Is Artificial Intelligence the Right Technology for Risk Management in Financial Services?
Financial services have long struggled to mitigate risks while maximizing rewards, and this challenge has only become more complex as the industry has evolved. Today, many financial institutions rely on artificial intelligence (AI) to manage risk and identify potential threats. However, while AI has proven effective in some areas such as fraud detection, its wider use in managing risk has been limited. That is, until the release of AI chatbots like ChatGPT, which use natural language processing to understand prompts and generate text or code. But, is AI the right technology for risk management in financial services?
AI Chatbots and Generative AI Technologies: A Game Changer for Risk Management
Experts believe that over the next decade, AI will be used for almost every area of risk management in finance, including assessing new types of risks, developing strategies to mitigate them, and automating the work of risk officers. More than half of large financial institutions are already using AI to manage risk. Generative AI technologies like OpenAI’s ChatGPT or Google’s Bard can analyze a vast amount of data in company documents, regulatory filings, stock market prices, news reports, and social media. This helps to improve current methods for assessing credit risk, create more realistic stress testing exercises, and capture and analyze large volumes of structured and unstructured data in real-time across the enterprise.
Early Use of Generative AI for Risk Management
Financial institutions are typically hesitant to discuss any early use of generative AI for risk management. Concerns around the quality of data being fed into AI systems, the complex statistical models that must be validated, and data privacy are significant obstacles that must be overcome. Some financial institutions are in the early stages of using generative AI as a virtual assistant for risk officers. Such assistants collate financial market and investment information and offer advice on strategies to mitigate risk.
SteelEye, a provider of compliance software for financial institutions, has already tested ChatGPT with five of its clients to identify suspicious communication for further examination. Insiders say ChatGPT proved useful in identifying suspicious communication in just minutes or seconds that compliance professionals might take hours to sift through.
Accuracy and Bias
Concerns have been raised about false information, bias and the effective ability for ChatGPT and similar AI technologies to comply with regulations. The complexity of these technologies may also make it hard for financial services firms to explain their systems to regulators.
The European Risk Management Council has flagged ChatGPT’s complexity as well as other AI technologies as a potential challenge for some financial institutions to implement.
AI is an active field of research in finance, and its use ranges from risk management for investment portfolios, fraud detection, and automating customer service to predicting credit risk and forecasting market trends.
In 2019, Wells Fargo, one of the largest banks in the United States, launched Control Tower, an AI-powered digital assistant that helps mortgage lenders navigate regulatory requirements.
While AI has already shown its potential in risk management and regulatory compliance, the challenges surrounding data privacy and the validation of complex statistical models are still uncertain. However, with rapid advances in technology and continued investment in AI research, the use of generative AI and other AI technologies in financial services will likely expand rapidly in the coming years.
AI is poised to transform the way financial institutions manage risk by automating tedious and time-consuming work. Despite some concerns over the quality of data input, complex statistical models, and data privacy, the benefits of AI in risk management outweigh the drawbacks. However, financial institutions must be careful to ensure that AI is not used as a substitute for human judgment in risk management. They must also ensure they can adequately explain how AI systems operate to regulators and comply with strict regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). With the right investment, oversight, and management, the potential of AI in risk management in the financial services industry is enormous.