The world of finance was an early adopter of artificial intelligence (AI). Banks were already using computer models in the 1980s to identify and take advantage of price discrepancies. Rule-based AI systems in the 1990s accelerated the fight against fraud and sought to improve credit scoring.
As the 2000s got underway, machine learning began being used in risk management and customer segmentation. More recent years have seen the advent of AI-powered high-frequency trading, real-time fraud detection, and vastly improved creditworthiness tools.
Today, deep learning models are driving innovation in customer service, financial planning, trend spotting, and FinTech applications such as payment systems, lending platforms, and regulatory compliance tools.
According to global management consulting firm McKinsey, generative AI could boost productivity in the banking sector by 3 to 5 percent and reduce operating expenditures by several hundred billion dollars annually.
Gen AI is known to generate false or illogical information at times. An airline learned that the hard way when it was successfully sued over incorrect chatbot information about its bereavement fare policy.
Concerns are being raised about transparency. Are there biases in the data? Are certain groups or individuals being disadvantaged for opaque reasons? What about intellectual property rights? And perhaps the biggest issue of all in banking: how secure are these systems? Can they be hacked?
AI has proven its ability to deliver results in finance, but the industry will have to proceed cautiously as it pursues future gains.