tracewise · hacker house goaagentic fraud investigation

It knows when it doesn't know yet.

Twenty fraud alerts with no labels, and a bank risk score that is wrong in both directions. Half the cases are legitimate by construction. Detection is not the hard part — knowing when the evidence is thin, asking for more, and changing your mind when it arrives is.

the evidence a row-based model cannot see

One handset. Seventeen cards. Eighteen customers.

Community detection over the card-to-device subgraph returned a connected component spanning seventeen other cards. A model scoring one payment in isolation cannot reach the second customer — this is two hops. Drag any node; the layout responds.

device ring · interactive
17 cards · 1 device
C13487-K1SM-G935F Build/NRD90M
shared deviceflagged cardconnected carddrag nodes · scroll to zoom · drag canvas to pan

the agent changing its mind

Watch the recommendation move when evidence arrives.

The brief requires the next best action recorded before evidence is requested and after it returns, because that is what separates an investigation from a classifier. Scrub the timeline and find the moment it flips.

Investigation replay

Drag the timeline to watch the agent's reasoning at that moment — recomputed from the case's real evidence, not replayed.

0.50uncertain
step 1 / 8

Evidence 1

evidence discovered (1)

  • Precedent (confirming): CC-0117 (confirmed fraud, $100.06 exposure): semantically similar analyst notes (cosine distance 0.27), no direct structural overlap

    Graph

    query:prior_case_candidates(case=CC-0117)

recommendation at this step

  1. CLOSE_NO_FRAUDAutoR0: policy default, settled low, no rule triggered

deterministic, and checkable

The decision is a rule engine, not a model's opinion.

Probability comes from a weighted evidence ledger; actions come from the bank's own ten rules. Toggle the evidence and move the exposure — the numbers below are computed by a faithful port of the same code that produced the twenty answer files, matching it to four decimal places.

Policy sandbox

Toggle evidence, drag exposure. The probability and the action list are recomputed live from the same formula and rules the agent runs — not a guess.

Corroboratingraises the probability
Exoneratinglowers it — why the agent does not block everything
$300.00
$0$1,000 SAR$2,500 L1/L2$5,000

BLOCK_CARD routes to L1 approval at this exposure.

0.17uncertain
fraud probability
0.150.300.700.85
Recommended actionslive
  • VERIFY_WITH_CUSTOMERAutoR1
  • STEP_UP_AUTHAutoR1

the benchmark

Twenty cases. Ten of them legitimate.

An agent that blocks everything scores badly, and the dataset is built to punish exactly that. Half these alerts are ordinary customers. Open any case for the full record: evidence with provenance, the precedent retrieved, the rules that fired, and the approval route.

1
fraud
9
legitimate
10
uncertain
CasePatternFraud probabilityExposureVerdict / status

hacker house goa · agentic fraud investigation

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