Tech Explained: Here’s a simplified explanation of the latest technology update around Tech Explained: Why trust is the currency of the AI-driven economy in Simple Termsand what it means for users..
As artificial intelligence becomes the invisible engine of digital commerce, the most critical question is no longer how fast systems can decide—but how responsibly they can do so. Every tap, click, and background authorization now triggers layers of algorithmic judgment. In milliseconds, machines assess identity, intent, and risk. For consumers, the outcome is simple: approved or declined. For the financial ecosystem, however, those micro-decisions determine something far larger—trust.
In this evolving landscape, the locus of control is shifting. Payments increasingly occur without direct human oversight, embedded seamlessly into apps, subscriptions, marketplaces, and autonomous services. The friction once associated with verification is being engineered away. Yet the paradox is clear: the less visible authentication becomes, the more essential it is that it works—securely, fairly, and predictably.
At the center of this transformation are leaders building the infrastructure that quietly governs these decisions. Among them is Mayank Taneja, Director of Product Management at Visa, who oversees large-scale authentication and transaction decision platforms operating across global markets. His career trajectory—spanning roles at PayPal and Capital One—reflects a consistent focus on scaling AI-driven systems within tightly regulated financial environments.
Taneja’s work sits at a pivotal intersection: where consumer expectations for seamless experiences meet institutional demands for risk control and regulatory compliance. As AI begins to act not merely as a tool but as a proxy for human judgment, the responsibility of those designing these systems intensifies. The future of digital commerce will not be defined solely by speed or automation, but by how effectively its architects safeguard confidence in moments that unfold too quickly for humans to see—yet are too important to get wrong.
What makes this moment in payments and AI fundamentally different from what came before?
For the first time, we’re not just automating tasks—we’re automating judgment at scale. Historically, payment systems relied on static rules, manually tuned thresholds, and retrospective analysis. Those systems worked, but they required constant human intervention as fraud patterns, devices, and consumer behavior evolved.
AI fundamentally changes that dynamic. Modern systems learn continuously, reason probabilistically, and adapt decisions in real time across millions of transactions. That means trust decisions are no longer reactive—they’re anticipatory. The shift is profound because decisions about access, authentication, and money movement now happen in milliseconds, often before a consumer is even aware. When AI operates autonomously at this scale, trust itself becomes the core product, not just an attribute.
How is AI already shaping everyday payment experiences without consumers realizing it?
AI has long played a role in fraud prevention, but its influence today is much broader and deeper. Modern platforms simultaneously evaluate behavioral patterns, device integrity, transaction context, and network-level signals to determine the optimal outcome for each payment.
What’s changed is adaptability. Instead of relying on static rules, these systems continuously learn from outcomes across the network and optimize decisions in real time. This allows platforms to approve more legitimate transactions while still blocking increasingly sophisticated fraud. Consumers don’t see the models—but they feel the impact through fewer false declines, faster checkouts, and more consistent experiences across merchants and channels.
You lead authentication and decision platforms at a global scale. What principles matter most when building systems of this magnitude?
Reliability and integrity are non-negotiable. These platforms must operate consistently across geographies, regulatory regimes, and technology stacks—particularly when they are deployed globally and used by large numbers of financial institutions and merchants in live production environments.
Equally important is decision integrity. AI systems are only as strong as the signals and governance behind them. That means ensuring inputs are accurate, standardized, explainable, and auditable across markets. At scale, even small inconsistencies can cascade quickly. Strong foundations—monitoring, controls, and governance—are what allow AI-driven systems to operate responsibly and sustainably.
You’ve worked across different layers of the financial ecosystem. How has that shaped your approach to leadership?
Working across issuer, wallet, and network environments gives you a system-level understanding of how trust is created and sustained. Issuers are primarily focused on consumer protection, credit risk, and regulatory accountability. Wallets and platforms tend to optimize for speed, usability, and adoption. Networks, on the other hand, have to balance all of these priorities simultaneously—while coordinating across thousands of partners with different incentives, risk tolerances, and regulatory obligations.
That breadth fundamentally shapes how I think about leadership. Instead of optimizing a single component in isolation, the role becomes one of orchestration—aligning incentives across the ecosystem so that improvements in one layer don’t create unintended consequences in another. Decisions have to work not just locally, but end-to-end across the value chain, often at a global scale. Sustainable innovation in financial systems happens when security, performance, and experience are advanced together, and when governance and scale are designed in from the start rather than added later.
Many people worry about AI making decisions about their money. How should platforms address that concern?
Those concerns are valid, especially in financial services where decisions have real and immediate consequences. The solution isn’t less automation—it’s better design. AI-driven systems need to be explainable, reversible, and configurable, so people understand why a decision was made and feel confident they can intervene when needed.
Explainability is critical because it gives users a sense of control, even when decisions are automated. Equally important is reversibility—when systems get something wrong, there must be clear paths to correct outcomes. And configurability matters because different users have different comfort levels with automation, particularly when it comes to money.
High-confidence scenarios should feel seamless and invisible, removing unnecessary friction from everyday transactions. But in moments of ambiguity or uncertainty, systems should slow down, add transparency, or involve the user directly. Trust is earned when AI behaves like a responsible partner—one that supports human intent rather than overriding it.
What makes AI adoption in financial services especially challenging?
The hardest challenges are rarely technical. Success depends on deep organizational alignment across product, engineering, data science, legal, and compliance teams—all of whom play a critical role in deploying AI responsibly. Models must meet performance goals while also satisfying rigorous standards for fairness, security, explainability, and regulatory compliance.
Payments are also inherently multi-party. A single transaction can touch issuers, networks, processors, merchants, and platforms, each with its own constraints and incentives. That means progress depends on ecosystem readiness, not just internal capability. Teams that succeed treat AI as core infrastructure, designed into the system from day one, rather than as an experimental layer added after the fact. Strong data foundations, governance, and cross-functional trust are what ultimately enable AI to scale in production environments.
How has AI changed the role of senior product leadership?
Product leadership has evolved from defining static experiences to setting strategic intent, guardrails, and ethical boundaries for systems that continuously learn and adapt. With AI optimizing execution dynamically, leaders can focus less on micro-decisions and more on long-term resilience, accountability, and system-level outcomes.
The nature of the work has shifted. Instead of asking, “How do we build this feature?” leaders now ask, “How should this system behave over time, under uncertainty, and at a global scale?” That requires judgment, not just delivery. It also requires anticipating second-order effects, aligning stakeholders, and ensuring that optimization never comes at the expense of trust or fairness.
Looking ahead, what will most transform how people interact with money?
We’re moving toward agent-driven commerce, where intelligent systems handle routine financial actions automatically—optimizing payments, managing risk, and embedding transactions directly into everyday life. Much of this will happen in the background, without users needing to actively manage each step.
In that future, people will define intent and constraints—what they care about, what they want to avoid—and systems will operate on their behalf within those boundaries. Human involvement won’t disappear, but it will become more intentional. The platforms that succeed will be those that combine autonomy with accountability, personalization with guardrails, and innovation with trust.