Artificial Intelligence (AI) systems are being regularly used by 18% of organisations, according to consumer information management firm Callcredit Information Group in its Fraud & Risk report.
With take-up of AI on the rise, PricewaterhouseCoopers (PwC) recently predicted that global gross domestic product (GDP) will be 14% higher in 2030 because of artificial intelligence.
This is the equivalent of an additional $15.7trn, or more than the current output of China and India combined, according to PwC research.
Data analytics AI technology is in the early stages of deployment but is already set to transform financial markets and could act as a key differentiator in performance, Matthew Hodgson, CEO of Mosaic Smart Data, tells GTNews.
Two of the key areas within financial services that are making huge advances in AI are fraud prevention and lending and leasing. Over the next three years, 24% of organisations are planning to introduce artificial intelligence for fraud prevention, Callcredit’s report found.
AI is also being used to significantly increase efficiencies around new business processes in lending and leasing. In the past 10 years, there has been a major step forward, says Bertrand Cocagne, head of product at Linedata Lending & Leasing.
“In small ticket and auto finance, some lenders are using automated credit scoring to underwrite almost a 100% of deals,” he reports.
“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 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,” he adds.
Ultimately, trust will follow from a positive experience, says Philipp Shoenbucher, co-founder and chief data scientist at AI payments firm Previse. “The key is that AI needs to enable a clear win-win situation which ensures that all parties see that they benefit from an AI-based solution that was not possible before,” he tells GTNews.
Cocagne adds: “The algorithms used within current AI systems, don’t allow for much transparency with regards to how the system rationalises its decisions, which makes it difficult for the industry to trust the outcomes, so the key to building this confidence is really the successful real-life application of AI. With real results and success stories more trust will come.”
An issue of contention is the type of criteria is being used to make decisions and whether it flouts fair lending laws and regulations. For example, a decision could theoretically be reached to grant or refuse a loan, based on the applicant’s ethnic origin due to perhaps a particular name having a higher risk factor. “This, of course, would be illegal in many countries,” says Cocagne. Without 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. They should also be able to explain why the decision was reached, argues Cocagne.
Hodgson says: “Potent algorithms for AI and machine learning (ML) are already widely available in the finance industry. The problem isn’t so much about having the latest and greatest AI/ML – it’s about the lack of the right sort of data to power the algorithms in a consistent format, accessible in one place.”
“Due to the lack of clean data, a typical time split for a data scientist/analyst in a large bank with multiple data stores and formats is probably 90% on just data transformation and only 10% collectively for all the much more valuable work, such as selecting the right algorithms to apply to the data,” he suggests.
This means that banks are only able to draw limited insights from the data, and AI is only as good as the data which powers it.
