VALID Systems Catches 15–17% More Bank Fraud with Snowflake ML

Every check deposit triggers a split-second risk decision, and the stakes of getting it wrong are high for both sides of a transaction. If a bank makes the incorrect choice, they’ll absorb the loss from fraud, or jeopardize a legitimate customer who can’t access the money they’re owed.

VALID Systems exists to stop this from happening in the first place, helping financial institutions from community banks to the top 10 make these calls in real time across teller, ATM and mobile channels. From catching over $600 million in fraud to removing friction from the deposit experience, their platform guarantees over $6 billion in immediately available funds each month across more than 425 million unique accounts.

After seven years, the legacy AutoML platform powering InstantFUNDs (funds availability scoring) and CheckDetect (check fraud detection) had hit its ceiling. Hyperparameter tuning was opaque, accuracy metrics were optimized for rows instead of dollars, and IT-gated deployment cycles blocked the decision science team. Every inference call sent data to an external SaaS, creating the kind of egress and governance exposure risks banks can’t accept. With model risk requirements tightening, VALID needed infrastructure that moved at the speed of business without creating new vulnerabilities.

VALID Systems rebuilt their inference layer on Snowflake ML, swapping their legacy prediction servers for Snowpark Container Services. InstantFUNDs and CheckDetect now run inside the Snowflake VPC, governed by existing role-based access controls. Features, training data, models and inference all exist in one place, maintaining the same API contract with zero egress.

VALID’s models now optimize for the dollar value of the decisions they make, not just whether they got a row right or wrong. This shift in objective compounds performance. A small improvement targeting the highest-risk transactions outweighs a larger improvement spread evenly across all of them. Every tuning run is logged and fully auditable, so compliance teams get the documentation as a byproduct of the process instead of an afterthought.

The decision science team owns every step of deployment without a single IT ticket, while shadow mode and traffic splitting allow safe rollouts with no downtime. Every request and response is automatically captured back into Snowflake tables, feeding the next training cycle in a closed, secure loop.

VALID rebuilt its entire ML platform in roughly three months, and the results hold across every model. InstantFUNDs deliver a 7–10% decrease in loss rate, and CheckDetect detects 15–17% more potential fraud losses before they hit the books. One bank’s ATM channel even exceeded 20%. Sub-100 millisecond inference and end-to-end API latency under 370 milliseconds keep results well inside SLA commitments.

Every pipeline run passes more than 20 automated compliance checkpoints, with artifacts stored directly in Snowflake. For financial institutions where model management can exceed 40 hours per model, that compliance documentation now arrives as a byproduct of the process. Each additional model reuses already-running Snowflake compute, with near-zero marginal cost to scale.

VALID’s next frontier, the Snowflake Feature Store, extends that further. Near real-time feature availability will soon let any future VALID model draw from the same production-ready feature catalog. For every bank absorbing the risk and every customer waiting on their funds, confident, secure banking decisions already happen in the blink of an eye.