Fraud Detection System for Banking Transactions

Ranya Batsyas; Ritesh Yaduwanshi

cs.LGApr 9, 2026

Fraud Detection System for Banking Transactions

Authors: Ranya Batsyas, Ritesh Yaduwanshi

Organizations: Department of AI DS, IGDTUW, Delhi, India.

Abstract

The expansion of digital payment systems has heightened both the scale and intricacy of online financial transactions, thereby increasing vulnerability to fraudulent activities. Detecting fraud effectively is complicated by the changing nature of attack strategies and the significant disparity between genuine and fraudulent transactions. This research introduces a machine learning-based fraud detection framework utilizing the PaySim synthetic financial transaction dataset. Following the CRISP-DM methodology, the study includes hypothesis-driven exploratory analysis, feature refinement, and a comparative assessment of baseline models such as Logistic Regression and tree-based classifiers like Random Forest, XGBoost, and Decision Tree. To tackle class imbalance, SMOTE is employed, and model performance is enhanced through hyperparameter tuning with GridSearchCV. The proposed framework provides a robust and scalable solution to enhance fraud prevention capabilities in FinTech transaction systems. Keywords: fraud detection, imbalanced data, HPO, SMOTE

Figures & tables

Figure 2: Bar plot showing transaction counts by type, with fraud appearing only in TRANSFER and CASHOUT.

Figure 3: Horizontal bar plot showing the nearly equal split of all fraud cases between TRANSFER (49.86%) and CASHOUT (50.14%) transactions.

Figure 4: Horizontal bar plot demonstrating extreme class imbalance.

Figure 5: Correlation matrix demonstrating relationships between variables.

	Precision	Recall	F1-score	Support
Non-Fraud [0]	1.00	1.00	1.00	635441
Fraud [1]	0.85	0.43	0.57	821
Accuracy			1.00	636262
Macro avg	0.92	0.72	0.79	636262
Weighted avg	1.00	1.00	1.00	636262

Table 1: Logistic regression classification report 0 0 footnotetext: Logistic regression model: [[635377, 64], [467, 354]] 0 0 footnotetext: Accuracy score on Test Data: 0.9991654381371197%

	Precision	Recall	F1-score	Support
Non-Fraud [0]	1.00	1.00	1.00	635441
Fraud [1]	0.74	0.97	0.84	821
Accuracy			1.00	636262
Macro avg	0.87	0.99	0.92	636262
Weighted avg	1.00	1.00	1.00	636262

Table 2: Decision tree classification report 0 0 footnotetext: Decision Tree Classifier Model: [[635153, 288], [22, 799]] 0 0 footnotetext: Accuracy score on Test Data: 0.9995127793267553%

	Precision	Recall	F1-score	Support
Non-Fraud [0]	1.00	1.00	1.00	635441
Fraud [1]	0.97	0.79	0.87	821
Accuracy			1.00	636262
Macro avg	0.98	0.90	0.94	636262
Weighted avg	1.00	1.00	1.00	636262

Table 3: Random forest classification report 0 0 footnotetext: Random forest model: [[635421, 20], [169, 652]] 0 0 footnotetext: Accuracy score on Test Data: 0.9997029525572798%

	Precision	Recall	F1-score	Support
Non-Fraud [0]	1.00	1.00	1.00	635441
Fraud [1]	0.98	0.70	0.82	821
Accuracy			1.00	636262
Macro avg	0.99	0.85	0.91	636262
Weighted avg	1.00	1.00	1.00	636262

Table 4: XGBoost classification report 0 0 footnotetext: XGBoost model: [[635427, 14], [245, 576]] 0 0 footnotetext: Accuracy score on Test Data: 0.9995929349859021%

	Precision	Recall	F1-score	Support
Non-Fraud [0]	1.00	1.00	1.00	635441
Fraud [1]	0.92	0.90	0.91	821
Accuracy			1.00	636262
Macro avg	0.96	0.95	0.95	636262
Weighted avg	1.00	1.00	1.00	636262

Table 5: Random Forest Model Performance 0 0 footnotetext: Random forest model: [[635373, 68], [83, 738]] 0 0 footnotetext: Accuracy score on Test Data: 0.9997626763817421%

Financial Fraud Detection

Financial fraud detection identifies suspicious transactions, accounts, claims, and behavior to prevent losses and guide investigation.

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FinFraudBench: A Heterogeneous Graph Benchmark for Financial Fraud Detection

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The increasing complexity of digital financial systems has reshaped financial fraud detection from isolated transaction classification into relational risk reasoning over interconnected financial entities. This shift has motivated graph-based fraud detection, where models identify fraudulent nodes by exploiting dependencies among customers, cards, merchants, categories, and locations. However, despite rapid progress in graph-based methods, existing public benchmarks remain misaligned with real-world financial systems in two important aspects. First, they often simplify financial ecosystems into homogeneous or single-node-type multi-relational graphs, failing to preserve the multi-entity and multi-relational nature of financial data. Second, they rarely provide large-scale heterogeneous financial graph datasets with realistic operating conditions such as extreme class imbalance and limited label availability, making it difficult to assess the practical effectiveness of current methods. To address these gaps, we present FinFraudBench, a heterogeneous graph benchmark for financial fraud detection. FinFraudBench contains two heterogeneous graph datasets (CreditCard-Fraud and BankTrans-Fraud) with up to 8.99M nodes and 89.23M directed typed edges. Each dataset preserves six financial entity types, fourteen directed edge types, and natural fraud rates that mirror deployment constraints. With these datasets, we establish a standardized evaluation protocol covering both ranking and imbalance-sensitive classification metrics, and evaluate representative baselines. Extensive experiments yield empirical insights into current methods' limitations and suggest promising avenues for future research. FinFraudBench is available at https://anonymous.4open.science/r/FinFraudBench-B002.

Abstract

Financial Fraud Detection

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