How to scale trust in BFSI industry

Met Mehul Vora from Mirae Asset Sharekhan and learnt how he’s re-architecting first principles inside a BFSI environment. 1. AI in SDLC — Speed without breaking trust Most AI-in-dev conversations chase velocity. His lens was different: velocity under compliance constraints. AI copilots are being deployed across coding, testing, and review layers—but within controlled sandboxes Code suggestions are not blindly accepted; they pass through policy-aware validation layers Sensitive financial logic is segmented, ensuring models never train on or leak critical data Core shift: From “Can AI write code faster?” → “Can AI write code that regulators won’t question?” 2. Form Digitalisation → Straight Through Processing (STP) — Killing friction at source Forms in BFSI are not just UX problems—they are revenue latency points. Moving from static forms → context-aware digital workflows Data captured once, reused across systems → no duplication loops Backend orchestration ensures zero human intervention for standard cases Core shift: From “Digitising forms” → “Designing systems where forms disappear into flows” 3. Agentic AI — From inputs to intent This is where it gets structural. Instead of rule-based workflows, systems are evolving to intent-detection layers Agentic AI interprets why a user is acting, not just what they input Downstream processes dynamically adjust—reducing exceptions, escalations, and manual overrides Core shift: From “If X, then Y” → “Understand intent, then decide path” What ties all three together It’s not AI adoption. It’s decision compression inside regulated systems. Faster code → without governance risk Faster onboarding → without operational drag Smarter workflows → without human dependency Most BFSI systems are designed for control. What leaders like Mehul are doing is redesigning them for controlled intelligence.

One of us built a company that manages $700billion on a foundation of trust. The other interviews billionaires for Forbes to find out exactly how they did it. Peter Mallouk and Jodie Cook sat down for a chat. Here's what came out: Context: AI can create everything now, and AI is making average businesses look great. Your one differentiator is trust. Build it and keep it, create an empire. Lose it, start from zero. How to build (and not lose) trust: 1. Publish your thinking Don't write books for the royalties. Don't write books for some ego reason. Write books because then your thinking is published and everyone knows what you stand for. They know you're consistent. 2. Tell them everything A client should find out everything they need to know from you and not somewhere else you've said it on the internet. Be a complete open book. They pay you to implement. 3. Notice the details Spell your client's kid's name wrong, and it undermines the trust you've built. Double check everything. Create a consistent experience. Like a restaurant with multiple venues, clients come to expect excellence. 4. Be one person Don't be one person with your clients, a different person with your team, and another behind closed doors. Join the different parts of your personality together. Be more you, no matter who is there. 5. Practice active listening A waiter takes an order from a table of five and doesn't write anything down. Creates unnecessary anxiety before the food arrives. Do the opposite. Active listening, making notes, repeating stuff back. Clients value that. 6. Guard the doors Peter still personally interviews everyone. He vetoes 10% of the people his team has already approved. A bad apple infects the whole bunch. Don't fully delegate recruitment, it's your name above the door. He's been doing this at the same firm since 2004. The trust game never stops. The trust tap never has to turn off. How do you create trust?

Research shows that majority of the enterprise customers require SOC 2 reports before signing contracts, yet, approximately only 30% deals successfully go through without any delays or non-compliance issues. An enterprise gifting platform struggled with lengthy security questionnaires, adding 2-3 weeks to their sales cycles. After securing a SOC 2 report, that timeframe dropped to just a day or two. A fast-growing SaaS firm lost two major clients because they lacked a SOC 2 report. Their competitors, with attestation in place, won multi-year deals despite having similar tech. In such cases TRUST, not tech, tips the scale 1. Trust = Revenue Attestation isn’t just compliance; it’s an essential due diligence element for BFSI and global clients. 2. Faster Sales Cycles With a SOC/Attestation report, RFP wins accelerate because security questionnaires shrink dramatically 3. Investor Confidence Early-stage startups with SOC 2 see higher valuations in funding rounds From where I sit, the companies that move to SOC attestation don’t just tick a compliance box. They: •Build confidence with enterprise buyers faster •Shorten procurement cycles •Strengthen their brand as a trusted partner And yes, they win more deals. A SOC report isn’t just an artifact, it’s your passport to larger markets If you are a service provider thinking about scaling, this is the right time to ask: Will the absence of a SOC report cost us our next big client? Happy to share insights from the market and our recent work helping organizations navigate their attestation journey.

As someone who is sitting on the other side of the table evaluating countless GenAI and AI agent product and pitches into banks and capital markets, I feel it’s important to share a perspective founders rarely hear but desperately need if they want to win in BFSI. When we evaluate a solution, we’re not looking at model architecture diagrams or clever orchestration frameworks. Those may impress engineers, but in financial services, the real questions are: Does this reduce audit hours or fraud backlogs? Does it cut servicing costs or compliance overhead? Does it fit into our regulatory environment without adding new risk? Can it generate measurable revenue impact, not just “innovation value”? Founders who win understand that BFSI is unlike other industries. Budgets live in cost centers, compliance, risk, ops not in “innovation labs.” Controls are not optional; they are the entry ticket. And “easy integration” means nothing unless you’ve fought through data silos, SSO across 500+ apps, and regulators asking if your tool leaks PII. This is why I'm sharing this playbook with founders: because too many great products die in sandbox purgatory simply by mispositioning. If you want scale, you must align to cost, compliance, and strategy. You must design for scale economies, switching costs, and regulator-ready trust. The BFSI segment is one of the hardest but also one of the most rewarding markets. Get it right, and you don’t just land pilots, you become infrastructure. https://lnkd.in/em-DAAUS

Recently, a CIO from insurance company reached out to me, trying to solve the problem of raining questions about AI like “AI is here to take our jobs”, “We won’t use it”, “You’re just training it so you can replace us” Sound familiar? It’s funny because 71% of BFSI CIOs are ramping up generative AI use to improve employee productivity but over 56% of them fail because of low adoption. Employee concerns about job security, skill gaps, and ethical implications can significantly impede AI adoption and effectiveness. Here’s a Strategic Approach to harness AI's full potential & put focus on your teams: ⭐ Transparent Communication: Address AI's role openly, emphasizing augmentation over replacement. ⭐Comprehensive Education: Implement training programs covering AI basics, specific applications, and ethical considerations. ⭐Skill Development: Identify and bridge gaps in AI tool proficiency. Alternatively, find tools that have low or zero learning curve and no-code to encourage employees to try it out. ⭐Ethical Framework: Develop and promote AI ethics guidelines to ensure responsible implementation. Make it available to all teams to review and comment on. ⭐Trust Building: Create feedback mechanisms for employees to contribute to AI development and deployment. ⭐Leadership by Example: Actively engage with AI initiatives, aligning them with organizational goals. With this people-centric approach, I was able to work with CIOs drive almost 100% AI adoption for our use case with Alltius in BFSI companies. This not only addresses immediate concerns but also positions our organizations for long-term success in the AI-driven future of finance. What strategies are you employing to prepare your team for AI integration?

AI at scale is a trust challenge, and that’s the biggest opportunity for every enterprise leader right now. As we partner with our customers, we see a clear shift: the conversation has moved beyond “AI for efficiency” and “responsible AI” to one of tangible, measurable value. The key to unlocking that value is trust. For our most regulated customers in healthcare, finance, and government, trust is not a “nice-to-have,” it’s a gatekeeper. These industries need to prove AI agents operate exactly as intended, protecting sensitive data and respecting jurisdictional rules from Day One. The reality is, a pilot program is only a pilot until it is built to scale. And what separates a successful pilot from a transformative enterprise solution is the foundation of trust. The governance layer has to be designed within the solution from the start, weaving together these three critical enablers: ☑️ Verification: You must be able to prove your AI agent is operating within approved business policies. This is not a technical feature; it is a way to ensure accountability and speed up time-to-market in a regulated world. ☑️ Sovereignty: Your sensitive data must be protected and observable in its rightful jurisdiction. This gives customers the peace of mind they need to truly innovate with their most valuable asset – their data. ☑️ Auditability: You must confidently demonstrate how a decision was made through transparent records that empower human oversight. This allows enterprises to stand behind their AI investments with certainty. When these three pillars converge, they remove the barriers that slow down progress and allow customers to move faster, smarter, and with greater confidence. This is where real business value emerges. Performance plateaus when trust is an afterthought. But when trust, structure, and impact advance together, we lay the foundation for true business transformation. #AIGovernance #GoogleCloud #DataSovereignty #AgentVerification

Trust but verify. Sounds simple, but so difficult to do at scale across multiple facilities. Trust is earned. Verification should be systematic. Clear standards and expectations. Training to enable leaders to meet these standards. Dashboards to verify that expectations are being met. Then get into the field and walk in their world. What's going well? What's holding them back? Where are they falling short, why? Where are they crushing it, and why? Then, and here's the key: Hold them accountable. Identify areas that fall short or need improvement and coach them. Put in clear expectations and timelines to measure improvements in these areas. Then follow up. They will respect the accountability, and it will create more trust, two-way trust. If a local leaders is crushing it, share it across your platform. This lets others know they are seen...and recognized. This also increases two-way trust.

Community banks control 57%+ of deposits in nearly 2,000 U.S. counties. Most people think community banks are losing ground everywhere. The data proves otherwise in rural America: This is structural market dominance across a significant portion of the country due to one word... Trust. You have built trust by playing the long game, investing in the relationship. When customers buy from you based on a complete value proposition, the difference is tangible. • Trusted customers bring 3-5x more deposits than rate-shopping customers. • They hold those deposits 40% longer on average. • Your cost of funds stays structurally lower without giving away margin across your entire book. The problem is that most banks treat trust as something they inherit, not something they actively scale. Traditional marketing feels like it betrays the relationship model. Billboards advertising rates feel desperate. Digital ads targeting demographics feel impersonal. So, most community banks under-invest entirely, relying on word-of-mouth. This cedes ground to competitors. Larger banks are actively marketing in these same markets, using sophisticated data to identify and target the exact households you've historically served. The Opportunity: Scale Trust with Data Data-driven marketing doesn't replace relationship banking; it scales it. You have the relationship. Data gives you the precision. Your transaction data reveals which households maintain significant balances at competing institutions. Instead of rate-bombing your entire market, you target those specific high-value households with relevant offers. For example, a farmer who uses you for equipment loans but banks elsewhere for operating capital is a known entity. Data helps you earn the rest of their business. Cross-sell becomes predictive rather than reactive. Retention campaigns focus on your most valuable relationships before they start shopping for alternatives. The result is a measurable balance sheet impact that reinforces trust, rather than compromising it. We’ve spent years solving this execution challenge for community banks. Our clients have generated $26 billion in balance sheet growth by treating marketing as a measurable balance sheet driver, not a brand exercise. The difference is our pay-for-performance model - we only get paid when actual accounts and balances are delivered. One client grew deposits by $497M with 87 basis points better cost of funds than their benchmark. Community banks already own local America's trust. The strategic question is whether you will use modern tools to defend and grow your rural dominance, or cede ground to larger competitors who are investing heavily in your markets. If you are leading a community bank with a strong rural presence and want to discuss how to defend and grow your deposit base in these markets, reach out to me. I will show you exactly how we are helping banks turn trust into measurable balance sheet impact.

Bank of America just deployed AI agents in actual banking roles. JPMorgan is tracking every employee's AI usage. And most BFSI companies? Still stuck debating whether to allow ChatGPT on company laptops. Here's what I've learned building compliance automation for financial services at AIxpertz.ai: The gap isn't about technology. It's about trust architecture. When we built our first KYC document review agent for a mid-size NBFC, the model accuracy was 94% on day one. Impressive on paper. But it took us 3 more months to ship. Why? Because the compliance team needed: Explainable decision trails for every flag Human-in-the-loop escalation paths that actually worked under load Audit logs that satisfied RBI's inspection framework Fallback routing when the agent's confidence dropped below threshold The 94% accuracy was table stakes. The trust infrastructure was the real product. What Bank of America understands (and most enterprises don't) is that deploying AI agents in regulated environments isn't an AI problem. It's a governance engineering problem. The agent is 20% of the work. The guardrails, audit trails, and escalation logic are 80%. We've seen this pattern repeat across 4 BFSI deployments now. The companies that ship fastest aren't the ones with the best models. They're the ones that build trust infrastructure first. What's the biggest blocker you've seen in deploying AI in regulated industries? #AgenticAI #BFSI #ComplianceAutomation #RegTech #AIArchitecture