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Transaction monitoring — complete lifecycle

Customer- and partner-facing view of Fraud / transaction monitoring from ingestion through resolution — with repository boundaries and unverified stages marked explicitly.

Evidence boundary

Stages marked Coded (Fraud AI) are implemented in MasteryHive Fraud AI products. Stages marked UNVERIFIED / external depend on the banking application backend or dashboard, which is not in the documentation source tree for this site.

Lifecycle map

flowchart TB
  ingest[1_Ingest]
  baseline[2_Baseline]
  score[3_Score_ML_Rule_or_CaseRules]
  report[4_Report_compose]
  handoff[5_Dashboard_report]
  post[6_Post_action]
  case[7_Durable_case]
  assign[8_Assignment]
  approve[9_Approval]
  resolve[10_Resolution]
  ingest --> baseline --> score --> report --> handoff
  handoff -.-> post
  post -.-> case --> assign --> approve --> resolve
# Stage What happens Completion signal Boundary
1 Ingestion Accept a batch or event; schedule baseline work Request accepted; work scheduled — not “all checks finished” Coded (Fraud AI)
2 Baseline / profiling Temporal, location/device, transaction behavioural checks Profile updated or cold-start skip Coded (Fraud AI)
3 Rule / ML evaluation Authenticated scoring returns a prediction Prediction returned to caller Coded (Fraud AI)
3b Case-rule evaluation Synchronous case-oriented rules HTTP prediction Coded (Fraud AI) — see Case Management Part A
4 Report Validate contract, apply policy, attach narrative Report ready to deliver Coded (Fraud AI)
5 Operational handoff Deliver structured report to banking application Report accepted by create-report endpoint Coded (Fraud AI → app)
6 Post-action Work after scoring/report (triggers may be published) — PARTIAL — publish exists; consumer UNVERIFIED
7 Durable case Case record in ops workspace — UNVERIFIED in source tree
8 Assignment Owner / queue — UNVERIFIED
9 Approval Maker-checker / approve-reject — UNVERIFIED
10 Resolution / closure Close, reopen, SAR filing — UNVERIFIED (filing also not in code — AML reporting)

Partial failure semantics (partner-visible)

Situation Expectation
Cold start Baselines may skip profile persistence; prefer rule scoring for those customers
Mixed ML+rule identity keys on report compose Rejected — see fraud output contract
Score/tier conflict Rejected; do not retry the same invalid body expecting success
Narrative model failure Deterministic summary fallback; decision still from policy code
Dashboard handoff failure Compose surfaces failure so delivery can be retried per environment
Case-rule handler errors Client-visible HTTP error for that request
Missing durable case after report Not a Fraud AI defect by itself — case desk is external / unverified

Reliability expectations

A scored transaction may not always receive a dashboard report

Scoring can succeed while report delivery is delayed, unsuccessful, or rejected by contract validation. ML scoring and report compose currently use different tier labels and score boundaries — some predictions never become reports until owners align policy. See Fraud output contract — Risk tier mapping.

Guidance:

  • Do not treat a missing report as “low risk.” Prefer the prediction until a report arrives.
  • Resubmitting a scoring call after fixes produces a new prediction and a new report attempt.
  • Confirm retry behaviour for your environment with MasteryHive.
  • Reconcile scoring responses against received reports by transaction id.

Decision policy

Primary decision + escalations on the dashboard payload are produced by fraud-decision-v1 (provisional / non-production). Escalation labels are not regulatory filings.

Separating “case” language

Term Meaning here
Case-rule evaluation Stateless prediction API — Case Management Part A
Durable case Investigator workspace — UNVERIFIED in this tree — Part B
Report Investigator-facing payload with evidence and narrative