FIs Spend Up to 40% of Analytics Resources Just Cleaning Data. What Are They Missing While They Do?
Every transaction processed, every check deposited, every loan application submitted generates data. Financial institutions sit on one of the richest data assets in any industry. But volume isn’t the same as value and bad data doesn’t just create errors. It creates silence where there should be signal.
The real danger isn’t what goes wrong. It’s what never happens at all.
Bad Data: A Problem FIs Have Learned to Live With
Bad data isn’t a new problem. Most financial institutions have simply normalized working around it — manual reconciliation, patched workflows, siloed systems that don’t talk to each other. The result is a familiar paradox: rich data archives spread across core banking, check processing, lending, and digital channels, but remain disconnected and difficult to access when it actually matters.
The operational cost is real. McKinsey research shows financial institutions spend 30 to 40% of their analytics resources just cleaning and reconciling data, not using it.
And 43% of bank leaders identify data quality and readiness as the primary reason their AI investments aren’t delivering.
That’s typically where the conversation stops. But framing bad data as an operational burden significantly understates the problem.
The Hidden Cost: What Never Happens
The biggest misconception about bad data is that it leads to mistakes. It does, but that’s not where the real cost is.
The real cost is in what never happens.
Consider what it actually takes for AI to work in banking. AI can automate reconciliations and flag errors in real time, but only if your systems know what stage a loan application is in, if teller transaction volumes are accurately tracked, and if your core systems and digital channels are properly integrated. Batched, stale data doesn’t feed intelligence — it feeds lag.
Yet, that’s precisely how many institutions still operate. According to Bank Director’s 2025 Technology Survey, 56% of banks keep data siloed in the system that generates it, and 41% still rely on spreadsheets to manage data used by business lines.
In an era where AI is the top planned technology investment for the first time — cited by 48% of institutions — building AI on that kind of foundation is like building a Ferrari that’s only has a dirt road to travel.
When data is delayed or fragmented, fraud detection becomes reactive rather than preventive…
- A pattern that should have triggered an alert gets flagged after the loss has occurred.
- A declined card transaction becomes a silent frustration rather than a proactive service moment.
- A customer whose transaction behavior signals financial stress never gets a timely nudge.
- A business owner whose cash flow pattern suggests a seasonal credit need gets a generic email three weeks too late.
These signals exist in the data. They’re just invisible when systems aren’t connected. And customers increasingly notice. They no longer compare their banking experience to other banks; they compare it to every digital experience they have.
The Compliance Cost That Can’t Be Absorbed
Bad data isn’t only a missed-opportunity problem. It’s a regulatory exposure and nowhere is that more visible than in fraud prevention.
Most financial institutions run separate fraud detection logic across core banking, digital channels, payment systems, and third-party integrations. Each system operates in its own lane, creating blind spots and inconsistent risk assessments.
Additionally, many financial institutions continue to silo AML/BSA from fraud operations. It is true that fraud teams focus on immediate loss prevention and customer protection while are compliance‑driven, working from suspicion rather than certainty and emphasizing documentation and regulatory defensibility. However, much of the time the two systems are not communicated, leading to gaps within the financial institutions.
Fraudsters have figured this out. Coordinated attacks now move across digital onboarding, instant payments, and core banking simultaneously, exploiting detection tools that were never designed to talk to each other.
Real-time payments make this more urgent, not less. These systems demand split-second decisions, but legacy infrastructure built for batch processing can’t keep pace. Layering point solutions on top adds complexity without adding a unified risk view. The result is increased latency, missed signals, and exposure that carries real consequences: lost revenue, higher operational costs, regulatory scrutiny, and reputational damage that’s hard to recover from.
These patterns can only be identified when data is connected across channels.
The Fix Isn’t Just Cleaner Data — It’s Connected, Timely Intelligence
Solving this requires two things working together: clean data at the point of capture, and systems that connect it across the enterprise in real time.
On the capture side, this is exactly why Kinective acquired OrboGraph earlier this year. OrboGraph’s solutions like OrboAnywhere, powered by OrbNet AI, not only automate check processing and payment validation — they create a wealth of structured, accurate data that feeds everything downstream. With 99%+ read rates and 99.5%+ accuracy, the data entering the system is reliable from the start.
That matters because AI is only as good as the data available to feed it.
On the connectivity side, Kinective’s platform ties it all together — connecting systems across the enterprise so that data stops living in silos and starts flowing to the right person, in the right role, at the right moment.
“This is the true power of our platform: turning data into action that makes banking more intelligent and secure.” said our CEO, Stephen Baker, in a recent press release.
With 88% of financial institutions planning to increase technology spending over the next two years and AI topping the investment list for the first time, the foundation question has never been more urgent. The institutions that win will be the ones whose data is clean enough, connected enough, and timely enough to act on.
