We’ve come together with mnAI, a UK company data platform, to help banks manage risk, and combat financial crime and fraud. This will allow you to better understand end-customers and make insightful, data-driven decisions for revenue growth and better customer service.
Last year, the government introduced a number of initiatives to help businesses during lockdown. Most notably, these included the Bounce Back Loan Scheme (BBLS) and Coronavirus Business Interruption Loan Scheme (CBILS). It's probable that the schemes were accessed by some customers where the risk of default is now considered to be very high.
According to the British Business Bank, £79.3 billion of loans have been approved to 1.67 million businesses through government lending schemes since March 2020. A Public Accounts Committee Report (December 2020) estimated that losses from fraud and credit risk could reach somewhere between £15 billion and £26 billion.
Consequently, lenders are seeking to quantify this risk, while developing innovative solutions to mitigate and manage their exposure.
The solution combines proprietary data and cutting-edge technology to create new, meaningful, and actionable insight that will reduce risk, improve collection success and decrease operational expenditure.
As the technology and underlying data is already established, multiple scenarios have been catered for; from simple API-based data requests to more-considered solutions that include the optimum collection and recovery operating model and the design of your staff’s user journey.
Our solution can also be implemented quickly and at scale, and includes support throughout the engagement process.
Strategically, our solution also provides additional value-add by augmenting current data and risk markers to gain additional insight on new or existing businesses that have accessed the government backed loan schemes.
“Our exciting collaboration with mnAI combines our expertise and capabilities in data management and data analytics to help our clients address these challenges.”
