WHERE AGENTIC AI BREAKS HERE
Bias the reviewer cannot see case by case
Each summary looks reasonable in isolation — but together, a pattern of inconsistent denials begins to emerge.
Hallucinated case summaries
The agent smooths over evidence inconsistencies. The reviewer approves a clean narrative the data does not support.
Inconsistent policy application
Two near-identical disputes, two outcomes. But together, discrepancies and inconsistencies show.
The agent generates the chargeback decision
AI summarises the case and recommends an outcome. Disseqt sits alongside, evaluating in real time.
Disseqt scores it for accuracy, bias, and consistency
The evaluation checks the summary against source evidence, decision history, and comparable cases. Each output gets a confidence score.
The reviewer sees the score, with root-cause analysis on every flag
When confidence passes, sign-off takes seconds. When it fails, the reviewer gets a specific reason: missing evidence, outlier pattern, inconsistency with similar cases.
04 The audit trail writes
itself
Compliance generates regulatory reports from the same data. Risk leadership reads live decision quality, not retrospective summaries.
Per-decision confidence scoring
Every AI-generated chargeback summary scored before the reviewer sees it. Clear pass or fail, with a reason on every flag.
Population-level bias signals
Patterns no individual reviewer would catch, surfaced before a regulator catches them
Regulator-ready reporting
FCA, CFPB, and scheme-rule reports generated from live decision data. No separate audit assembly.
One pattern, adjacent workflows
The same assurance shape reused across fraud claims, billing disputes, and merchant credit decisions.
