The key features mimic those of the model that lacks graph features; however, it's been enriched with graph metrics. Additionally, the relevance of each feature sees a significant boost compared to their counterparts in the base model. This suggests that including fewer graph features can augment the model's accuracy and insight, offering a greater yield than a wide range of features used in a model without graph elements
Three-Level Network Graph for Fraud Transactions
Customer: 4378993458389626
Customer: 3575540972310993 shopping_net
personal_care
shopping_pos
fraud_kerluke-Abshire
fraud_Con Greenholt, O’Hara and Balistreri fraud_Streich,
fraud_Volkman Ltd
fraud_Reichel LLC
misc_pos
fraud_jacobi and Sons
misc_fraud_Bemier_Volkman and Hoeger
fraud_halley Group
fraud_Hamill-Daugherty
shopping_net
3575540972310993
fraud_Miller-Harris
4378993458389626fraud_Labadie, Treutel and Bode
fraud_Herman, Treutel and Dickens
fraud_Huel-Langworth
misc_net
fraud_DuBuque LLC
fraud_Miller-Harris
fraud_Mosciski, Gislason and Mertz
fraud_DuBuque LLC
fraud_Luettgen PLC fraud_Raynon, Feest and Miller
fraud_Bins-Rice
fraud_Hickle Group
grocery_pos
Customer Credit Card Number Merchant Name Category
shopping_pos
grocery_pos
gas_transport
The tri-level network graphs mapping fraudulent transactions (illustrated above) provide a striking visualization of relationships between customers, fraudulent merchants, and corresponding categories for two customers. This graphical representation is instrumental in pinpointing patterns, deciphering connections, and distinguishing clusters within fraudulent transactions. The inclusion of graph features also enables us to analyze the correlation between the target and the graphical attributes. Integrating these graph features has enhanced precision and accuracy, outperforming the model that lacks graph features. The model's performance could be further amplified by leveraging the power of community analysis, suggesting promising avenues for future optimization. The final hurdles As with any emerging technology, several challenges must be addressed before graph-based fraud detection methods can be widely adopted. These include: • High computation time: The process of computing graph features can be time-consuming, mainly if the data set is large. • Data quality: Sparse data and missing information can introduce complexities in creating graphs or network features. • Graph network visualization: Plotting a graph network can be extremely challenging when dealing with a dense network or a large data set. • Domain expertise: A strong foundation in the subject matter is a crucial prerequisite for identifying network structure and determining relationships.
© 2023 Fractal Analytics Inc. All rights reserved
07
Made with FlippingBook - PDF hosting