Help investigators see the cases that deserve a closer look, and why.
Models and rules score each transaction, claim or application for risk and explain which signals drove the score. High-risk items go to an investigator's queue with the evidence assembled. People make the decision; the system helps them prioritize.
- 01Collect event and context data
- 02Score and explain
- 03Prioritize the queue
- 04Investigate and decide
- 05Feed back outcomes
The workflow
Fraud, risk and compliance analysts who review alerts and cases, and the operations teams whose transactions, claims or onboarding decisions depend on them.
- Static rules produce so many alerts that analysts cannot review them all properly.
- New fraud patterns are found months later, in losses or chargebacks.
- Analysts rebuild the same evidence from several systems for every case.
- Outcomes of investigations are not fed back, so the rules never improve.
Collect event and context data
SystemTransactions, claims or applications are joined with account history, device, network and reference data as they arrive.
Score and explain
AIModels and rules produce a risk score and the top contributing signals for each item.
Prioritize the queue
SystemItems above agreed thresholds enter the investigation queue, ordered by risk and value, with evidence and related cases attached.
Investigate and decide
PersonAn analyst reviews the case, gathers anything else needed and decides the outcome — clear, hold, request information or escalate.
Feed back outcomes
SystemConfirmed outcomes are recorded and used to retrain models and tune thresholds on a regular schedule.
Where people stay in control
The system does not decline customers, deny claims or close accounts on its own. It ranks and explains; trained analysts decide, and their decisions are recorded with reasons. Thresholds, model changes and any automatic holds are approved by the risk owner, and model performance and fairness are reviewed on a set schedule.
- Confirmed fraud found per alert reviewed
- Share of confirmed cases that were flagged before loss
- False positive rate and its effect on legitimate customers
- Analyst time per case
Data and integrations
- Historical transactions, claims or applications with confirmed outcomes
- Account, customer and device data available at decision time
- Existing rules and alert history
- Case management system access for queueing and feedback
Realistic boundaries
- Scores support decisions; adverse actions against customers remain human decisions made under your policies.
- Models need labeled outcomes to learn; where confirmed cases are few, rules and anomaly detection carry more weight.
- Features are reviewed for proxies of protected characteristics, and explanations are kept for audit and regulatory review.
How we build it
- Machine Learning & Predictive AnalyticsForecasting, classification, anomaly detection and recommendation models trained on your data and evaluated against the decisions they support.
- Data EngineeringData architecture, pipelines, quality and access that make your data usable for reporting, operations and AI.
- Responsible AI & GovernancePractical ownership, review, privacy and audit controls that let teams ship AI without losing track of what it does.
- Data Analytics & BIAgreed metric definitions, dashboards and analysis designed around the decisions your teams actually make.
- Financial ServicesDocument review, knowledge access, fraud signals and customer operations — built with the controls and auditability the sector expects.
- InsuranceClaims intake, underwriting submissions, policy knowledge and fraud signals — AI that prepares the file so adjusters and underwriters decide with the evidence in front of them.
- Retail & CommerceCustomer service, catalog operations, demand planning and personalization — connected to the commerce platforms and order systems you already run.
Questions buyers ask
Not unless you decide it should, for specific cases with clear evidence and an approved policy. By default it prioritizes and explains, and investigators make the call. Any automatic hold is designed with a fast path to human review.
Each score comes with the signals that contributed most, in plain terms, and the model version and data used are logged. See our AI governance work for documentation and review practices.
Map this workflow with us.
Tell us about the workflow or product. We reply with questions, a suggested first step and who would work on it.