Validated case study · Machine Learning

Humach Finance: Fraud Triage

Work with rare-event detection, threshold trade-offs and cost-aware evaluation.

Download mini_finance.csvUse Python, SQL, Excel or your preferred workflow unless a task specifies otherwise.
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Project dataset

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txn_idamountforeignnightmerchant_riskfraud
T001490030
T002860010
T0031230040
T0041600020
T0051970000
T0062340030
T0072710010
T0083080040
T0093450120
T0103820000
T0114190030
T0124560010
T0134931040
T0145300020
T0155670000
T0166040030
T01718410011
T0186780140
T0197150020
T0207520000
T0217890030
T0228260010
T0238630041
T0249000020
T025370000
T026741030
T0271110110
T0281480040
T0291850020
T0302220000
T0312590030
T0322960010
T0333330040
T03415700021
T0354070000
T0364440130
T0374810010
T0385180040
T0395551020
T0405920000
T0416290030
T0426660010
T0437030041
T0447400020
T0457770100
T0468140030
T0478510010
T0488880041
T049250020
T050620000
T05112990031
T0521361010
T0531730040
T0542100120
T0552470000
T0562840030
T0573210010
T0583580040
T0593950020
T0604320000
60 rows6 columnsmini_finance.csv
Project workspace

Analyse → validate → submit.

Numeric and decision tasks are auto-checked. Written reasoning is checked for completeness and key ideas, then included in your local submission report.

0/5 validated
01

How many transactions are in the dataset?

02

How many transactions are labelled fraud?

03

What is the largest transaction amount?

04

For rare fraud, which evaluation view is often more informative than accuracy alone?

05

Describe the business trade-off in lowering the fraud threshold.

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