July 13, 2017

Retail banks will increasingly use artificial intelligence to help determine credit ratings: GlobalData

LONDON -- Artificial intelligence (AI) is anticipated to make a significant impact on the retail banking sector, such as using non-traditional data types to assign credit ratings to potential borrowers, according to research and consulting firm GlobalData.

The company’s latest report explains how technologies such as machine learning, predictive analytics, and natural language processing (NLP) are already making their mark in banking, with both front-office and back-office operations set to be transformed.

From the consumers’ perspective, NLP technologies such as chatbots are starting to allow more effortless and intuitive interactions with banks. These chatbots often employ highly advanced analytics to offer financial insights to consumers, such as warning them when they are likely to go overdrawn or recommending changes in behavior that will allow them to save money.

“Consumers, especially younger ones, can lack confidence around financial matters and find it hard to manage their finances effectively," states Daoud Fakhri, principal analyst for retail banking at GlobalData. "There is therefore a potentially large market for AI-based services that offer a guiding hand or can assume some of the responsibility for making appropriate decisions.”

AI will also transform behind-the-scenes operations. One area that is already experiencing significant change is lending. Traditional credit scoring techniques are ill-equipped to deal with consumers who lack conventional credit records, which is a common occurrence in developing markets. However, some lenders are now using AI to analyze non-traditional types of data, such as mobile phone usage and social media profiles, to predict the creditworthiness of borrowers.

“Although these consumers may not have access to regular banking services, many are heavy users of mobile phones and social media, and this generates huge amounts of data that can be analyzed to model their financial reliability," according to Fakhri. "There is therefore huge potential to widen access to credit without exposing lenders to higher levels of risk.”

 

 

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