The role of AI in the lending and leasing industry

At the annual Linedata Exchange Europe conference in London, Bertrand Cocagne, Head of Product at Linedata Lending & Leasing, outlined the obstacles AI needs to overcome to be a real game changer in lending and leasing.

Artificial Intelligence (AI) is by no means a new buzz word and we’re already seeing examples of it being used to significantly increase efficiencies around new business processes in lending and leasing. In the last ten years we’ve seen a major step forward. In small ticket and auto finance some lenders are using automated credit scoring to underwrite almost a 100% of deals. The trust however is not there yet across all segments of asset finance, and in the more complex and bigger ticket credit cases AI is no more than a guide helping humans validate decisions.

Automatic credit scoring and processing is arguably the most advanced use of AI in asset finance. In theory, masses of back-office data built up over decades around the customer, their assets and payment history should inform the decision on whether to accept or refuse credit. The sticking point is what type of criteria is being used to reach the decision, and whether it’s in contravention of fair lending laws and regulations. In the absence of being able to predict how a system is going to use the data, businesses need to be extra vigilant with the type of data being input and also be able to explain why the decision was reached.

For loan origination, AI is already finding its role as a sales adviser, helping with customer decisions through the automatic analysis of supporting documents. It’s a great system to speed up processes, but on the flip side it boils down to whether people can trust the technology enough to allow an algorithm to manage their money, or if traditional human interaction will prevail. For now it’s a combination of humans interpreting data presented by AI, including key analysis that helps to direct both sales and credit activities.

Acquiring an empty artificial intelligence unit is easy, and in our increasingly connected world gathering mases of data from the back office doesn’t present a problem. The most difficult task for any business looking to deploy AI is how to train the system. It’s only by improving learning methods we can truly realize this technology’s real potential.