Trust in the age of AI: Navigating customer relationships in BFSI’s automated future

By Sameet Gupte, Co-Founder & CEO, EvoluteIQ

In the world of AI, where processes are automated, trust is the core feature that cannot be coded.

Algorithms approve loans in seconds, detect fraud, and customise financial advice down to the decimal. While AI promises the unmatched efficiency, speed and personalisation, at the same time it challenges the foundational trust that has long defined customer relationships in finance.

In an industry where relationships have traditionally been built on handshakes, in-person conversations, and personal rapport, the move toward automation raises critical questions. Can AI deliver empathy? Will automation dilute the human touch? And, most importantly, can AI strengthen rather than weaken the trust between institutions and their customers?

The future is not an either-or, but wherein technology partners with humanity, adding horsepower to human ability while also providing the clear accountability and transparency clients need. To succeed, we must move from reactive compliance to proactive trust-building built on a foundation of explainable AI, supported by robust governance frameworks and oversight that ensures human judgment strengthens rather than is displaced by the key elements of the financial relationship.

The Scale of Transformation

The BFSI industry is undergoing yet another transformation in the form of an “AI First” strategy for the future. The adoption of AI globally in 2024 across this market segment has reached $26.2 billion and will continue to grow at a CAGR of 22% till 2034 ( Global Market Insights Inc.). In parallel, banks continued to achieve significant cost savings, which are “force-multiplied” by AI-enabled advances in fraud detection, underwriting, and process automation.

Banks now spend $31.3bn on AI, which is more than 30% of their tech budget. Top banks like J.P. Morgan spent $1.5 billion on AI projects that delivered exponential value and had more than 300 production use cases built with AI.

More than half of customer communication in U.S. banking is already being managed by AI systems. Industry outlooks further recommend that AI’s role in these collaborations may grow to comprise nearly all customer touchpoints in time.

The Imperative of Trust in BFSI’s AI Era

The contemporary financial services industry faces a profound challenge, i.e. the need to rapidly adopt AI to remain competitive while simultaneously maintaining the human element that underpins customer relationships. Financial institutions are presented with a stark choice: embrace AI for customer service or risk being outpaced by competitors. The stakes associated with this decision are substantial, as customers who receive prompt and effective support tend to remain with their providers longer and increase their engagement and spending.

The Trust-Technology Paradox

AI is everywhere… customers demand modern, personalised, algorithm-driven experiences, but often distrust the very technology. As the digital payments market hits $20 trillion worldwide, financial institutions will continue to look to AI for fraud detection, user authentication and customer insights. For example, AI on Mastercard’s platforms now protect an average of over 159 billion transactions per year and has increased fraud detection rates by up to 300% while reducing approval delays, proving trust in safety and reliability.

Research exposes a vital paradox: around 64% of consumers want personalised experiences, but only 26% have any trust in organisations to handle their personal data responsibly. Nowhere is this challenge more pronounced than in the BFSI market segment, where 73% of banking customers state that trust matters to them the most when selecting a provider, ahead of product and price. 50% of consumers identify trust as their #1 factor in selecting a financial provider, compared to only 15% of marketing executives prioritising it already today. This ‘trust gap’ risks unravelling the advantages of AI transformation, with 39% of customers switching banks following a poor customer experience.

The Evolving Definition of Trust

Historically, the Banking and Insurance industries represented trust based on continuity and familiarity; a branch manager who knew your family history, an insurance agent walking you through your policy or a financial advisor sitting across the table discussing your finances. As digital-first interactions are becoming prevalent, the measure for trust now comprises transparency, speed, fairness and data protection. Trust has moved from the full relational to a blended performance and principle caricature.
AI’s Dual Impact on Customer Relationships

The benefits of AI automation are obvious: 24/7 availability, decision-making, and few error rates. However, human supervision is still crucial in high-stakes or emotional contexts. Top institutions are adopting a hybrid trust model with machines performing repetitive tasks and humans stepping in when required. For example, AI could quickly pre-approve a claim swiftly, but for complex processes, an advisor would follow up that provide explanation, reassurance and support.

The pursuit of operational efficiency through advanced automation is a compelling driver for AI adoption. AI-enabled customer service, for example, has the potential to lessen resolution times by up to 50% or more, decrease the live agent headcount required for consistent service quality, and significantly boost upsell revenue. However, this drive for efficiency must be meticulously balanced with the enduring need for empathy and personalisation in client interactions. A primary concern for financial institutions is to avoid creating a perception among customers that they are merely being processed through a “faceless system”. This delicate balance is crucial for maintaining and building trust.