s been able to identify key segments of vulnerability previously not identified or used within its credit decisioning process.  

Finally, it benefited from the development of advanced analytical techniques which improved its decision modelling approach. As part of this it considered different statistical techniques such as gradient boosting and neural networks to build more efficient models.  

This work not only improved the predictive capability of its models dramatically, whilst also improving new business delinquency, has allowed it to simplify its model landscape from more than 70 to less than 15 across all unsecured products. This led to increased sophistication in each model and how it’s used and monitored but with more transparency and alignment on customer decisions. 

Reflecting on the reasons why HSBC UK took home the award, the judges said it displayed a great use of advanced analytics and new data. This was combined with a simplification and automation of processes, and a focus on new analytical techniques being used to improve lending decisions.