Global Fintech Interview With Ravi Nemalikanti, Chief Product and Technology Officer at Abrigo: Web-based Banking Models

Ravi Nemalikanti, Chief Product and Technology Officer at Abrigo chats about some of the top threats impacting web-based banking models in this Global Fintech Series interview:

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Take us through your time in tech and tell us about your role at Abrigo?

I am Abrigo’s Chief Product and Technology Officer, where I lead our technology strategy and set product and development priorities to drive innovation and strengthen the company’s competitive advantage. I’m honored to be the recipient of the 2024 Haas Technology Leadership Award for North America from Carlyle, which recognizes exceptional technology leadership.

Before joining Abrigo in 2022, I served as Chief Technology Officer for Digital Banking at NCR Corp., where I led the organization’s digital-first banking technology roadmap. Earlier in my career, I spent 14 years at Thomson Reuters in leadership roles spanning tax and accounting, global trade, and risk management.

I hold a bachelor’s degree in engineering from Andhra University in Andhra Pradesh, India, and an MBA from the University of Chicago Booth School of Business.

What are the core benefits of your latest ACH fraud detection solution, how does it serve end users?

Abrigo Fraud Detection with ACH delivers data-driven protection across both origination and receipt channels, allowing financial institutions to detect fraud in real time the moment an ACH file arrives. By combining configurable rules, behavioral analytics, and third-party verification, we help teams identify ACH fraud earlier and streamline investigations.

The solution is built to support compliance with the Nacha risk management framework, while enabling institutions to reduce losses, improve operational efficiency, and provide stronger protection for their customers. With this solution, institutions can detect ACH fraud in real time upon file arrival, configure rules to align with their unique risk profile, and comply with Nacha requirements with confidence.

What are some of the fraud detection features that most banks today don’t have enough of with their apps and web banking models and what thoughts would you share to help them fix this?

One of the biggest gaps I see in today’s app- and web-based banking models is that fraud detection is still too fragmented and too reactive. Banks have data, but it lives across multiple systems, which makes it hard to see risk clearly and act quickly. Similarly, many institutions don’t have a truly unified view of ACH activity. Ingesting ACH data from multiple systems into a single view dramatically improves detection accuracy and gives fraud teams the context they need to spot risk earlier. Patterns also get missed when solutions rely on static rules. Engines that use behavioral analytics to identify high-risk patterns like mule activity, unusual timing, and other anomalies see patterns immediately, not hours later.

Another major gap is flexibility. Fraud teams need self-service tools to activate best-practice rules or build and configure custom rules aligned to their institution’s unique risk profile. Waiting on technical resources slows decision-making and increases exposure.

Finally, we often see banks often fall short with verification. Integrating external account and customer verification helps confirm transactions sooner and significantly reduces false positives, which improves both efficiency and customer experience. My advice to banks is simple: move toward unified data, real-time intelligence, and configurable, customer-centric workflows. That’s how you get ahead of fraud instead of chasing it.

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Can you talk about some leading AI powered tools and features in fintech from around the world that are serving this purpose well?

Lending and financial crime are where banks feel the most friction, and they’re also the best starting points for AI to make a meaningful impact. Lending, for example, depending on the size and type of financial institution, a single origination flow might involve anywhere from two employees to more than ten across the bank or credit union. When you step back and look at the tasks those teams need to complete, a significant number of them are ideal targets for AI. From extracting data to drafting narratives and flagging discrepancies in documentation, AI can take on repetitive work and reduce errors, freeing relationship managers to focus on customers instead of paperwork.

The opportunity doesn’t end at origination. Loan administration is another area where the workload is heavy and often repetitive and manual. Covenant tracking, managing ticklers, and ensuring compliance deadlines are repetitive and asynchronous activities that AI can streamline. By monitoring documents and borrower performance data in real time, AI can surface exceptions earlier, automate reminders, and reduce the administrative burden on staff while also strengthening oversight.

In financial crime, adaptive models can scan transactions in real time, triage alerts faster, and reduce false positives. That gives analysts back hours of their day to focus on genuine risks instead of being buried in noise. The lesson from my experience is that AI delivers the most value when it augments human judgment rather than attempts to replace it, and when outputs are explainable to regulators, boards, and customers.

What are some of the most exciting fintech innovations you’d like to talk about more in this chat?

I’m seeing several innovations globally that are setting the bar for what’s possible:

One of the biggest challenges in unlocking the full potential of AI is the data fragmentation and legacy systems within the financial institutions. At a platform capability level, the OSI (Open Semantic Interchange) initiative by Snowflake is very interesting and may help accelerate adoption and innovation in the fintech space.

Technology that gives a proactive view into credit needs of a small business by connecting cash flow data, account activity, and external signals like seasonality or market trends. With that visibility, AI can anticipate when additional credit might be needed, surface tailored options, and even streamline the application process so the business has access to capital before the need becomes urgent.

Cash flow–based credit scoring that goes beyond FICO to incorporate richer signals, making credit more accessible to small businesses and underbanked populations.

Explainability engines that generate plain-language narratives for regulators and customers, critical in an industry where trust and accountability are non-negotiable.

Five thoughts around the future of fintech before we wrap up?

Generative AI is moving fintech from reactive to proactive operations. In lending, we will start to see agentic AI that does more than validate documents. Digital “Always-On” agents will anticipate where delays or risks might emerge in the loan pipeline and take action before they cause bottlenecks. That means flagging missing data, drafting follow-up communications, or even coordinating next steps across different teams. The outcome is that loans move faster with fewer handoffs.

In financial crime, AI agents will assemble investigative narratives automatically, connect anomalies across portfolios, and adapt as criminal tactics evolve. Instead of analysts spending hours piecing together the story, they will start with a complete AI-generated view that highlights the riskiest patterns and behaviors. The result is faster decisions, higher accuracy, and stronger compliance, all without increasing staff burden.

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