Explainability
Definition
The degree to which a model's output can be explained in terms of its inputs and logic. Logistic regression and decision trees are inherently explainable; deep neural networks are not. Forensic applications prioritise explainability because the output must withstand expert cross-examination.
Related terms
- Anomaly scoring
- A numeric score assigned to each entity or transaction based on how different it is from the expected population, derived from multiple...
- Isolation Forest
- An unsupervised machine-learning model for anomaly detection. It builds random decision trees and scores each record by the average depth required to...
- Logistic regression (supervised fraud model)
- A classification model trained on historically labelled transactions (fraud vs. legitimate) to estimate the probability that a new transaction is fraudulent. Requires...
- Network analysis (link analysis)
- A method that models entities (people, companies, accounts, addresses) as nodes and connections between them (shared attributes, transactions, ownership) as edges, then...
- Timeline reconstruction
- The assembly of events from multiple data sources onto a chronological axis to establish the sequence of actions in a fraud: when...
Explained in
- Data Analytics in Fraud InvestigationsThe degree to which a model's output can be explained in terms of its inputs and logic. Logistic regression and decision trees are inherently explainable; deep...