Enhancing Financial Decision-Making Through Explainable AI And Blockchain Integration: Improving Transparency And Trust In Predictive Models

Abstract

Financial Industries tend to adopt technologies faster than any other industries. Recently, they have started adopting artificial intelligence (AI) and machine learning algorithms at a rate faster than other sectors. Owing to this, they have undergone a seismic transformation. AI-powered predictive models have shown extraordinary ability to analyze massive amounts of financial data, detect patterns, and make correct predictions. These capabilities have fuelled breakthroughs in various fields, including credit risk assessment, investment techniques, fraud detection, and algorithmic trading. However, the opaqueness of AI models has arisen as a significant worry in the financial decision-making landscape alongside rapid growth. Transparency and interpretability in AI-driven forecasts are becoming increasingly important to financial organizations, regulators, and customers. Many AI algorithms are natural black boxes, and this raises questions on judgments made, what factors influence them, and whether they are biased. Lack of explainability hinders the broader acceptance of AI technologies and poses potential risks in regulatory compliance, accountability, and customer trust.

The eXplainable AI (XAI) has emerged as a critical field of research and development to address the black-box nature of AI algorithms. XAI aims to provide human-understandable explanations for the decisions made by AI systems. Even though XAI can explain the decisions made, there is no guarantee that these decisions were not tampered with or manipulated by some adverse actors. This makes it questionable for its applications in financial and other such sectors where transparency, auditability, and security are crucial. Integrating XAI with blockchain has emerged as a compelling solution to address the challenges AI models face in financial decision-making. Blockchain, best known for its decentralized and immutable ledger, complements XAI's objective of providing transparent and human-interpretable explanations for AI predictions. Combining these two cutting-edge technologies offers a synergetic approach to enhance trust and transparency in financial AI models. In this paper, we discuss the critical aspects of the XAI-blockchain fusion, its potential benefits in finance and also the limitations and challenges in implementing it.