Designing For Trust In Fintech’s Future

Abhishek Gandotra, VP of Product at Green Dot.

The rise of AI has changed the way financial services operate—but it has also changed how fraud is committed. In 2024 alone, U.S. consumers lost over $12.5 billion to fraud, a 25% increase year over year. Many of these scams were powered by AI-generated deepfakes, spoofed documents and synthetic identities.

This evolution comes just as global regulators raise the bar on risk, compliance and transparency. For fintech product leaders, this presents both a challenge and a responsibility: How do we build innovative, AI-powered solutions that are also safe, compliant and trusted?

Why This Matters To Me

Over the past decade, I’ve led product transformations across AI platforms, fraud prevention systems and core banking infrastructure, most recently as VP of Product at Green Dot. I’ve worked closely with regulators, legal, engineering and risk teams to embed AI into high-stakes environments, from fraud scoring models and banking platforms to compliance-by-design frameworks.

AI, fraud and compliance aren’t abstract topics for me—they’re the core of what I’ve spent my career building. I care deeply about using AI to improve financial access without compromising trust.

AI’s Impact On Financial Fraud

Generative AI has supercharged financial crime. Fraudsters now create synthetic identities with real-seeming credit histories, generate fake documents to pass KYC checks and clone voices to impersonate executives—all at scale. We’ve entered a new era where the barrier to launching sophisticated fraud is low, and the damage can be massive.

But AI is also our most powerful defense. As a leader in the fintech space, I’ve seen financial institutions implement machine learning to detect anomalies across billions of transactions in real time. Behavior-based risk scoring, device fingerprinting and pattern recognition help flag threats while reducing false positives, improving both security and customer experience.

In this arms race, AI is being weaponized by both sides. The difference lies in who applies it responsibly, with oversight and accountability.

The Rising Tide Of Regulation

Around the world, regulators are responding to these threats and to the proliferation of AI in financial services with sweeping new rules:

• DORA (Digital Operational Resilience Act) in the EU requires banks and fintechs to meet strict standards for cyber resilience by 2025, including oversight of third-party tech vendors.

• AML laws are tightening globally, with regulators prioritizing “effective, risk-based” programs. Institutions that fail to meet the bar face fines in the billions, as seen in recent enforcement actions.

• The CFPB has made it clear: AI tools must still comply with existing consumer protection laws. If a chatbot misleads a user or an algorithm discriminates in lending, the company is liable.

• The EU AI Act, passed in 2024, introduces a risk-based framework that subjects high-risk financial AI systems—like credit scoring and fraud detection—to auditability, fairness testing and transparency.

Fintechs, once lightly regulated, are now facing bank-grade scrutiny. And that shift isn’t temporary; it’s the new normal.

Balancing Innovation And Oversight

This landscape demands more than just compliance. It calls for intelligent design choices that balance speed with safety.

In my broader product leadership experience, I’ve learned that the strongest innovations are born not in spite of constraints, but because of them. Here’s what that means in practice:

1. Compliance By Design: Don’t bolt on risk reviews at the end. Embed them into your product life cycle. If you’re building an AI-powered credit tool, make fairness and explainability part of the initial architecture, not an afterthought.

2. Use AI To Govern AI: The same tools that power personalization can help monitor model bias, track fraud signals or trigger alerts for regulatory violations. When used wisely, AI can automate oversight, but human review must remain in the loop.

3. Risk-Based Friction: Not every transaction needs the same level of verification. Tailor controls based on actual risk, so users experience protection without unnecessary roadblocks. The key is to protect without paralyzing.

4. Stay Ahead Of Policy Shifts: Emerging rules like the EU AI Act and CFPB supervision aren’t surprises; they’re signals. Join industry groups, engage with regulators early, and build modular systems that can adapt as the rules evolve.

Ultimately, trust can’t be retrofitted. It must be designed from the ground up.

Designing For Trust: A Call To Action

Fraud will continue to evolve. So will regulation. But what doesn’t change is the need for customer trust. In a digital-first world, trust is your most valuable currency—and the hardest to win back once lost.

Here’s what I challenge our industry to focus on:

• Build for inclusion. Use AI to bring more people into the financial system, not leave them out. Test for bias. Serve the underbanked thoughtfully.

• Make compliance a product advantage. When done right, controls don’t slow you down; they give customers confidence to engage more deeply.

• Be radically transparent. If a bot makes a decision, let customers know. If security friction exists, explain why. Communication builds credibility.

• See risk as a design input. Don’t just ask, “Is this feature cool?” Ask, “Is it safe, fair and resilient?” Innovation that overlooks risk isn’t innovation; it’s exposure.

The fintech companies that succeed next won’t just be the fastest movers. They’ll be the most trusted builders. Let’s use AI, compliance and risk as levers to create financial products that serve more people, more safely—and do it all with integrity.

The future of fintech belongs to those who design for trust.

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