.png)
And why verified, first-party data is the only reliable foundation for financial industry intelligence.
By Jay Rosenblatt, CEO of Alpharank
There is a pattern that plays out every day across boardrooms and strategy teams: someone asks a generative AI platform (Claude, ChatGPT, Gemini, Grok, etc.) a business question. The platform responds quickly, confidently, and in complete sentences. The answer looks authoritative. However, it often isn't.
I want to address this gap directly, as my team and I experience it almost daily.
The experiment that made the problem undeniable
We recently asked Claude to identify the loan origination systems (LOS), account opening platforms, and mortgage systems used by credit unions in the $100M-$25B asset range, and by the top nonbank mortgage lenders in the country.
This is a straightforward prompt, and the output looked impressive. Hundreds of institutions. Clean columns. Confident vendor assignments. Color-coded spreadsheets.
It was indeed, though substantially wrong.
Credit unions were assigned Symitar/Jack Henry across the board simply because Symitar is the most common core processor in the industry, a plausible-sounding default, not a verified fact. When we spot-checked against institutions whose actual systems we could verify, the errors were immediate and consistent. Six spot-checks, six misses, and all presented with equal confidence.
When we pushed back, the AI even admitted it directly:
"I essentially guessed, and I shouldn't have. I don't actually have confirmed knowledge of which specific vendors they use. I applied it as a default to hundreds of companies without confirmed knowledge."
The system acknowledged the problem only after being challenged by someone, myself, the CEO of a tech company, who already knew the correct answers. For anyone who didn't and that is most users, a lot of the time, the fabricated data would have gone straight into a strategy deck, a sales target list, or a competitive analysis.
Why hallucination is a structural problem, not a fixable bug
Generative AI systems are trained on vast amounts of written text. They learn patterns about how language works and how concepts relate. When you ask a question, they generate the most plausible-sounding response, but they are, at their core, pattern-matching engines.
"Plausible-sounding" and "accurate" are not the same thing.
When a model lacks reliable data on a specific question, it doesn't say "I don't know." It generates a reasonable-sounding answer from adjacent patterns. This phenomenon, hallucination, is not something that will be patched. It is inherent to how these systems produce language, and it is most dangerous in exactly the domains financial services professionals rely on most: specific vendor relationships, technology stacks, contract details, and data that changes frequently.
The deeper problem is that AI delivers wrong answers with the same tone and formatting as correct ones. A human analyst who is guessing signals uncertainty. A language model does not. A spreadsheet with 233 rows and color-coded vendor columns looks like research, even when no one actually checked.
For a financial institution building a market analysis, providing value to real prospects by technology stack, or sizing an addressable market, it is not a minor inconvenience. It is the kind of mistake that costs real money and real credibility.
What reliable intelligence actually requires
The answer is not to stop using AI as these tools are genuinely useful for drafting, summarizing, and frameworks. The answer is to be rigorous about where AI-generated content ends and verified data begins.
Reliable intelligence in financial services requires first-party data collected directly and systematically, not inferred. It requires continuous updating, because technology decisions, mergers, and platform changes make last year's dataset unreliable today. And it requires transparent sourcing: if you can't answer "how do we know this?" you shouldn't be building strategy on it.
What Alpharank brings to this problem
Alpharank was built specifically for this. While general-purpose AI platforms hallucinate because they lack verified sources, Alpharank operates on a proprietary first-party data infrastructure with more than 7 billion transactions and verified behavioral and transactional data that reflect how consumers and businesses actually interact with financial institutions. More than 200 financial institutions trust Alpharank's platform to drive their strategy, competitive positioning, growth, and marketing decisions.
When an AI platform is asked which technology vendor a specific credit union uses, it has no reliable source, so it guesses. When Alpharank is asked, the answer comes from data that has been systematically collected, validated, and maintained. The confidence is earned, not generated.
That difference translates directly into better decisions: competitive intelligence based on what is actually true, technology stack visibility that enables precise targeting and benchmarking, and consumer insights drawn from billions of real transactions, not pattern-matched approximations.
When the decisions are consequential, the data has to be real and unbiased.
Jay Rosenblatt is the CEO of Alpharank.
About Alpharank:
Over 200 banks and credit unions trust Alpharank to optimize production from their websites and online applications, resulting in dollars booked to the balance sheet. Without storing personal information, data models and benchmarks are trained on a unique dataset of +7 billion full-funnel events from “click” to “funded” with known balance-sheet outcomes!
Learn more about how you win with better click intent, journey efficiency and effectiveness, prospect quality, and campaign optimization. Most financial institutions buy clicks from Google, Meta, etc., and just hope for funded applications. We all know hope is not a strategy. Alpharank measures the quality of your clicks so you only buy the good stuff. Stop their algorithm from draining your budget while sending you lookers, not bookers. Pay for performance only and get 50% more for your marketing dollar.
Contact us for a free evaluation of your paid media campaigns and a complimentary 3-step improvement plan tailored to your balance sheet strategy.