yssfmlha/transfer-fraud-model

Bank transfer fraud model (feature input)

Gradient-boosted classifier. You compute the 15 features yourself and pass them in; the model returns P(fraud) and a risk level 0-3. Trained on a synthetic dataset - validate on real data.

Usage

from huggingface_hub import snapshot_download
import sys
folder = snapshot_download("YOUR_USERNAME/YOUR_REPO"); sys.path.insert(0, folder)
from predict import FraudModel

model = FraudModel.load(folder)
print(model.predict({'amount': 361.71, 'hour': 2.115, 'day_of_week': 2.0, 'history_count': 87.0, 'is_new_destination': 0.0, 'times_sent_to_destination': 3.0, 'days_since_last_to_destination': 0.0373, 'amount_vs_avg': 1.3774, 'amount_zscore': 0.3393, 'amount_vs_max': 0.2473, 'hour_familiarity': 0.046, 'hours_since_last_transfer': 0.6567, 'transfers_last_1h': 4.0, 'transfers_last_24h': 4.0, 'distinct_destinations_last_24h': 2.0}))

predict accepts one dict, a list of dicts, or a pandas DataFrame with these columns (extra columns are ignored, order doesn't matter, missing ones raise an error).

Input features

feature	definition
amount	Amount of the transfer being checked (same currency as in training).
hour	Hour of day as a decimal in the customer's LOCAL time (14:30 -> 14.5), range 0-24.
day_of_week	Monday=0 ... Sunday=6.
history_count	Number of the sender's earlier transfers in the last 180 days.
is_new_destination	1 if the sender never sent to this destination account in the last 180 days, else 0.
times_sent_to_destination	How many earlier transfers (last 180 days) went to this destination.
days_since_last_to_destination	Days since the sender's last transfer to this destination; -1 if never.
amount_vs_avg	amount / average amount of the sender's earlier transfers; 1.0 if no history.
amount_zscore	(amount - mean of earlier amounts) / std of earlier amounts; 0.0 if no history or std = 0.
amount_vs_max	amount / largest earlier transfer amount of the sender; 1.0 if no history.
hour_familiarity	Share (0-1) of the sender's earlier transfers made within +-2h of this hour (circular: 23:00 and 01:00 are 2h apart); 0.5 if no history.
hours_since_last_transfer	Hours since the sender's previous transfer (any destination); -1 if no history.
transfers_last_1h	Number of the sender's earlier transfers in the previous 1 hour.
transfers_last_24h	Number of the sender's earlier transfers in the previous 24 hours.
distinct_destinations_last_24h	Number of different destination accounts among those last-24h transfers.

"History" = the sender's transfers strictly before the one being scored, within the last 180 days.

Risk levels (probability cut-offs: [0.0053, 0.0819, 0.5633])

level	label	test transfers	fraud	fraud share
0	low	41,273	26	0.1%
1	elevated	2,355	103	4.4%
2	high	473	168	35.5%
3	critical	502	456	90.8%

Test metrics

{ "roc_auc_test": 0.9924, "pr_auc_test": 0.8382, "fraud_rate_test": 0.0169, "alert_precision_level2plus": 0.64, "alert_recall_level2plus": 0.8287, "recall_level1plus": 0.9655 }