The finance sector is among the keenest adopters of machine learning (ML) and artificial intelligence (AI), the predictive powers of which have been demonstrated everywhere from back-office process automation to customer-facing applications. AI models excel in domains requiring pattern recognition based on well-labeled data, like fraud detection models trained on past behavior. ML can support employees as well as enhance customer experience, for example through conversational AI chatbots to assist consumers or decision-support tools for employees. Financial services companies have used ML for scenario modeling and to help traders respond quickly to fast-moving and turbulent financial markets. As a leader in AI, the finance industry is spearheading these and dozens more uses of AI.

The risk landscape of AI is broad and evolving. For instance, ML models, which are often developed using vast, complex, and continuously updated datasets, require a high level of digitization and connectivity in software and engineering pipelines. Yet the eradication of IT silos, both within the enterprise and potentially with external partners, increases the attack surface for cyber criminals and hackers. Cyber security and resilience is an essential component of the digital transformation agenda on which AI depends. A second established risk is bias. Because historical social inequities are baked into raw data, they can be codified--and magnified--in automated decisions leading, for instance, to unfair credit, loan, and insurance decisions.