false-positive reduction lab

rule tuning + cost trade-offs

problem

financial crime teams face a trade-off: rules that catch more fraud also create more false positives. too many alerts waste investigation time and cost money, while too strict thresholds let fraud slip through.

dataset

• 500 customers (kyc risk, pep flag, sar history)
• 10,000v transactions (amount, country, mcc, channel, device, ip, timestamp)
• ~3% transactions labelled as fraud (fraud_y) for evaluation

features engineered

• amount_z : z-score of transaction amount relative to customer history
• tx_count_1d, tx_count_7d : customer activity velocity
• geo_mismatch : tx country vs home country
• device_fanout : distinct customers per device in last 7 days

rules

• r1 : amount > 95th percentile per customer
• r2 : tx_count_1d >= 10
• r3 : geo_mismatch == 1 and amount > 80th percentile globally

final alert logic = r1 OR (r2 AND r3)

results

• baseline (p=0.95): 304 alerts, precision ~1.6%, recall ~24%, weekly cost ~£12.5k
• tuned (p≈0.70): 329 alerts, precision ~2.1%, recall ~33%, weekly cost ~£11.9k
• drift monitoring showed stable r1, fluctuating r3, higher alert rates in online/mobile

plots

disclaimer

this project uses synthetic data. it is an educational prototype, not a production aml engine or legal advice.

repository structure

false_positive_lab/ README.md # project overview and instructions requirements.txt # dependencies .gitignore # files to ignore in gits LICENSE # open source license (MIT) data/ # synthetic customer and transaction datasets docs/ # charts (cost curve, pr curve, drift monitoring) notebooks/ # main analysis notebook outputs/ # csv of threshold evaluation

tests/ # simple pytest scripts basic validation checks are included in /tests/test_features.py.

• transactions_fp.csv exists in /data
• amount_z exists and has no nulls
• geo_mismatch is binary (0/1)
• device_fanout is always >= 1

run tests

pytest -v