The $1.5B Untapped Market Opportunity for Revenue Intelligence...(Chapter I)

In October 2023, about six months after I started my company, my wife was diagnosed with a scary medical condition. As newcomers to the U.S. healthcare system, it was overwhelming to juggle appointments, insurance details, and the emotional toll of it all. I’m beyond grateful we found a fantastic surgeon—and I’m certain my wife’s positivity played a huge role in her recovery!

Not long after we got the hospital bill, my go-to-market instincts started buzzing. In tech, I’m used to SaaS, PLG, and usage-based pricing—those are just the headlines, but the human interaction, the market dynamics, the math, and the technological trends are what fascinate me. And honestly, every business on the planet has some form of “Revenue Operation,” whether they call it that or not. That realization sent me down a rabbit hole: how do other industries—particularly healthcare and BFSI—achieve operational excellence around revenue?

I ended up chatting with some truly brilliant folks—data experts, AI innovators, operations pros, executive leaders—and walked away with eye-opening insights. This article, co-authored with my friend and collaborator, the one and only Ryan Schrupp (from US Bank), distills those discoveries, focusing on the untapped potential in Banking, Financial Services, and Insurance (BFSI).

The Untapped Opportunity in Banking and Financial Services

The State of Revenue Intelligence

The technology industry has long led the way in Revenue Intelligence (RI) adoption—now entering the era of “Revenue AI”—driven by SaaS business models, structured sales processes, and an obsession with metrics. Companies like Gong, Clari, and Salesforce Revenue Intelligence have transformed how sales teams forecast revenue, optimize deal cycles, and measure pipeline health. However, while the tech sector has reaped clear benefits, BFSI (Banking, Financial Services, and Insurance) remains vastly underdeveloped—yet significantly more lucrative from a revenue perspective.

At its core, Revenue Intelligence is about turning go-to-market execution into a data-driven science. But here’s the key insight: while tech companies focus on conversion rates and quarterly growth, BFSI faces high-stakes, complex revenue models that rely heavily on forecasting and optimization. Historically, these institutions have lacked the infrastructure and mindset to deploy robust Revenue Intelligence capabilities at scale. That is rapidly changing. As AI-driven analytics, automation, and predictive modeling advance, more banks see the need to unify massive volumes of siloed data—spanning credit, deposits, payments, and more—into actionable intelligence that can help them optimize the balance sheet and better serve their clients.

Key Metric: Global spending on AI in BFSI is projected to grow from ~$22.5B in 2022 to over $368B by 2032 (Allied Market Research), underscoring how much budget is shifting toward advanced analytics and revenue-focused solutions.

Why BFSI Is Ripe for Revenue Intelligence

Optimizing Product Adoption and the Balance Sheet

In banking and insurance, revenue isn’t just about closing new deals. It’s about optimizing product adoption, cross-selling, pricing risk effectively, forecasting deposit and credit demand, and ultimately balancing revenues against costs to maximize returns. A critical activity is managing the balance sheet—especially in a dynamic rate environment where customers have become highly rate-sensitive. They want the lowest possible rate on loans and the highest rate on deposits. Meanwhile, banks strive to push profitable products, such as payments or treasury solutions, that can offset the margin pressure on deposit and credit products.

Key Metric: Retail and commercial banking alone surpasses $3T in global revenue (McKinsey), meaning even a small percentage improvement via data-driven cross-selling could yield billions in incremental revenue.

Relationship returns (ROE) have therefore become a top focus. An accurate forecast of a relationship’s revenue is crucial for calculating its return on equity. Once a bank understands the revenue potential and costs associated with a client or portfolio, it can decide whether to reprice certain products, cross-sell profitable offerings, or—if necessary—exit a relationship that is eroding value. This level of precision requires next-level forecasting and data-driven decision-making that traditional BI reports simply cannot deliver.

From Onboarding Challenges to Client Retention

Many banks still rely on outdated, macro-driven forecasting models rather than granular, behaviorally predictive methods. Churn and attrition present huge risks; however, predicting them accurately is extraordinarily difficult. Beyond churn, some customers may start to disengage early in their onboarding journey—perhaps they fail to ramp up usage of a product, underutilize a line of credit, or keep significantly lower balances than expected. Revenue Intelligence can flag these signals in near real-time, prompting bankers to intervene with new solutions or support.

Equally important is identifying opportunities. Clients who expand internationally, face higher fraud risks, or show early indicators of growth may be prime candidates for additional services—global treasury, advanced payments, or specialized lending products. With a robust data foundation and AI-driven pattern detection, banks can proactively address client needs instead of waiting for them to either ask or—worse—drift away to a competitor.

Key Metric: In a survey of corporate and commercial banks, 100% reported accelerating adoption of advanced analytics for front-line revenue generation in the last two years (McKinsey), reflecting a growing urgency to leverage data.

The Cost of Inaction

Challenger banks, fintech disruptors, and digital-first insurers have already taken major steps to harness advanced analytics and machine learning for revenue growth. Traditional financial institutions that fail to modernize face a clear disadvantage: their competitors can dynamically reprice deposits or loans as the market shifts, while also personalizing cross-sell offers with pinpoint accuracy. In short, data-rich, insight-poor institutions risk losing profitable clients and missing out on margin opportunities if they don’t move swiftly.

Leading banks are now investing heavily in developing first- and third-party data signals across their product lines to better forecast client needs. Their focus is less about credit alone—since many institutions can no longer “lead with credit”—and more about identifying revenue gaps at a client level, recommending relevant product actions, and ensuring the timing aligns with the customer’s situation. This holistic approach to relationship management is pushing bankers to rely on integrated data and insight, rather than gut feelings.

Key Metric: Venture capital funding in the broader revenue and sales intelligence market nearly tripled from ~$321M to over $950M between 2020 and 2021 (People.ai), suggesting robust demand for next-generation data platforms.

Competitive Themes Driving Adoption

1. Data Fragmentation and Underdevelopment

Banks sit on mountains of internal data—ranging from daily transactions to lengthy onboarding details—but this data often remains siloed and underdeveloped. Legacy systems make data extraction and transformation costly and time-consuming. Transaction-level data can be particularly challenging to aggregate at scale, especially for lines of business like payments. Revenue Intelligence thrives on unifying these disparate data streams, offering real-time alerts and actionable recommendations that bankers can use without sifting through endless BI reports.

Key Metric: Approximately 40% of organizations cite data quality and siloed systems as a primary barrier to advanced analytics adoption (SNS Insider), illustrating why BFSI struggles to implement revenue intelligence effectively.

2. Regulatory Environment as Catalyst

While compliance often slows innovation, many banks are finding that strong data governance and advanced analytics can actually get them ahead of potential regulatory issues. By proactively detecting unusual financial patterns, suspicious account activity, or product onboarding bottlenecks, banks can address problems before regulators demand change. Though some banks haven’t been forced by regulators to adopt Revenue Intelligence, it’s increasingly seen as a strategic shield—if you know your data, you can control your risk.

3. Efficiency Pressures and Drowning in Data

With margins tightening, leaders are demanding higher productivity from bankers. Relationship managers are juggling larger portfolios, more complex client needs, and a constant requirement to cross-sell across multiple product groups. They’re drowning in data—often confronted with 15–20 separate BI reports that they must interpret and act upon.

Revenue Intelligence can solve this information overload by delivering “best next actions” tailored to each client. For instance, a data-driven system might detect that deposit balances are drifting, or a client’s payments volume is spiking, suggesting an upsell opportunity for treasury services. Instead of requiring bankers to discover these insights manually, the platform surfaces them automatically, enabling more effective relationship management.

Key Metric: Nearly 75% of front-line teams spend up to a third of their day gathering data across reports rather than acting on it (Accenture), showing a massive efficiency gap that revenue intelligence can address.

4. Dynamic Pricing, Deposit Strategies, and More

Whereas some banks once pinned their growth hopes on loan origination, the landscape has shifted. In a volatile rate environment, deposit pricing has become a top priority. Quick repricing after a Federal Reserve rate move can protect a bank’s margin and retain clients—especially when paired with strategic cross-sells. Similarly, signals that a customer is moving funds elsewhere can trigger proactive outreach.

AI-driven anomaly detection can also reveal if a competitor has begun offering more attractive rates, or if the client’s transaction patterns imply they’re about to expand into a new region. These insights let banks offer customized deposit products or transaction services to keep and grow the relationship. This level of proactive strategy is the future of Revenue Intelligence—shifting from reactive data dashboards to immediate, actionable, and highly contextual guidance.

The Changing Decision-Making Unit (DMU)

Adopting Revenue Intelligence in banking requires a cross-functional buying group that combines input from finance, product, relationship management, risk, and data science teams. CFOs want to see how these tools impact profitability and ROE. Heads of Lending and Risk Officers need assurance that data-driven forecasts align with the bank’s credit policies. Product and Relationship Managers hold critical domain expertise, ensuring the platform addresses real-world scenarios. And Data Science teams validate the solution’s technical feasibility and integration with existing infrastructure.

In a large financial institution, the sheer number of stakeholders can slow decision-making. But as the need for accurate forecasting and actionable insights becomes more urgent, banks are establishing centralized analytics groups or revenue strategy teams that champion these solutions. Once they demonstrate tangible returns—like improved deposit retention or new cross-sell conversions—buy-in from senior leadership follows.

Key Metric: About 55% of large banks have created a Chief Data or Chief Analytics Officer role in the last three years (Deloitte), reflecting increased executive-level focus on data-driven revenue strategies.

The Window of Opportunity

Over the next three years, the BFSI sector will decide which vendors can adapt Revenue Intelligence beyond tech and fully integrate it into the core operational fabric of banking. The central question is: Will incumbent institutions build in-house solutions, or will they partner with AI-driven vendors that can accelerate time to value?

For solution providers, the challenge is twofold: first, educate the market on the necessity of advanced Revenue Intelligence—especially around balance sheet optimization and deposit strategies. Second, prove immediate financial impact in pilots or limited rollouts. Banks are inherently cautious, so demonstrating fast ROI in a specific area (such as deposit repricing) can unlock broader adoption.

Ultimately, the real disruption lies in applying the same data-first principles that revolutionized tech sales to banking’s relationship-based revenue model. This isn’t just about adding yet another analytics dashboard. It’s about delivering actionable intelligence at scale, so bankers can respond quickly to market shifts, outmaneuver rate-chasing customers, and maximize each client’s ROE. That’s the defining advantage of modern Revenue Intelligence, and it’s poised to reshape BFSI for the next decade.