Confidential Case StudyFraud Analytics · Machine Learning · Banking

Fraud Detection Models for Banking Risk

End-to-end development of machine learning fraud detection models covering origination fraud and behavioural risk signals — with explainability, governance documentation, false positive management, and operational deployability at the core.

PythonMachine LearningFeature EngineeringModel ValidationStatistical ModellingSHAP / ExplainabilityGovernance Documentation

Business Problem

Fraud losses arise across multiple stages: origination fraud, account takeover, and behavioural drift. Rule-based systems struggle to adapt to evolving fraud patterns. Machine learning models offer improved detection but introduce explainability and governance challenges that are particularly acute in regulated banking.

The design challenge: build models performant enough to reduce fraud losses, explainable enough to satisfy governance, and operational enough to deploy within existing banking infrastructure.

My Role

I led the analytical development: data exploration, feature engineering, model selection and training, validation design, and governance documentation. I also contributed to stakeholder presentation of model findings and the design of deployment and monitoring considerations.

Feature Engineering

Feature engineering is where domain knowledge translates into model signal:

Behavioural Indicators

Transaction velocity, pattern changes, deviation from historical baseline, unusual timing

Application-Level Signals

Origination patterns, data consistency, characteristic combinations associated with fraud typologies

Network-Derived Features

Connectivity features derived from shared identifiers and relationship analysis

Temporal Features

Recency, frequency, and sequence patterns over time

Validation

Validation was designed to reflect production conditions:

01Out-of-time validation to assess model stability and performance drift

02False positive analysis — operational impact of review load on credit and investigations teams

03Score distribution analysis across customer segments to identify concentration risks

04Sensitivity analysis on decision threshold selection

Governance

Model documentation covering methodology, data sources, feature definitions, and validation results

Explainability using feature contribution analysis for individual predictions

Clear limitations and assumptions documented alongside model performance

Monitoring framework covering score stability, population shift, and degradation triggers

Escalation and override logic documented for operational deployment

Business Impact

Strengthened origination fraud detection with improved model-based scoring

Provided explainable, governance-compliant fraud scoring for credit and risk review

False positive management reduced unnecessary friction for legitimate customers

Model documentation met internal risk and audit standards

Lessons Learned

01Governance documentation is not a post-project task — it shapes every design decision from data preparation onwards.

02The best model is not always the most complex one. Interpretable models that can be explained to a risk committee often deliver better organisational outcomes.

03Out-of-time validation is non-negotiable. Temporal data leakage can inflate apparent performance significantly.

04False positive management requires ongoing dialogue with operational teams — the cost is context-specific and evolves.

Confidentiality Note: Due to employer obligations, code, raw data, proprietary models, and internal investigation details are not disclosed. This case study presents architecture, methodology, and business impact only.

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