Release Notes

What we ship, month by month.

Run-True Decision releases. Ordered newest first.
Plain language. No marketing fluff.

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April 2026 LATEST

Sprint 1 — Audit-Ready Decision Logs

Banks running Run-True Decision can now answer the question every internal audit team eventually asks: “Show me exactly why you made this decision.” Decision audit records are now captured for evaluation requests — with encrypted sensitive fields and privacy-preserving lookup — and analysts can replay past decisions to see how today’s rules and models would have scored them differently. This is the foundation for backtesting, audit trails designed for internal review, and rule-change confidence.

What's New

  • •Decision Audit Records. Decision audit records are now written for evaluation requests, capturing inputs, rule outputs, and the model versions in effect at decision time. Designed to support data residency requirements — your audit data stays where your decision data is.
  • •Audit Replay. Replay past decisions against the current rule set and model. Returns a verdict showing whether the result matches, drifted, or changed because of a rule update or a model update.
  • •Drift detection across rule and model versions. When today’s engine produces a different result than the original decision, the system tells you precisely whether the change came from a rule edit, a model update, or both — so you can defend a rule change in a control-committee review.
  • •Role-based access for analyst replay workflows. A new permission gates who can run replays, separating day-to-day operators from senior analysts performing rule-change reviews.

Improvements

  • •Encoder path tuned for banking-mode traffic — designed for millisecond-level overhead on the live decision path.
  • •New command-line tool for one-off audit replay against any historical decision ID.
  • •Tightened end-to-end test coverage on the audit persistence path so audit logs survive partial outage scenarios.

Fixes

  • •Resolved an edge case where a transient persistence error could mask the underlying audit-record failure mode.

Coming Next. Backtesting on captured audit records — analyst tooling to test a proposed rule change against the past month of real decisions before promoting it. Available on the roadmap.

April 2026

Billing & Metering Hardening

For banks running Run-True Decision on-premise, billing transparency matters as much as decision accuracy. This release ships audit-ready metering reports with HMAC-validated metadata so finance, procurement, and compliance teams can independently verify usage records without trusting vendor-supplied summaries.

What's New

  • •Audit-ready billing reports with HMAC-validated metadata. Every billing report carries an HMAC signature so on-premise customers can verify the report wasn’t modified after generation.
  • •In-dashboard audit UI for reviewing past metering reports, exporting them for procurement, and downloading the signed evidence file.
  • •Role-based access — billing access is scoped separately from fraud-operations access, so finance can see what they need without seeing PII.

Improvements

  • •Command-line verification tool included for offline procurement reviews.
  • •Period-boundary handling tightened so usage rollups match exactly across all timezones.
  • •Reports designed to support data residency requirements — the audit chain stays in your infrastructure.
April 2026

Native Agent Integration (MCP)

Run-True Decision is one of the first fraud decision engines to expose a native MCP (Model Context Protocol) interface, letting AI assistants and analyst copilots query the engine directly through standardized tool calls. Fraud teams can wire the engine into Claude, ChatGPT, and other agent runtimes without building a custom integration.

What's New

  • •Native MCP server with a curated set of tools — fetch a decision, summarize an investigation, list rule triggers, and pull aggregate metrics by tenant.
  • •Bearer-token authentication so each agent runtime gets a scoped, revocable identity rather than sharing the engine’s primary API key.
  • •Curated tool surface — the MCP server exposes only the read-side investigation actions, keeping write operations behind the dashboard’s existing maker-checker approvals.

Improvements

  • •Tooling hardened to handle multi-tenant access cleanly so a single deployment can serve multiple business units without crossing data boundaries.
  • •Test coverage extended to verify agent-issued queries against the tenant isolation design.
April 2026

Adversarial QA Framework

Fraud detection is the only product category where the user is actively trying to break it. We built an adversarial QA framework that runs a structured red-team pass against the engine — designed to catch the structural blind spots traditional regression tests miss.

What's New

  • •Red-team and green-team test harness that exercises the engine against known fraud patterns and adversarial mutations.
  • •Dashboard surfacing of adversarial run results so analysts can see exactly which mutations were caught and which slipped through, alongside their normal investigation views.
  • •Structural-gap detection that flags scenarios the rule library doesn’t yet cover — surfaces gaps before a real attacker finds them.

Improvements

  • •Test data generation pipeline rebuilt to mix synthetic and real-shape transaction patterns so coverage maps to actual SEA market behavior.
  • •Run reports formatted for control-committee review — a standing artifact for every release cycle.
March 2026

Banking Fraud Templates Library

A pre-configured library of banking fraud templates is now live, covering the scenarios SEA banks asked for most often during pilot conversations: account takeover, wire-transfer scams, structuring patterns, and authorized-push-payment fraud. Banks can deploy the library out of the box and tune individual templates without writing code.

What's New

  • •Pre-configured banking fraud templates for wire, account takeover, structuring, and APP-scam scenarios. Built from real bank requirements.
  • •No-code rule editor — fraud teams can adjust thresholds, add carve-outs, and shadow-test new templates from the dashboard.
  • •Cross-cut signals that fire alongside domain rules to catch scenarios that single-rule logic misses (for example, beneficiary-familiarity scoring on wire transfers).
  • •Shadow mode for every template — see what would have triggered before promoting a rule to production.

Improvements

  • •Rule editing flows tightened around a maker-checker approval pattern so a single analyst can’t unilaterally change production rules.
  • •Backtesting hooks added to the no-code editor for “what would have happened” analysis.
  • •Audit trail on every rule change so a control committee can reconstruct the rule history.

Want to see this in action?

Pilot programs available now for SEA banks.

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