ANALYSIS OF DATA ENGINEERING FOR FRAUD DETECTION USING MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE TECHNOLOGIES
Sandeep Rangineni, Divya Marupaka, Peer-Reviewed, F Angiulli, C Pizzuti, B Baesens, T Van Gestel, S Viaene, M Stepanova, J Suykens, J Vanthienen, A Bahnsen, D Aouada, A Stojanovic, B Ottersten, L Barabesi, A Cerasa, A Cerioli, D Perrotta, S Barua, M Islam, X Yao, K Murase, S Bhattacharyya, S Jha, K Tharakunnel, J Westland, K Boudt, P Rousseeuw, S Vanduffel, T Verdonck, M Breunig, H Kriegel, R Ng, J Sander, M Brito, E Chvez, A Quiroz, J Yukich, R Carroll, D Ruppert, N Chawla, K Bowyer, L Hall, W Kegelmeyer, T Chen, C Guestrin, A Dal Pozzolo, O Caelen, Y Le Borgne, S Waterschoot, G Bontempi, L Davies, U Gather, J Davis, M Goadrich, Dr, Naveen Prasadula, T Fawcett
Abstract
Because identifying possibly fraudulent transactions is a binary classification issue, several methods may be utilized to achieve this objective.Machine learning Artificial intelligence and the blockchain are all examples of such developments.Techniques such as AI, ML, and DL (deep learning) fall under this umbrella.When seen through the lens of machine learning, this idea becomes clear.However, the model has to be clear and easy to comprehend so that it can inspire confidence in management and lead to the creation of fraud prevention strategies.It would be much easier to evaluate suspicious transactions if there were models that allowed fraud experts to understand why a certain instance was flagged as such.As a result, it would be far less difficult to investigate any fraudulent financial dealings.Potentially suspicious transaction reviews would be made much easier with this modification.Therefore, we propose a variety of data engineering options for improving an analytical model's performance while keeping its interpretability intact.Our data engineering process consists of many phases, each of which is dedicated to a different aspect of feature and instance engineering.