The Institute of Analytics (IoA) has a strong interest in AI and is committed to this market. While AI offers multiple benefits, it’s critical to appreciate the risks that emerge from unregulated technologies. AI has gained rising interest in the finance industry, transforming multiple parts of the industry. This movement has been supported by new data, including various datasets and online textual information. As businesses integrate hybrid cloud solutions and strengthen their computer skill sets, they are more capable of maintaining high performance at scale, creating a competitive advantage.

AI empowers financial organisations to make data-focused decisions, securing valuable insights and strengthening overall performance. AI provides diverse opportunities within finance, incorporating fraud detection, enhancing the customer experience and the automation of policy enforcement.

It’s critical to highlight that while data is a fundamental element of AI, it can also act as a customer of AI. Not all data is appropriate for model training, and some companies lack the sample size required to test their tools. AI can support data creation, supporting further growth and contributing towards developing more structured AI algorithms.

To explore real examples of AI in finance, we can observe JP Morgan as a good example. The leading bank has publicly prioritised an AI-first approach and underwent various business transformations to break down legacy silos and deliver horizontal data consolidation and collaboration systems. Back in 2021, after volatile retail stock markets, traders observed the impact of retail activity on various stocks. As a consequence, JP Morgan explored a structured approach to monitor social discussions for determining short squeezes and implementing timely risk mitigation measures. This example highlights the need to implement a proactive leading indicator with strong predictive options rather than depending on retrospective factors. By exploring historical data and combining the concepts of influence, the JPM team created a leading risk mitigation algorithm model.

Activities with multiple search processes can take time, with considerable variation in completion time depending on employee efficiency. Businesses like JP Morgan used auto-responsive models to constantly monitor business processes, and determine the task at hand.

The availability of big data relevant to business use cases has opened up various opportunities for AI within the finance industry. For example, asset management benefits from using historical information and emotional analysis to determine certain market trends and investment opportunities. Alternative data types like customer behaviours strengthen the ability of algorithms to identify vital data points and support critical investment decisions.

As new technologies advance, there is a risk of disruption while regulators try to maintain pace. While AI offers various benefits to the financial industry, it’s critical to prioritise implementation safely and ethically. Focusing on the necessary measures will enable the necessary foundations to ensure consistent ethical use of AI in the future. Regarding financial customer and investor protection, AI services in finance can create or intensify other risks. Applying algorithms in financial decision-making can lead to biases, causing risk to customers and investors.

Policymakers should explore the implications of these technologies and recognise both the benefits and risks associated with implementing AI. By focusing on creating regulatory frameworks, policymakers can provide customer protection and the ethical application of AI in the financial industry.

More recently, the European Parliament introduced the Artificial Intelligence Act, a major move towards integrating structured rules for AI technology. The act divides AI into four risk levels, and AI applications involving vulnerable groups would face stricter conditions. The introduction of the AI Act highlights policy recognition of the need to regulate AI technology. The act aims to create clear rules to control risk and safeguard customer investor interest, but with accelerated developments in AI, policymakers face a growing challenge in maintaining pace with the changes.