The Playbook to Stop Polymorphic AI Fraud
Keep every rule earning its keep
Turn intent and live threat telemetry into optimized detection strategies — create targeted rule strategies, tune thresholds, and surface coverage gaps — without manual, engineering-heavy effort.
Triage, tune, and close the gaps
Vera works on production data to rank what’s working, tune thresholds with the arithmetic shown, and surface the fraud no rule covers yet.
Rank what’s working
Rule portfolio triage ranks rule performance and surfaces zero-true-positive rules worth retiring.
Thresholds backed by the math
Threshold tuning uses WOE/IV with projected impact on recall and false positives — the arithmetic shown.
Find fraud no rule covers
Coverage-gap profiling and plain-language typology-to-rule surface fraud nobody wrote a rule for, with net-new and overlap checks.
Propose, review, publish
Vera analyzes production data, shows its arithmetic, and proposes threshold or logic changes with a full before/after summary — a human reviews and approves before anything ships.
From telemetry to published change
Ask Vera to tune or improve a rule.
It analyzes production data and shows its arithmetic.
It proposes threshold or logic changes with a full before/after summary.
You review and approve.
The change moves through the standard lifecycle to publish.
Vera proposes; a person approves
Both tuning and discovery agents work on production data, show their arithmetic, and never ship a change on their own — with checkpoint gating, analyst override, and a full before/after change summary at every phase.
Strategy across fraud and AML
Rule threshold tuning, rule discovery, and targeted strategy changes across fraud and AML. Example: after new FinCEN guidance on rapid movement of funds through newly opened accounts, Vera proposes a rule combining account age, transaction velocity, and rapid withdrawals — which the team tests, approves, and deploys through existing governance.
More coverage, fewer false positives
In one merged-cohort case study, a credit union cut false positives by roughly 80% using Vera-tuned strategy changes.
Tune fast without breaking what works
A credit union used Vera to build a reversible risk switch without rewriting its 100+ core rules, cutting false positives for a newly merged cohort by roughly 80% and deploying in under a day.
Tuned against real detection telemetry
Vera tunes against unified real-time risk data and actual rule-performance telemetry and can push governed changes into the pipeline — and over standard MCP, your own AI can orchestrate this with no custom middleware.
Governed, private, and auditable
Like every Vera agent, strategy tuning runs on Amazon Bedrock statelessly — prompts and data are never stored by AWS or used to train foundation models, and are processed in full per-customer isolation.
- Stateless processing on Amazon Bedrock — nothing stored by AWS
- Net-new and overlap checks so results are never double-counted
- Measured results kept separate from estimates
- Checkpoint gating and analyst override at every phase
- Full before/after change summary, audit-logged
Common questions
Does it change production automatically?
No. Every change is proposal-only and requires human approval.
How does it avoid inflating results?
Proposals are checked for net-new true positives and overlap, so nothing is double-counted, and measured results are kept separate from estimates.
Optimize strategy without the overhead
See how Vera turns telemetry into governed strategy changes.