AI can recommend - but can it explain? The transparency gap in digital advisory

Artificial intelligence is rapidly changing digital advisory across various sectors. In the BFSI sector, AI is becoming the basis for decision-making, moving beyond experimental use. As adoption grows, a pressing reality is emerging in boardrooms and regulatory discussions- while AI is excellent at making recommendations, it struggles to explain its decisions. Today's AI systems are very good at finding patterns, improving results, and providing highly personalized suggestions. However, many of these systems lack decision visibility, offering little to no insight into how a decision was made. 

Strategic issue

For leaders dealing with risk, compliance, and customer trust, this lack of transparency is now a strategic issue, not just a technical problem. Trust, risk, and the cost of unexplained decisions are at the heart of digital advisory in BFSI. Stakeholders like customers, regulators, and internal risk teams - expect clarity and accountability with loan approvals, investment suggestions, or fraud alerts. When an AI model denies a loan or flags a transaction without clear reasoning, it creates friction in what should be a smooth, trust-driven experience. It also raises concerns about bias, fairness, and governance. The effects of this 'explainability gap' are significant. 

For financial institutions, it creates operational blind spots. Without knowing how models make decisions, it becomes hard to audit results, ensure compliance, or improve systems effectively. For customers, it undermines confidence. A recommendation without reasoning often appears random, no matter how accurate it is statistically. For regulators, this creates systemic risk, which could damage financial systems if ignored. This is why there is growing support for AI transparency and accountability in regulations. 

From Accuracy to Accountability 

Global frameworks increasingly stress the importance of explainable AI, model auditability, and ethical AI use. Regulators now ask not just if AI works, but how it works, why it works, and whether it can be trusted. In highly regulated areas like BFSI, this shift is changing how AI can be embraced. However, the industry is still focusing on performance metrics like accuracy, speed, and scalability over interpretability. While these metrics matter, they are no longer enough. The next stage of AI development will require a more balanced approach - one where explainability is integrated into the design and deployment of AI systems instead of being an afterthought. 

Explainability in AI isn't about oversimplifying complex algorithms into basic reasons. It's about developing ways to provide meaningful, context-aware insights into decision-making processes. This could involve model-agnostic explanation layers, interpretable architectures, or combined systems that merge rule-based logic with machine learning. The aim is not to trade sophistication for simplicity, but to ensure that complexity does not sacrifice clarity. 

Rethinking AI Strategies

This shift presents both a challenge and an opportunity for CXOs. The challenge lies in redesigning AI strategies to add transparency without hurting performance. This may require investing in new tools, governance frameworks, and collaborative skills that connect data science, risk, and compliance. It also involves a cultural shift that values explainability as a business necessity, not merely a technical detail. The opportunity is even more compelling.

Competitive Advantage 

Organizations that excel in explainable AI will be better prepared to meet regulatory expectations and will stand out based on trust. In a world where customers are more aware of how their data is used, transparency can become a strong competitive edge. It allows organizations to shift from automation without clarity to accountable intelligence, creating systems that are not only smart but also understandable and defensible. Additionally, explainability improves internal decision-making. When AI outputs can be understood, they become more useful. Business leaders can make informed choices, challenge assumptions, and connect AI-driven insights with broader strategic goals. In this way, explainability is not just about meeting regulations; it is also about maintaining control. 

Looking ahead

As AI continues to grow in digital advisory, the industry is at an inflection point. While accuracy will continue to matter, it will no longer be the sole measure of success. The true distinguishing factor will be the capacity to explain, justify, and support AI-driven decisions. The focus is no longer on whether AI can deliver results - it clearly can. The real question is whether those results can be trusted, understood, and governed. In BFSI, where trust holds significant value, this difference is crucial. The future of digital advisory will rely not just on the most advanced algorithms but on the most transparent ones.

Anuj Khurana

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Anuj Khurana is Co-Founder and CEO at Anaptyss.