Introduction

AI's potential to increase the efficiency of business processes is widely accepted. Financial services providers have explored AI across a variety of applications, including:

• Fighting fraud across industries and use cases by leveraging large amounts of data and machine learning algorithms.
• Providing enhanced customer service through interfaces such as chatbots.
• Making more accurate credit decisions by considering traditional and alternative data points that signal creditworthiness.
• Augmenting risk and control practices related to credit monitoring, payment collections, loan recovery, and loss forecasting.
• Using natural language processing to analyze regulations, news publications, consumer feedback, and other large volumes of written information.
• Strengthening cybersecurity protocols through behavior-based detection, network metadata, malicious-attack identification, compromised-account detection, and enhanced forensic analysis.
• Increasing the reach and effectiveness of marketing activities through more tailored product recommendations.
• Providing highly tailored investment advice and supporting algorithmic trading by analyzing historical market data.

Financial institutions are among the most highly regulated businesses. Practically all their internal and external processes are subject to specific legal standards. However, navigating these rules and interpreting them in connection with new technologies such as artificial intelligence is not always easy.

This guide discusses the current regulatory landscape, recent developments in the field, and how financial regulators are using AI.

Relevant Rules Applicable to Financial Institutions