From Regulated Institutions to Agile Hedge Funds

Artificial intelligence (AI) is transforming the financial industry, offering new ways to analyze data, make decisions, and automate processes. From the heavily regulated world of traditional banking to the more agile and experimental world of hedge funds, AI is increasing efficiency, managing risk, and driving innovation.

The Historical Context of Artificial Intelligence in Finance

AI and machine learning (ML) have a long history in the financial sector, with roots in the 1980s when banks and insurers began using AI to develop personalized financial plans for customers and combat fraud. Early AI systems, while basic and rule-based, have evolved into sophisticated models capable of predictive analytics and automated trading.

In the 2000s, a wave of innovation in finance fueled by the growth of big data and advances in computing power further accelerated AI adoption in finance. Machine learning models became integral to credit scoring, fraud detection, and risk management. These early successes laid the foundation for AI’s broader adoption in finance today, where it is not only a tool for operational efficiency but also a strategic asset that provides competitive advantage. By 2017, Goldman Sachs had developed automated trading programs based on machine learning models, and today it is actively developing generative AI projects for non-client-facing work.

Impact of AI on Fraud Detection and Risk Management

AI has dramatically improved fraud detection and risk management in finance. Machine learning models can analyze transaction patterns in real time to spot anomalies, flagging suspicious activity before it escalates. AI’s ability to predict and mitigate risk is particularly valuable in credit risk assessment, where accurate prediction is critical.

However, using AI in these areas also comes with challenges, especially in regulated environments. Regulators such as those governing banks require that these institutions can demonstrate how decisions are made and ensure that models are free from bias or error.

Challenges of implementing artificial intelligence in regulated sectors

Integrating AI into regulated financial institutions has been stymied, particularly around the interpretability and traceability of models. Regulators require financial models to be transparent, with decisions traceable to specific inputs and processes. This “glass box” approach contrasts with the “black box” nature of many advanced AI models.

For example, large language models (LLMs), such as those used in natural language processing, often operate in ways that are not fully understood, even by their creators. This lack of interpretability makes it difficult for financial institutions to justify their use in critical areas such as credit scoring or loan approval processes, where decisions must be defensible in court or in regulators.

The need for explainability is not only a regulatory issue, but also a business one. Without clear explanations, there is a risk of misinterpretation, which can lead to significant financial or reputational losses.

AI is now a key regulatory area in financial services, along with cybersecurity and data privacy. Firms must navigate complex regulations at global, federal, state, and industry levels. Europe is leading the way with the EU AI Act, while the U.S. effort includes state laws and federal guidance, such as the NIST AI Risk Management Framework and recent implementing regulations. As the regulatory landscape evolves, financial institutions must proactively manage AI risk and compliance.

Artificial Intelligence in Hedge Funds: Flexibility and Innovation

Hedge funds operate with far fewer constraints, allowing them to experiment more freely with advanced AI models. They have been quick to adopt machine learning and AI technologies, particularly in the development of trading algorithms that can process and analyze vast amounts of data to identify patterns and predict market movements.

Hedge funds’ flexibility allows them to use more experimental AI models, including ones that might be considered too opaque or risky for traditional banks. This freedom has led to rapid innovation in the hedge fund sector, with AI playing a central role in developing new trading strategies and managing large portfolios.

In fact, researchers from Texas A&M University and Finland’s University of Vaas looked at the performance of AI-based hedge funds and found that firms with higher levels of automation performed significantly better. The researchers found that AI funds achieved average returns of 74–79 basis points per month, compared with average returns of 0.23–0.28 basis points for the least automated funds. The results underscore the transformative potential of AI in finance and, more broadly, in innovation-driven sectors.

The Future of AI in Finance

The future of AI in finance points to even deeper integration. Smaller, more specialized AI models could address some of the current challenges in regulated sectors and provide a balance between innovation and compliance. Additionally, advances in model interpretability will be key to ensuring that AI systems are both efficient and trustworthy.

For business leaders in finance, understanding the evolving AI landscape is essential. By understanding the history of AI in finance, current regulatory challenges, and technological advances at the frontier of innovation, institutions can navigate the complexities of AI implementation while maintaining a competitive advantage.