AI is utilized in banking for tasks like fraud detection, customer service, and risk assessment. It employs machine learning algorithms and natural language processing to enhance security, automate routine processes, and personalize customer experiences, improving operational efficiency and enhancing overall banking services.
This research paper introduces an ensemble learning model, combining extreme gradient boosting (XGBoost) and random forest (RF) algorithms, to optimize bank marketing strategies. By leveraging financial datasets, the model demonstrates superior accuracy, achieving a 91% accuracy rate and outperforming other algorithms, leading to substantial sales growth (25.67%) and increased customer satisfaction (20.52%). The study provides valuable insights for banking decision-makers seeking to enhance marketing precision and customer relationships.
This research addresses the challenge of customer churn in the banking sector using Genetic Algorithm eXtreme Gradient Boosting (GA-XGBoost). The study emphasizes the significance of techniques like SMOTEENN in handling data imbalances and introduces the SHAP interpretation framework for model interpretability. The optimized GA-XGBoost model proves effective in predicting customer churn, offering valuable insights for proactive customer retention strategies in the dynamic banking landscape.
Researchers from Nanjing University of Science and Technology present a novel scheme, Spatial Variation-Dependent Verification (SVV), utilizing convolutional neural networks and textural features for handwriting identification and verification. The scheme outperforms existing methods, achieving 95.587% accuracy, providing a robust solution for secure handwriting recognition and authentication in diverse applications, including security, forensics, banking, education, and healthcare.
Researchers delve into the intricate relationship between speech pathology and the performance of deep learning-based automatic speaker verification (ASV) systems. The research investigates the influence of various speech disorders on ASV accuracy, providing insights into potential vulnerabilities in the systems. The findings contribute to a better understanding of speaker identification under diverse conditions, offering implications for applications in healthcare, security, and biometric authentication.
Researchers introduce SynthAML, the first publicly available synthetic dataset for studying critical challenges in anti-money laundering (AML). This dataset, created using innovative synthesis techniques, addresses issues like efficiency, effectiveness, class imbalance, concept drift, and interpretability, offering a platform for standardized assessment and academic research in the AML domain.
Researchers introduce the "general theory of data, artificial intelligence, and governance," offering fresh insights into the complexities of the data economy and its implications for digital governance. Their model, which incorporates data flows, knowledge concentration, and data sharing, provides a foundation for addressing the challenges of data capitalism and shaping equitable and innovative data policies in the digital age.
ThreatAdvice, a cybersecurity firm headquartered in Birmingham, Ala., has introduced TAFraudSentry, a fraud deterrence platform utilizing state-of-the-art artificial intelligence and image analysis techniques.
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