The Potential Impact of Explainable AI in Finance

by Joy Dasgupta, CEO at GyanAI

Amidst a digital revolution, the financial sector is experiencing a profound transformation driven by the power of artificial intelligence (AI). From fraud detection and prevention to predictive analytics and customer-centric chatbots, finance is embracing new possibilities with AI and redefining the quality of products and services. However, as AI becomes increasingly integrated into financial systems, a new imperative emerges—the need for transparency, accuracy and trust. It is where the promise of Explainable AI is most evident.

Avoiding bias in decision-making

Explainability in AI is crucial, particularly in finance services. In 2017, ProPublica exposed how life insurance companies’ algorithms unintentionally perpetuated racial disparities in the US. Factors like ZIP codes and educational qualifications were used, resulting in higher costs for individuals in minority neighbourhoods, despite similar risk factors. In the UK, meanwhile, the Information Commissioner’s Office last year launched an investigation into potential job or loan applicants being disadvantaged on the back of AI-powered automated decision-making.

To address these concerns and ensure fairness, explainability is key. By providing comprehensive explanations regarding how output is generated, the methods employed, and the sources of data used, trust and confidence in the results can improve. Crucially, this transparency extends to acknowledging any limitations or uncertainties in the decision-making process.

Navigating the regulatory landscape

The financial services industry operates within rigorous regulatory frameworks where adherence to compliance standards is paramount and failure to do so can result in heavy ramifications. In this context, leveraging innovative technologies, such as Explainable AI, can transform decision-making processes, and bolster overall accuracy and compliance.

Imagine you have a compliance document for your company but, with regulations constantly evolving, keeping pace with these changes is increasingly challenging. By harnessing AI-driven systems, you can automate the comparison between your compliance document and the regulatory framework. The intelligent system would identify any material adjustments that need to be made whenever a regulatory change occurs. It goes beyond simple word changes, date modifications or alterations to entity names. The focus is on pinpointing any substantial changes within the regulations that could impact compliance. By employing Explainable AI in this manner, financial institutions can streamline their compliance efforts, not only saving valuable time and resources but also minimising the risk of non-compliance and potential penalties.

AI contributes a massive £3.7 billion to the UK economy so its responsible growth is a big focus area and important to keep the country as a global hub of innovation. The UK government recently launched a whitepaper to guide the use of AI in the country, aimed at driving responsible innovation and maintaining public trust in this transformative technology and taking a fresh approach to regulating AI. The government plans to establish a new sandbox where businesses can trial how regulations can be effectively applied to AI products and services. This will support innovators in bringing their new ideas to the market without undue regulatory obstacles. The Financial Conduct Authority (FCA) is also developing a framework for the integration of AI in financial services with the aim of establishing a comprehensive and informed approach to effectively regulate AI in the financial sector.

Enhancing explainable research

Banks can utilise Explainable AI in various functions, including fraud identification, loss and churn prediction, and debt collection. Moreover, explainability is a fundamental aspect when examining risk factors, developing investment banking strategies, managing portfolios or conducting patent research. Its significance extends to these processes, ensuring transparency and comprehensibility.

Patent research involves accessing and interpreting information from sources like the Intellectual Property Office (IPO) and more, which can be challenging due to complex terminology. Visual representations and data visualisation techniques aid researchers in understanding intricate connections between various inventions and identifying hidden patterns. Transparent and easily interpretable research methodologies not only facilitate seamless knowledge-sharing and collaboration but also contribute to mitigating biases, improving decision-making processes and ensuring ethical practices.

Overcoming challenges in implementing XAI

The implementation of Explainable AI in the financial sector is not without its share of challenges and limitations. First, the existing models, such as Large Language Models (LLMs), often operate as black boxes, making it challenging to comprehend their decision-making processes.

Retrofitting explainability into these models is a complicated task. Avoiding bias and ensuring fairness are critical considerations, as AI systems must actively avoid discriminatory practices. Second, the financial industry operates within a strict regulatory environment that demands transparency and accountability. Ensuring that Explainable AI aligns with these requirements poses an additional challenge. Accuracy and reliability are paramount in finance, and any implementation of XAI must not compromise on these essential aspects. The intricate nature of financial data also presents challenges in extracting meaningful explanations. Effectively addressing these challenges necessitates a collaborative effort between experts in the financial sector and AI research.

The past six months have witnessed a growing awareness and interest in the potential of LLMs, and their implications are profound.  Some versions of AI lack explainability and these have ignited discussions on how to leverage AI capabilities within highly regulated domains particularly when it comes to establishing trust. Retrofitting explainability into LLMs, given their black-box nature, may prove to be an inadequate approach. Therefore, injecting explainability into the outcomes produced by LLMs becomes crucial for their advancement.

The promise of AI in finance lies in demanding explainability for decisions and applications surpassing the threshold of acceptable risk. This approach enables a comprehensive and transparent AI ecosystem and it is through this integration and collective efforts that we can unlock the maximum benefits for the financial industry.