Autonomous AI discovery built for fintech & payments.
Run an autonomous AI discovery on your transaction, lending, and risk data — find the chargeback drift, the cohort default the origination model can’t see, and the ACH segment concentration, before the next risk review.
Built for VPs of Risk, Heads of Fraud, and Heads of Credit at card issuers, lenders, processors, and neobanks.
Origination, servicing, transaction, bureau — read together.
Risk models read one system. The drift hides between them. The chargeback the rules engine missed, the cohort default the origination score didn’t predict, the ACH return concentrated in a segment nobody weighted — where the loss is leaking, why the rate moved, and which segments are quietly outperforming the model all live across multiple tables. DecisionBox runs them overnight. The agent decides what to investigate, writes the queries, validates every number against your data, and ships ranked findings the risk committee and the auditor can both follow.
See how every number gets checkedThe signals already in your data are the ones the model didn’t weight.
Three benchmarks risk and credit teams are quietly paying right now. Each one points to the same gap: the signal was there; the bandwidth to investigate it wasn’t. Every cycle of waiting compounds into loss the model could have flagged.
Three findings, three actions, one overnight pass.
Three pairs the agent surfaced in real fintech discoveries. Each finding ships with a numbered recommendation attached — not a chatbot you have to drive. See how the autonomous discovery loop produces ranked, validated outputs.
Each pair above is from a real DecisionBox run. Source data is anonymized for the public site.
See the chargeback drift and the cohort the model can’t see, before your next risk review.
For card, lending, payments, and neobanking — on the same engine.
Same engine, same data, different analysis areas. Pick the surface that matters this cycle.
Catch the merchant cluster before the chargebacks land.
Dispute and chargeback signals show up in the data days before the rules engine catches them. The agent watches merchant clusters, MCC drift, and dispute timing together and flags the segment driving the next wave — the same diagnostic shape that why-metric-moved investigations are built around.
- Chargeback rate drift by merchant cluster and MCC
- Dispute concentration by issuer, BIN, and time-of-day
- First-party vs third-party fraud signal separation
- Card-testing pattern detection by velocity and amount
- Refund-vs-chargeback ratio drift by category
Built for risk teams who need to show the auditor the work.
Three things every risk review surfaces. Every DecisionBox deployment ships with all three.
Sees what the model didn’t weight
You’ll see drift the rules engine, scoring model, or monitoring system didn’t flag. The agent decides what to investigate; you review. Not a chatbot you drive — a ranked backlog of chargeback clusters, cohort defaults, and segment concentrations you didn’t have to write.
Every number the auditor can follow
Every claim re-queried against your data before it ships. Every SQL query and reasoning step is logged and visible — the paper trail risk, model, and audit committees expect before trusting a number that triggers a control change.
Your data stays with you
Your credentials never leave your environment. The agent reads what’s already there; nothing exfiltrates. Every query and decision is logged for the team that has to defend the numbers downstream — and for the regulator that may ask later.