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ARTIFICIAL INTELLIGENCE IN LENDING
The dawn of the AI era in lending
With an estimated $5 billion in the global AI market by 2020, unsecured lending is expected to grow by more than 960% in the next four years. Welcome, the new AI era in lending
V. Ramkumar
Senior Partner, Cedar Management Consulting International LLC
T
he days when it took customers several weeks to apply for a loan, and yet run the risk of eventually being turned down by banks, are long gone. The dawn of the artificial
intelligence era has now enabled banks to develop quick risk assessment models, and instant credit scoring that fast tracks the entire process and also creates a real value differentiator in the marketplace for early adopters.
When access to funding was reported to be reduced from weeks to hours with automated lending driven by Santander, the underlying engine was primarily the AI-based quick risk scoring that enabled immediate risk assessment, speeding up the underwriting process and providing working capital within hours. Similar use cases are aplenty elsewhere – a case in point being the initiative by Scotiabank that reportedly enables lending to customers who are new to the bank, wherein the borrower’s credit worthiness is assessed using AI, enabling the decision to lend to be made the same day, even without the customer having to walk into the branch. Kabbage was the engine in both the above examples.
The application of AI is not just about quick risk assessment and same day credits. It has also been a core value proposition in reducing the error rates in document processing, and eliminating human error. JP Morgan, for instance, has reportedly adopted AI through its COIN program, that quickens document review and reduces mistakes in loan servicing. An estimated 12,000 contracts are reviewed, and almost 360,000 hours of work-load are reduced to a few seconds. Franklin Amercican Mortgage has reportedly achieved mroe than 80% document recognition for its mortgage portfolio, with minimal error rates, with a customized solution it has developed.
An even more interesting application of AI in the world of credit is in the personalisation of customer experience. RBC, for example,
provides its customers with a recommendation on their best repayment strategy, based on analysis of their financial habits, and also suggests how much they should target to be their monthly contribution. In addition to reducing the loan liability and payback period of the customer, this approach also generates a whole range of new data points that further enable having a targeted customer service offering.
The advent of AI in lending has its share of challenges, too. Essentially, there are three types of issues and challenges that are likely to be pertinent.
A. Privacy issues
With a gamut of sensitive information being processed, customers are at continuous threat of frauds, privacy and data theft. Considered a core issue that has to be grappled with, especially with all the impact created by the Facebook case, banks and customers are equally weary and likely to deal with this issue with more sensitivity in the future.
www.ibsintelligence.com | © IBS Intelligence 2018
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