Logistic Regression (Supervised Fraud Model)
Definition
A classification model trained on historically labelled transactions (fraud vs. legitimate) to estimate the probability that a new transaction is fraudulent. Requires a labelled training set and is interpretable: each feature's contribution to the score is a coefficient.
- Model type
- Supervised classification model
- Training input
- Historically labelled fraud vs. legitimate transactions
- Output
- Probability score that a transaction is fraudulent
- Interpretability
- Each feature has an explainable coefficient
Common questions
Why is logistic regression favoured over less interpretable models in some fraud investigations?+
Its per-feature coefficients let an analyst explain precisely which factors drove a given fraud probability score, which supports a defensible, explainable finding rather than a black-box result.
What is the key limitation of a logistic regression fraud model?+
It needs a sufficiently large and accurately labelled set of past fraud and legitimate transactions to train on; without reliable labels the model's probability scores cannot be trusted.
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