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Why fraud models should only propose. By Amir Shachar.

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Riskernel
Sample · synthetic data

This is a sample. Every number on this page is synthetic. It shows the shape of a day-14 shadow-test report and describes no real company or result.

Sample report

Sample report (synthetic data)

What you get at day 14 of a shadow test, laid out the way it arrives. All figures are made up and round on purpose.

1 · Summary Sample · synthetic data

One flow, one number, 14 days.

Flow
Instant payouts to newly added debit cards
Agreed number
Share of payouts sent to manual review
Period
14 days
Events scored
100,000
Where it ran
In parallel. Nothing in the decision path.

2 · Scores against current results Sample · synthetic data

Where the scores fall, and what your system did.

Each event gets a score from 0 to 1. The last column is how many events in that band your current system sent to review.

Score distribution against current results (synthetic)
Score bandEvents scoredShare of eventsCurrent system sent to review
0.0 to 0.261,00061.0%100
0.2 to 0.421,00021.0%200
0.4 to 0.610,00010.0%500
0.6 to 0.85,0005.0%1,050
0.8 to 1.03,0003.0%1,450

Read first. 5,500 events in the two highest bands were not sent to review by the current system. They are the first events a joint review reads. A high score is a reason to look, not a verdict.

3 · Top reasons Sample · synthetic data

Why the highest scores were high.

Every score ships with its reasons. This is how often each one appeared among the top reasons for events in the two highest bands.

  • first_time_payee_corridor55%
  • payout_velocity_5m_spike45%
  • new_beneficiary_device35%
  • ip_country_mismatch25%
  • card_added_under_1h15%

4 · Findings Sample · synthetic data

What the report would flag.

Sample findings 3

F-01Sample · synthetic dataObservation

High scores cluster on payouts to destinations added shortly before the payout.

In this synthetic set, most events in the top band went to a destination added within the previous hour.

Evidence
Reasons table above. Synthetic.
First control
Hold or cap the first payout to a newly added destination.
F-02Sample · synthetic dataObservation

The two systems disagree on a defined set of events.

The events in the two highest bands that the current system did not send to review are listed with their reasons in the raw scores.

Evidence
Summary and table above. Synthetic.
First control
Read that list together, event by event, before changing any rule.
F-03Sample · synthetic dataHypothesis

Some high scores may share a device across accounts.

The data in a shadow test is not enough to say. A pilot can test it.

Evidence
Not established. Synthetic.
First control
Add a shared-device check to the pilot and measure it.

5 · What a pilot would measure Sample · synthetic data

The same number, live, for 30 days.

A pilot runs the engine in parallel on the flow and measures it against the number named above. It ends in a go or no-go on evidence.

  1. The agreed number.

    Share of payouts sent to manual review, reported the way you already report it.

  2. The disagreement list.

    What happened to the events the two systems read differently.

  3. The open hypothesis.

    The shared-device question from finding F-03.

See the pilot rung · How a shadow test works

Want this on your own traffic?

A shadow test is 14 days and free. You keep the report.