Fraud teams drown in false positives, burn hours on dead-end alerts, and risk filing SARs on good customers. The TAZI ATO Agent scores account-takeover risk per session and transaction with explainable ML on session, device, transaction and payee-velocity data — a named fraud pattern behind every alert. Proposed actions are validated by an expert panel applying your fraud team’s policy and a simulated focus group of your own customers before anything executes.
Reviewing the top 1% of the scored queue instead of 1% picked at random: 2,289 of 3,421 actual frauds surfaced in 3,338 reviews — a 65× lift, worth $480K more fraud caught per year and $2.5M measured on hold-out.
True-positive rate on quiet-account takeover, with the validated-action layer on the same detector.
Annual false-negative fraud cost, after validation.
False SAR exposure on legitimate customers in the top tier — full action ladder, 100% auditable rule trail.
Every alert carries a named pattern — credential-reset transfers, rapid multi-payee, IP hopping — with probability and reliability. A reason code your investigators already recognize, auditable in plain language.
Alerts ranked by dollar exposure (amount × fraud probability), not raw confidence — the highest-probability alerts often carry small amounts. Every answer ships with a case receipt: source, scope, population, ordering.
Fraud (SIU), AML and legal seats — with their real mandates and vetoes — sign off every action in a logged meeting. Every ruling keeps its reasoning on the record.
"Will a genuine customer be harmed by this action?" — asked and answered before anything executes, by personas carrying real properties from your customer population.
SAR drafts assembled from monitoring data, with tipping-off phrasing engineered out. Filer fields and mandatory review stay with your institution — compliance keeps the pen.
Fraud teams just ask, in plain language: current exposure, which alerts to work first this shift, what drives risk, help on one case. Every answer is grounded in model output and cites the pattern behind it.
Define the target label, the business KPI and the action ladder (monitor → step-up auth → hold → SAR) on an anonymized data sample; build the data dictionary.
Train the prediction model and its companion explanation model; explanations arrive in your fraud team's language.
The action-proposal agent drafts an action per alert; the expert panel applies your fraud team's policy and the focus group simulates customer friction. Only corroborated actions reach the queue.
A/B test against the incumbent triage; handoff document, fairness workpaper and compliance docs — approximately 72 proof-of-value (POV) hours, then live with monthly monitoring.
A credit union struggling with account takeovers faced high false positives and wasted time investigating unnecessary transactions. It turned to TAZI for a more efficient fraud-detection solution. Within just two months, TAZI improved the credit union’s true positives by 56%, reduced unnecessary investigations, and prevented $2M in fraud losses — a 53% gain in investigation efficiency that lets the fraud team focus on high-risk cases.
Two value drivers: fraud loss prevented by catching more takeovers, and analyst time saved by cutting the noise. Enter four numbers about your fraud operation.
Figures in USD, pre-set for a midsize US institution (illustrative — change any field). A human stays in the loop on consequential actions; freed analyst capacity is reallocated, not removed.
Used only for the platform-cost estimate.
Estimated annual platform cost: $180K — auto-estimated as $150,000 base + $1,200 per analyst / year, used only for the ROI percentage. Illustrative; actual pricing depends on data volume, agents deployed, and integration scope.
Every input maps to data your teams already hold. Here's where to pull it and how to compute it.
| Lever | Where it comes from | How to compute / benchmark |
|---|---|---|
| ATO attempts per year | Fraud case management + post-incident review. | Confirmed ATO cases + estimated missed (from customer reports, chargebacks, later-discovered fraud). Context: US ATO losses ~$15.6B in 2024, up from $12.7B in 2023 (Javelin). |
| Average loss per takeover | Fraud loss ledger / finance. | Total ATO fraud losses ÷ number of successful takeovers over the same period. |
| Alerts reviewed per year | Case-management / transaction-monitoring queue. | Count of alerts routed to human review in 12 months. |
| Detection rate today | Labeled outcomes on known-fraud cases. | True positives ÷ (true positives + missed fraud) = recall on confirmed ATO. Measure on a labeled sample. |
| Detection rate with TAZI | Back-test on the same labeled set. | Re-score historical cases with the model; recall on the identical sample gives an apples-to-apples lift. |
| Cut in unnecessary reviews | Precision of the current queue. | Share of reviewed alerts that turn out legitimate today, minus after tuning. Benchmark: only ~1 in 5 blocked transactions is truly fraud (Wedge et al.); false-positive losses ≈ 19% of fraud cost vs ≈ 7% for real fraud (J.P. Morgan). |
| Cost per investigation | Fully-loaded analyst cost × handling time. | Analyst hourly rate × avg minutes per alert. Benchmarks: ~7–10 min per alert; analyst salaries ~$65K–$85K (FluxForce). |
Illustrative model; figures depend on your inputs and are not a performance guarantee.
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