From Hours to Minutes: How a Global Payments Leader Is Transforming Fraud Investigation with PromptQL

A Fortune 500 financial services company (operating across commercial payments, cross-border transactions, and payment solutions across multiple continents) came to PromptQL with a problem.

A problem that most fraud ops teams know intimately: too many alerts, not enough analysts, and a manual investigation process that couldn't scale.

Their team processes more than 1,000 cases a day. Each one requires an analyst to manually work through 80+ investigative questions across authentication logs, transaction systems, and account records, pulling from over a dozen data sources, one at a time.

Investigation time: hours, when it needs to be minutes.

"There's never enough analysts to do the work. We detected fraud — what do we do from there? This is a human-dependent process that I can't scale. I don't have the time to sit around and build agents."

PromptQL provides their team an AI analyst that auto-answers the full investigative checklist across all data sources in seconds, with confidence levels on every answer, so human analysts stay focused on judgment calls, not data retrieval.

The Challenge

Their ML models are working well. Detection isn't the concern. The bottleneck is everything that happens after the alert fires.

Each analyst works through the same investigative checklist for every case: authentication anomalies, velocity patterns, geolocation signals, transaction history, pulling manually from Oracle, AWS, and Snowflake environments. The company has already codified the logic: they know the 80+ questions, have mapped the data sources, and understand the queries behind each one. They've even spent an engineering sprint scoping whether traditional technology can solve it. It can.

But hardcoded SQL automation is brittle. Fraud patterns change. Every new fraud type means another sprint, more maintenance, more technical debt.

What they need is something that can answer those questions dynamically and do it in seconds.

How PromptQL Solves It

PromptQL connects to the company's existing Snowflake environment. No migration, no rearchitecting. It answers the full investigative checklist automatically across all relevant data sources.

Auto-answered investigative checklists. The workflow that used to take hours now takes minutes. Authentication signals, transaction patterns, geolocation anomalies — all surfaced in a single output, automatically, for every case.

Confidence levels on every answer. Analysts see immediately where the output is high-certainty and where human judgment is needed. The system keeps people in the loop at exactly the right moment.

Deterministic, auditable outputs. Same question, same answer, every time. Every finding traces back to its source data. No black box, no guessing, nothing a regulator can't follow.

Works within existing infrastructure. Data stays in the customer's environment. PromptQL connects to Snowflake and supporting systems where the data already lives. No egress, no new pipelines to maintain.

Business Outcomes

Hours compressed to minutes. For a team closing 1,000+ cases a day, the compounding efficiency is significant. Analysts stop retrieving data and start making decisions.

Analyst capacity unlocked. The same team handles more volume without adding headcount, focusing entirely on the judgment calls that require a human.

Built-in audit trail. Every output is traceable to source data. Compliance review doesn't require rebuilding the reasoning. It's already there.

A foundation for more. Fraud investigation is the entry point. The same infrastructure opens the door to client communication automation, detection model improvement, and cross-functional fraud analytics across the business.

The fraud ops leader frames the goal simply: "We need to get better at investigating 1,000 cases a day — making better decisions and closing cases with minimal analyst intervention, if any."

That's exactly what PromptQL is built for.

See how PromptQL supports a variety of Financial Service use cases. → Learn more

See it in action on your data → Request a demo