ANOMALY DETECTION SYSTEM IN BANKING TRANSACTIONS USING A MACHINE LEARNING MODEL BASED ON GRAPH NEURAL NETWORK

Graph machine learning and fraud detection systems are growing and popular today. Fraud detection systems have been widely used as a tool to detect potentially fraudulent transactions. Fraud detection systems can be used to determine patterns of transactions that are suspected of being criminal t...

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Main Author: Aziz Bukhori, Hilmi
Format: Theses
Language:Indonesia
Online Access:https://digilib.itb.ac.id/gdl/view/73912
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Institution: Institut Teknologi Bandung
Language: Indonesia
id id-itb.:73912
spelling id-itb.:739122023-06-25T06:47:23ZANOMALY DETECTION SYSTEM IN BANKING TRANSACTIONS USING A MACHINE LEARNING MODEL BASED ON GRAPH NEURAL NETWORK Aziz Bukhori, Hilmi Indonesia Theses banking fraud detection, graph-based fraud detection, classification, tree-based fraud detection , inference time INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/73912 Graph machine learning and fraud detection systems are growing and popular today. Fraud detection systems have been widely used as a tool to detect potentially fraudulent transactions. Fraud detection systems can be used to determine patterns of transactions that are suspected of being criminal transactions. Graph machine learning development can be implemented in anything that can be represented in graph form. The banking fraud detection system can be implemented in graph form by connecting customers who have made transactions with other customers or customer transactional activities. From the graph that has been formed, predictions will be made so that new transactions can be classified as fraudulent transactions or not by connecting these transactions with the graphs that have been made. The experimental results show that the graph-based fraud detection model produces better performance than the tree-based model, but with a longer inference time. to try to reduce the inference time generated in the graph model, the authors also do sampling with importance sampling method. The results shown, with accuracy that is still higher than the tree-based model, sampling can reduce the value of inference time in the graph model, although it is still at a higher value than the tree-based model. Based on the experimental results, the best AUC Score was produced by the GraphSage model with an AUC of 0.997, but based on the fastest inference time, it was produced by LightGBM from a tree-based model, namely 3.96 ms. With the implementation of importance sampling, the resulting AUC is 0.994 with an inference time of 503 ms. text
institution Institut Teknologi Bandung
building Institut Teknologi Bandung Library
continent Asia
country Indonesia
Indonesia
content_provider Institut Teknologi Bandung
collection Digital ITB
language Indonesia
description Graph machine learning and fraud detection systems are growing and popular today. Fraud detection systems have been widely used as a tool to detect potentially fraudulent transactions. Fraud detection systems can be used to determine patterns of transactions that are suspected of being criminal transactions. Graph machine learning development can be implemented in anything that can be represented in graph form. The banking fraud detection system can be implemented in graph form by connecting customers who have made transactions with other customers or customer transactional activities. From the graph that has been formed, predictions will be made so that new transactions can be classified as fraudulent transactions or not by connecting these transactions with the graphs that have been made. The experimental results show that the graph-based fraud detection model produces better performance than the tree-based model, but with a longer inference time. to try to reduce the inference time generated in the graph model, the authors also do sampling with importance sampling method. The results shown, with accuracy that is still higher than the tree-based model, sampling can reduce the value of inference time in the graph model, although it is still at a higher value than the tree-based model. Based on the experimental results, the best AUC Score was produced by the GraphSage model with an AUC of 0.997, but based on the fastest inference time, it was produced by LightGBM from a tree-based model, namely 3.96 ms. With the implementation of importance sampling, the resulting AUC is 0.994 with an inference time of 503 ms.
format Theses
author Aziz Bukhori, Hilmi
spellingShingle Aziz Bukhori, Hilmi
ANOMALY DETECTION SYSTEM IN BANKING TRANSACTIONS USING A MACHINE LEARNING MODEL BASED ON GRAPH NEURAL NETWORK
author_facet Aziz Bukhori, Hilmi
author_sort Aziz Bukhori, Hilmi
title ANOMALY DETECTION SYSTEM IN BANKING TRANSACTIONS USING A MACHINE LEARNING MODEL BASED ON GRAPH NEURAL NETWORK
title_short ANOMALY DETECTION SYSTEM IN BANKING TRANSACTIONS USING A MACHINE LEARNING MODEL BASED ON GRAPH NEURAL NETWORK
title_full ANOMALY DETECTION SYSTEM IN BANKING TRANSACTIONS USING A MACHINE LEARNING MODEL BASED ON GRAPH NEURAL NETWORK
title_fullStr ANOMALY DETECTION SYSTEM IN BANKING TRANSACTIONS USING A MACHINE LEARNING MODEL BASED ON GRAPH NEURAL NETWORK
title_full_unstemmed ANOMALY DETECTION SYSTEM IN BANKING TRANSACTIONS USING A MACHINE LEARNING MODEL BASED ON GRAPH NEURAL NETWORK
title_sort anomaly detection system in banking transactions using a machine learning model based on graph neural network
url https://digilib.itb.ac.id/gdl/view/73912
_version_ 1822993419949047808