Enhancing financial fraud detection in digital finance applications through machine learning algorithms and real-time data analytics

The rapid evolution of financial fraud in digital finance applications—such as mobile banking, cryptocurrency transactions, and online payment gateways—has rendered traditional rule-based detection systems increasingly ineffective, leading to heightened financial losses and security vulnerabilities. These systems struggle to adapt to complex fraudulent schemes, resulting in inefficiencies in identifying such activities. This research introduces a Scalable Black Widow-driven Gradient Boosting Machine (SBW-GBM) framework designed to enhance fraud detection through real-time data analysis and machine learning (ML). The framework evaluates performance in both decentralized finance and traditional financial contexts, utilizing two separate datasets. The first dataset is based on an Ethereum Phishing Transaction Network; the second originates from Kaggle and pertains to Credit Risk Assessment. Data preparation involves addressing missing values, normalizing numerical features, and employing outlier detection techniques to improve data quality. For feature extraction, Principal Component Analysis (PCA) reduces data dimensionality while preserving critical information regarding transaction behaviors. The classification employs Gradient Boosting Machine (GBM) for high predictive accuracy, with the SBW algorithm dynamically fine-tuning GBM hyperparameters to enhance efficiency. Inspired by black widow spiders, the SBW algorithm optimizes hyperparameter selection by eliminating weak solutions and reinforcing stronger ones. This results in an adaptive fraud detection model trained on labeled transaction data. Experimental results confirm that SBW-GBM achieves an F1-score of 0.899, an accuracy of 0.948, an error rate of 0.128, and an experimental runtime of 0.40 seconds on Dataset 1, outperforming the baseline FFSVM. For Dataset 2, SBW-GBM attains a precision of 0.875, a recall of 0.660, an F1-score of 0.750, and an AUC of 0.932, surpassing benchmark traditional classifiers. By continuously learning from new transaction patterns, the proposed framework ensures adaptability to emerging threats and supports real-time fraud detection.