Building AI for Community Banking: An Interview with Anubhav Pradhan, Co-Founder of Shiftmate

Community banks and credit unions sit at the intersection of trust, regulation, and deeply entrenched technology — an environment where generic fintech answers rarely work. Drawing on hands-on experience building and scaling enterprise software for financial institutions, Anubhav Pradhan has spent years translating fragmented systems and regulatory constraints into products that teams can actually use. In this conversation, the Co-Founder and Chief Product Officer of Shiftmate reflects on the operational realities of community banking, the origins of the platform, and what it takes to build AI-native infrastructure for a highly regulated, relationship-driven sector.

Anubhav, to begin with, could you tell us about the specifics of the community banking market and why its players needed a separate answer for running their business in the first place?

Community banking — and especially credit unions — operates on a fundamentally diverse model than large banks, yet for years the software available failed to reflect that reality. Credit unions are member-owned cooperatives focused on serving their communities rather than maximizing shareholder value. Community banks share this ethos: they are relationship-driven, locally embedded, and compete on trust and context, with success measured in member outcomes and relationship depth. The challenge wasn’t a lack of intent or capability, but a mismatch between their operational reality and the tools available.

What they needed was a purpose-built answer — integrated from the begin, relationship-first in design, and aligned with their mission. The goal isn’t simply to trade more products, but to identify genuine needs, improve financial wellness, and support staff as trusted advisors. That gap created the opportunity for Shiftmate.

How did the idea for this product — and the business overall — come to you? 

This wasn’t something we stumbled into. All three of us had been working in this space for years and kept encountering the identical difficulty from diverse angles. Over time, a clear pattern emerged: community banks and credit unions were investing in technology, but the results weren’t showing up in daily workflows, staff productivity, or member engagement. The frustration wasn’t a lack of strategy or intent — it was structural. There was no single lightning-bolt moment — just repeated exposure to the identical gap until it became clear that a better, purpose-built answer for community institutions needed to exist.

How did you and your co-founders work together to turn that insight into a real product – and what did you personally lead?

The three of us came at the difficulty from diverse angles, but we were aligned ahead that the gap was structural: data, workflows, and execution were fragmented across too many systems. My lane was product and platform design – taking what we were hearing in the market and turning it into a cohesive thesis, a buildable architecture, and something teams could actually adopt.

In practice, I led the translation from “this is the difficulty” to “this is the platform.” I worked closely with my co-founders and engineering to define the end-to-end system – the CDP and integration layer, the controls/auditability model, and the agentic workflows and Co-Pilot interface that keep humans in the loop. In parallel, I partnered with the team on outcome-first packaging and go-to-market alignment – shaping pricing and positioning so what we built matched how credit unions actually operate and grow. The work was highly collaborative, but my responsibility was owning the product direction and making sure the platform design, delivery model, and commercial story stayed coherent as we executed.

What is Shiftmate, and what is the core idea behind your patented technology? 

Shiftmate is an AI-native growth platform built specifically for credit unions and community banks. We describe it as the “growth brain” that sits above the existing technology stack and turns strategy into execution. Here’s the difficulty we’re solving: most institutions have clear growth goals — growing loans, deepening card relationships, improving retention — but execution breaks down. Signals, workflows, and measurement are fragmented across core banking, digital channels, loan origination, card platforms, CRM, and marketing tools, with no single layer orchestrating them. As a result, growth initiatives either don’t launch consistently or break down invisibly once they do.

And how exactly does it assist credit unions and community banks compete with large financial institutions?

Shiftmate sits above that stack as the execution layer.

First, we uncover unmet needs in real time by unifying fragmented data across systems — core, digital banking, cards, lending, CRM, and external accounts — into a living customer profile. We apply intelligence to identify who is likely to refinance, who is underfunded and ready for a loan, who is ready for a card, and who is at risk of churn — not generic “next product” suggestions, but genuine needs based on a full financial picture.

Second, we execute growth plays consistently by routing the right action to the right surface — staff workflows, digital banking prompts, personalized marketing journeys, or proactive outreach — with policy controls and human oversight built in. Third, we track execution end to end, making visible the drop-offs that undermine growth, from application abandonment to activation failures, so teams can intervene and improve outcomes.

This isn’t about outspending large banks. It’s about giving community institutions comparable execution capability within their constraints, enabling them to operationalize trust, relationships, and deep member understanding at scale. Shiftmate assists them act proactively, close execution gaps, and consistently operate as trusted advisors — becoming better at being community institutions, not larger ones.

How was the architecture of Shiftmate born — the combination of a CDP, an API gateway, agentic workflows, and a Co-Pilot interface? What product and technical principles did you lay down from the very beginning?

The architecture was designed by working backward from a clear reality: in this market, growth is a data and execution difficulty, not a UI difficulty.

We defined three requirements that had to work together. First, data must be unified. Community institutions have data spread across core , digital channels, cards, lending, CRM, marketing tools, and external accounts. The CDP layer normalizes this data into a single, living customer profile that serves as the source of reality. Second, the platform must make sense of the data. Transactions alone don’t indicate readiness to refinance or churn risk. The intelligence and rules layer applies models, policy logic, and compliance-aware rules to determine what action makes sense, for whom, and when. Third, the system must act, not just report. Agentic workflows execute outreach, staff tasks, and digital prompts, while the Co-Pilot interface keeps humans in the loop for approval, override, and accountability.

An API gateway ties everything together as the control plane, governing data flow, security, isolation, and auditability. From the begin, the platform was built to be API-native, interoperable with existing stacks, strongly isolated across tenants, outcome-driven (“sense → decide → act”), AI-native with human control, and secure and auditable by design.

Can you tell in more details which domain-specific factors did you take into account in Shiftmate?

Sure, there were several of them:

• Integration at enterprise scale is the core challenge: We integrate fragmented data across modern APIs and legacy systems to create a unified, customer-centric view.
• Security is foundational, not a feature: Multi-layer security and cryptographic tenant isolation are built in across the entire stack.
• Platform flexibility with out-of-the-box answers: Pre-configured integrations and workflows deliver day-one value while adapting to institutional policies.
• Low upkeep for small IT teams: The platform is designed for minimal maintenance, with self-healing integrations and non-technical workflows.
• Legacy systems define the playing field: We work with decades-old core systems and existing vendor ecosystems rather than forcing replacement.
• diverse optimization objectives: Success is measured in relationship depth, financial wellness, and trust, not acquisition or conversion.
• Trust and explainability are non-negotiable: Human-in-the-loop controls, transparent reasoning, and full audit trails ensure defensibility.
• Compliance built into workflows: Fair lending, UDAAP, privacy, and BSA/AML are embedded directly into platform logic.

Given that the financial industry requires strict security and audit, how do you find a balance between the speed of innovation and compliance?

We don’t view speed and security as competing priorities. In financial services, security defines what “quick” actually means.

Our principle is simple: if something isn’t secure and auditable, it doesn’t ship. We build security and compliance into difficulty definition and system design, not as a final checklist. Guardrails like feature flags, limited pilots, and human approvals allow secure experimentation. By investing heavily in foundations ahead — identity, network isolation, audit logging — we move quicker over time without creating security debt. Transparency also matters. We work closely with customer compliance teams and are explicit about how data is handled, logged, and controlled. That openness builds trust and accelerates adoption.

You have been building products in the financial sector for many years. What has been the most valuable insight for you about how technology can transform traditional industries?

The most valuable insight I’ve gained is that technology transforms conservative industries by amplifying what already works, rather than trying to replace it. Community banks and credit unions aren’t behind because they lack understanding of technology; they are constrained by regulation, risk, legacy systems, and limited resources. At the identical time, they have real strengths — trust, relationships, and local expertise. From my experience building products in this space, real transformation happens when technology meets institutions where they are, respects those constraints, and builds on existing strengths. When designed this way, better data creates better context, which leads to better decisions and more consistent follow-through. It’s that compounding effect, over time, that drives lasting change.

How do you view the evolution of AI in financial services over the next 3–5 years? What will be the next milestone for Shiftmate?

I  try not to over-predict where AI will be in three to five years, because this space is evolving too rapidly. What I do know is that we’re building at a uniquely powerful moment. For the first time, has genuinely leveled the playing field for community banks and credit unions, allowing them to use technology designed for their reality rather than adapting tools built for large financial institutions. What’s changed is that these institutions can now operate from their strengths. They already have deep trust in their communities and staff with real expertise; AI allows them to scale that trust and insight in ways that weren’t possible before. Our focus is building technology that amplifies those strengths — surfacing genuine needs, supporting staff as trusted advisors, and delivering measurable impact in the form of stronger relationships, better outcomes, and sustainable growth. The goal isn’t to turn community institutions into large banks, but to assist them be better at what they were founded to do, consistently and at scale.