Explainable AI could transform how banks detect and verify fraud

With fraud attacks becoming increasingly sophisticated, machine-learning systems must evolve quickly. Yet, regulatory frameworks in Europe, North America and Asia are moving toward more stringent requirements for transparency, fairness and auditability in AI-driven decision systems.

Interpretable artificial intelligence (AI) models can dramatically improve the accuracy, transparency and trustworthiness of online credit card fraud systems, according to a new peer-reviewed study. The research, published in Applied Sciences, examines how explainable machine-learning techniques can help financial institutions detect fraudulent transactions at scale while meeting increasing regulatory demands for model accountability and visibility.

The paper “Interpretable Ensemble Learning Models for Credit Card Fraud Detection,” evaluates two advanced ensemble-learning approaches, Random Forest (RF) and XGBoost (XGB), and integrates them with explainable artificial intelligence frameworks to understand how each model makes predictions in a high-stakes financial environment. The authors show that these systems offer extremely strong detection performance, but equally importantly, they provide interpretable outputs that help banks and regulators verify that AI-based fraud detections are fair, justified and aligned with governance standards.

The study revolves around a key question: can high-performing ensemble models remain trustworthy and transparent when applied to a real-world fraud-detection dataset that is severely imbalanced and anonymized? The authors find that the answer is yes, if machine-learning performance is combined with rigorous interpretability techniques that illuminate how the underlying algorithms arrive at their decisions.

AI models deliver exceptional fraud detection accuracy on an extremely imbalanced dataset

The study examines whether ensemble-learning algorithms can deliver strong detection performance on a dataset where fraud represents far less than one percent of all transactions. The researchers use the well-known European Credit Card Fraud Detection Dataset, which consists of more than 284,000 credit-card transactions, only 492 of which are fraudulent. This imbalance has long challenged traditional rule-based fraud systems, which often produce high numbers of false alarms or miss fraudulent behavior altogether.

To address this disparity, the authors apply the Synthetic Minority Oversampling Technique during training, enabling both Random Forest and XGBoost to learn meaningful patterns from the rare fraud cases. The dataset is divided into an 80–20 split for training and testing, and the authors tune each model extensively using a range of hyperparameters to identify the combination that maximizes performance.

The optimized Random Forest model consists of 500 decision trees operating without depth limits. It demonstrates near-perfect accuracy and strong performance across all key metrics, including precision, recall, F1-score and ROC AUC. The model identifies fraudulent transactions with high confidence, while keeping false positives and false negatives to extremely low levels.

The XGBoost model, tuned separately with optimized learning rate, tree depth and class-weight parameters, performs similarly well. It achieves exceptionally high recall, indicating that it catches more fraud cases than Random Forest, albeit with slightly lower precision. Both models outperform traditional machine-learning and rule-based fraud systems commonly used in the financial sector.

As per the authors, ensemble-learning frameworks offer a significant advantage in imbalanced fraud settings. Their ability to combine multiple weak learners into a strong predictive engine gives them the flexibility to identify subtle anomalies that may not be apparent in simpler models. Yet, despite the strong numerical performance, the researchers argue that raw accuracy is not enough. Financial institutions also need transparent and interpretable outputs to comply with regulations and maintain customer trust.

Addressing trust and governance requirements

The authors investigate how to interpret and validate the predictions made by ensemble models that are often considered “black boxes.” To address this, they use two explainable AI frameworks, SHAP and LIME, to break down the contribution of individual features in each model’s decisions.

Because the dataset’s inputs are principal component analysis features labeled V1 to V28, plus time and amount, the underlying financial variables are anonymized. This makes interpretability methods essential, since investigators and auditors cannot rely on intuitive domain knowledge to understand model behavior.

SHAP, which computes the contribution of each feature to a prediction, identifies consistent patterns across both Random Forest and XGBoost. Features such as V14, V12, and V10 consistently appear as the strongest indicators of fraud. These findings show that fraudulent transactions often exhibit unusual signatures across specific principal-component dimensions, even when those dimensions do not correspond to easily interpretable real-world variables.

LIME, which builds local interpretable models around individual predictions, provides granular insight at the transaction level. It shows which features push the model toward classifying a transaction as fraudulent and which push in the opposite direction. This allows investigators to see the exact reasons behind a model’s decision for any specific case, increasing traceability and accountability.

The authors argue that this interpretability layer is critical. Ensemble models may deliver extremely strong detection performance, but banks cannot rely solely on accuracy metrics. They must be able to explain and justify why an AI system flags a particular transaction. With SHAP and LIME integrated into the fraud-detection pipeline, financial institutions can generate clear, human-readable explanations for each prediction, enabling compliance with emerging AI governance standards and regulatory frameworks requiring transparent decision-making.

Trade-offs between precision, recall and interpretability

While Random Forest and XGBoost both performed exceptionally well, the authors identify meaningful differences that can guide financial institutions in selecting the appropriate model based on their risk tolerance and operational requirements.

Random Forest delivers slightly higher precision, meaning it produces fewer false alarms. This is advantageous in contexts where customer inconvenience from false fraud alerts must be minimized. The model spreads importance weights across several key features, producing a more distributed interpretability profile that may be easier for compliance teams to understand.

XGBoost, on the other hand, provides higher recall, meaning it identifies a greater proportion of fraudulent events. This makes it a strong option for institutions with a low tolerance for undetected fraud. However, its predictions rely more heavily on the top few predictors and may be more sensitive to small variations in feature values.

Both models generalize well to new data and do not display problematic overfitting, despite the high variance in cross-validation F1 scores. This reliability reinforces their suitability for real-world deployment.

Critically, the paper shows that interpretability tools work well on both ensemble approaches. SHAP and LIME reveal consistent and stable patterns in how each model uses the top PCA-based features, allowing financial institutions to understand the rationale behind predictions regardless of the chosen algorithm.

Growing need for interpretable AI in financial security

With fraud attacks becoming increasingly sophisticated, machine-learning systems must evolve quickly. Yet, regulatory frameworks in Europe, North America and Asia are moving toward more stringent requirements for transparency, fairness and auditability in AI-driven decision systems.

The authors argue that financial institutions will face mounting pressure to justify model decisions to auditors, regulators and customers. Traditional fraud-detection models often function as opaque black boxes, making it difficult to verify that decisions are fair or compliant. By incorporating robust interpretability tools, institutions can better meet regulatory expectations and uphold ethical standards.

The authors recommend that institutions adopt hybrid approaches that integrate predictive performance and interpretability, rather than prioritizing one at the expense of the other. As fraud patterns continue to evolve, interpretable ensemble models may become a critical component of secure, trustworthy digital financial infrastructures.