Global Fintech Interview with Ravi Nemalikanti, Chief Product and Technology Officer at Abrigo

Ravi Nemalikanti, Chief Product and Technology Officer at Abrigo chats about the growing AI and fintech connect in this interview:

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Please take us through some of Abrigo’s recent product enhancements? 

Earlier this month, we announced a major expansion of our AI-powered product suite designed to help banks and credit unions move faster, improve accuracy, and stay compliant in a rapidly changing environment. The new capabilities target high-friction areas where financial institutions often struggle with time and resource constraints.

• AskAbrigo: A generative AI knowledge assistant that gives staff instant answers from their institution’s own policies and procedures. Institutions using it report saving up to five hours per week, with the added benefit of consistency and transparency since the original source material is always surfaced.
• Abrigo Lending Assistant: Automates key steps in the lending process by extracting data, drafting editable loan narratives, credit memos, and validating documents against requests. It reduces errors and frees lending staff to focus on higher-impact work.
• Abrigo AML Assistant: An agentic AI capability for anti–money laundering teams. It triages alerts faster, reduces the time spent on false positives, and assembles investigative data into explainable summaries. Analysts testing the tool have noted faster, more meaningful triage and stronger decision and documentation consistency.
• Allowance Narrative Generator: Automatically generates precise, editable narratives to explain and document reserve changes, saving time across reporting periods and improving transparency in allowance documentation.

All enhancements are modular, allowing banks and credit unions to adopt AI at their own pace—starting where it will have the most impact and scaling over time. Our mission is to deliver AI that institutions can trust: secure, explainable, and aligned with their compliance needs. This frees staff to focus on higher-value work and ultimately serve their communities better. 

What challenges do banks and FI’s currently face when adopting genAI solutions to enhance their offering? 

According to a recent MIT study, a majority of AI pilots do not deliver business value. One of the big drivers that drives up the failure rate is the nature of use cases that enterprises choose for their AI pilots. These end up either too wide or too narrow to yield any realistic value. The right use cases are those that are chosen from a very strong and deep understanding of the experience journeys, whether they are internal team members or external customers or members. This process of identifying the right use case to yield the most value is the first and foremost challenge that banks and credit unions face. 

Further, banks and credit unions are balancing heavy workloads, strict regulatory deadlines, and rising customer expectations. While generative AI has the potential to unlock hundreds of billions in value across the industry, adoption isn’t without challenges. Institutions can’t afford ‘black box’ systems; they need AI that’s transparent, explainable, and compliant. At the same time, leaders worry about disrupting day-to-day workflows or overextending resources with large-scale transformations. 

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That’s why we’ve built the Abrigo AI suite, so every product is modular, explainable, and easy to adopt. The real value comes from our deep understanding of where our customers spend the most time and what tasks consume them day to day. With that insight, we can embed AI into the repetitive and deterministic areas, freeing our customers to focus their energy on the work that truly drives impact. 

Institutions can start with high-impact use cases like lending or AML, capture quick wins, and then scale AI at their own pace. Our goal is to deliver agentic AI capabilities that uncover insights, anticipate risks, and ultimately help banks and credit unions create lasting value for their customers and communities. 

Specific to lending and financial crime, how can global financial institutions and banks improve workflows with modern AI tools and tactics? 

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. Take lending as an example. Depending on the size and type of financial institution, a single origination flow might involve anywhere from two people 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 manual. Covenant tracking, managing ticklers, and ensuring compliance deadlines are met are all high-friction, repetitive 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. 

A few thoughts on how GenAI is set to reshape lending and financial crime segments? 

Generative AI is moving these areas from reactive to proactive operations. In lending, we will start to see agentic AI that does more than validate documents. It 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. 

Five of the most innovative AI backed fintech innovations from the global market that have piqued your interest and why? 

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.
• Real-time fraud detection platforms that use streaming data and adaptive models to catch new fraud tactics within hours instead of weeks.
• 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.

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