TimeTrail: Unveiling Financial Fraud Patterns through Temporal Correlation Analysis
In the field of financial fraud detection, understanding the underlying patterns and dynamics is important to ensure effective and reliable systems. This research introduces a new technique, "TimeTrail," which employs advanced temporal correlation analysis to explain complex financial fraud patterns. The technique leverages time-related insights to provide transparent and interpretable explanations for fraud detection decisions, enhancing accountability and trust. The "TimeTrail" methodology consists of three key phases: temporal data enrichment, dynamic correlation analysis, and interpretable pattern visualization. Initially, raw financial transaction data is enriched with temporal attributes. Dynamic correlations between these attributes are then quantified using innovative statistical measures. Finally, a unified visualization framework presents these correlations in an interpretable manner. To validate the effectiveness of "TimeTrail," a study is conducted on a diverse financial dataset, surrounding various fraud scenarios. Results demonstrate the technique's capability to uncover hidden temporal correlations and patterns, performing better than conventional methods in both accuracy and interpretability. Moreover, a case study showcasing the application of "TimeTrail" in real-world scenarios highlights its utility for fraud detection.
Code (0)
등록된 구현이 없습니다.
Tasks
Fraud DetectionSimilar Papers 제목 키워드 기반
Fighting Accounting Fraud Through Forensic Data Analytics
Accounting fraud is a global concern representing a significant threat to the financial system stability due to the resulting diminishing of the market confidence and trust of regulatory authorities. Several tricks can b…
Detecting Financial Fraud with Hybrid Deep Learning: A Mix-of-Experts Approach to Sequential and Anomalous Patterns
Financial fraud detection remains a critical challenge due to the dynamic and adversarial nature of fraudulent behavior. As fraudsters evolve their tactics, detection systems must combine robustness, adaptability, and pr…
Fraud DetectionMixture-of-ExpertsGraph Neural Networks for Financial Fraud Detection: A Review
The landscape of financial transactions has grown increasingly complex due to the expansion of global economic integration and advancements in information technology. This complexity poses greater challenges in detecting…
Fraud DetectionAiding Humans in Financial Fraud Decision Making: Toward an XAI-Visualization Framework
AI prevails in financial fraud detection and decision making. Yet, due to concerns about biased automated decision making or profiling, regulations mandate that final decisions are made by humans. Financial fraud investi…
Decision MakingFraud DetectionCoDetect: Financial Fraud Detection With Anomaly Feature Detection
Financial fraud, such as money laundering, is known to be a serious process of crime that makes illegitimately obtained funds go to terrorism or other criminal activity. This kind of illegal activities involve complex ne…
Anomaly DetectionFraud Detection