There is a need for a more accurate scoring within lending. By implementing alternative and social scoring, lenders can achieve higher accuracy in their credit evaluations.
By collecting data from multiple alternative data sources and connecting them to loan performance, banks can harness unique insights that can be leverage in the competitive loan market. A case study of a large European bank showed that the bank saw an 14.7% improvement in credit scoring accuracy for new clients after implementing alternative scoring. The improvement in credit scoring accuracy lead to lower credit losses and higher loan approval rates for the bank.
Lenders have always faced the challenge of capturing new customers, and especially the profitable ones. As societies and technologies evolve, data-driven and social scoring is a great complement to traditional credit scoring.
An alternative data source such as social scoring provides deepened insight into the applicant's ability to pay back a loan. By utilizing web-front analytics, the applicant's behaviour on the web can give a fairer credit score. The types of social and alternative data that are interesting for scoring purposes are Facebook, LinkedIn, email provider, IP address, geolocation and device database's information, to mention a few key sources.
A scoring method that gathers information such as online behaviour, location-based data and additional data points will tell a much more accurate story about people's history and credit scores, reducing fraud and making lenders able to capture customers they would otherwise miss out on.
For applicants with an irregular income (e.g. freelance workers, students, expats) or applicants that are new clients to the bank, alternative scoring is an excellent tool for improved pre-scoring of customers, which will lead to improved application quality.
Social and alternative data-driven credit scoring helps banks and financial institutes to improve their accuracy and balance their risk appetite. For me, this is a perfect example of how technology can accelerate businesses growth and at the same time help lenders to get access to a fair credit.
Per holds degrees from Yale University and KTH Royal Institute of Technology. After 6 years in Singapore working for Fortune 500 tech giant ABB, he returned to Sweden in 2015 to join Emric, a disruptive FinTech software provider. As the lead for marketing and business development, he helped to establish Emric’s position as a strong Nordic player within analytics and lending software and as a first-rate provider of leasing software both in the Nordics and across Europe. After Emric joined the Tieto Group in 2016, Per became the head of credit business development and analytics. Per is excited about growing the analytical products suite in the field of machine learning and AI.