Abstract
This white paper describes the concept of Queue Management for different types of Fraud and how it can be leveraged using Analytics for retail banking [1–3]. Not managing queues properly poses challenges in terms of operational efficiency as well as there is cost involved in training fraud reviewers for particular fraud types. Data science is vastly used in banking and finance industry for extracting insightful information which can be used for strategic decisions in fraud management [4–7]. This document explains a methodology to implement this concept using Machine Learning technique of Adaptive Boosting which takes care of multinomial classification problem. Analytics can help in classifying the fraud alerts to most likely queue/types and channelize the alert to relevant investigation team. This helps to optimize the business process by responding to Fraud incident in efficient and effective manner; reduce misclassification cost, providing better customer service by preventing frauds with shorter turnaround time and eventually higher stakeholder value [8–20].
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References
Veeramachaneni, K., Arnaldo, I., et al. (2016). AI2: Training a big data machine to defend. In IEEE 2nd International Conference on Big Data Security on Cloud.
Sankhwar, S., & Pandey, D. (2016). A safeguard against ATM fraud. In IEEE 6th IACC.
Coppolino, L., D’Antonio, S., Formicola, V., et al. (2015). Applying extensions of evidence theory to detect frauds in financial infrastructures.
Hooi, B., Shah, N., Beutel, A., et al. (2016). Birdnest: Bayesian inference for ratings-fraud detection. In SIAM International Conference on Data Mining.
PWC. (2016). Global economic crime survey. https://www.pwc.com/gx/en/services/advisory/forensics/economic-crime-survey.html.
Najafabadi, M. M., Villanustre, F., et al. (2015). Deep learning applications and challenges in big data analytics. Journal of Big Data.
Jiang, M., Beutel, A., Cui, P., Hooi, B., Yang, S., & Faloutsos, C. (2015). A general suspiciousness metric for dense blocks in multimodal data. In ICDM 2015 IEEE International Conference.
Sullivan, R. J. (2014). Controlling security risk and fraud in payment systems. Economic Review—Federal Reserve Bank of Kansas City; Kansas City (Third Quarter), 5–36.
Dal Pozzoloa, A., et al. (2014, August). Learned lessons in credit card fraud detection from a practitioner perspective. Expert Systems with Applications, 41(10).
Halvaiee, N. S., & Akbari, M. K. (2014). A novel model for credit card fraud detection using Artificial Immune Systems. Applied Soft Computing.
Gunnemann, S., Unnemann, N. G., & Faloutsos, C. (2014). Detecting anomalies in dynamic rating data: A robust probabilistic model for rating evolution. In Proceedings of the 20th ACM SIGKDD (International Conference on Knowledge Discovery and Data Mining).
Jiang, M., Cui, P., Beutel, A., Faloutsos, C., & Yang, S. (2014). Inferring strange behavior from connectivity pattern in social networks. In Advances in Knowledge Discovery and Data Mining (pp. 126–138). Springer.
Pavía, J. M., Veres-Ferrer, E. J., & Foix-Escura, G. (2012). Credit card incidents and control systems. International Journal of Information Management.
Soltani, N., Akbari, M. K., & Javan, M. S. (2012). A new user-based model for credit card fraud detection based on artificial immune system. In CSI International Symposium on Artificial Intelligence and Signal Processing (AISP).
Jyotindra, N. D., & Ashok, R. P. (2011). A data mining with hybrid approach based transaction risk score generation model (TRSGM) for fraud detection of online financial transaction. International Journal of Computer Applications, 16.
Krivko, M. (2010). A hybrid model for plastic card fraud detection systems. Expert Systems with Applications, 37.
Cheng, H., Tan, P.-N., Potter, C., & Klooster, S. A. (2009). Detection and characterization of anomalies in multivariate time series. SDM (pp. 413–424). SIAM.
Yue, D., Wu, X., Wang, Y., Li, Y., & Chu, C.-H. (2007). A review of data mining-based financial fraud detection research. In International Conference on Wireless Communications Networking and Mobile Computing (pp. 5514–5517), September 2007, IEEE.
Gao, J., & Tan, P.-N. (2006). Converting output scores from outlier detection algorithms into probability estimates. In Proceedings of the Sixth International Conference on Data Mining, US, IEEE.
Nikam, S. S. (2015). A comparative study of classification techniques in data mining algorithms. Oriental Journal of Computer Science and Technology. http://www.computerscijournal.org/?p=1592.
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Trikha, A., Khant, S.M. (2019). Queue Classification for Fraud Types: Banking Domain. In: Balas, V., Sharma, N., Chakrabarti, A. (eds) Data Management, Analytics and Innovation. Advances in Intelligent Systems and Computing, vol 839. Springer, Singapore. https://doi.org/10.1007/978-981-13-1274-8_20
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DOI: https://doi.org/10.1007/978-981-13-1274-8_20
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