A novel approach to increase scalability while training machine learning algorithms using Bfloat 16 in credit card fraud detection

• URL: http://arxiv.org/abs/2206.12415v1
• Date: Fri, 24 Jun 2022 01:22:17 GMT
• Title: A novel approach to increase scalability while training machine learning algorithms using Bfloat 16 in credit card fraud detection
• Authors: Bushra Yousuf, Rejwan Bin Sulaiman, Musarrat Saberin Nipun
• Abstract summary: This research focuses on machine learning scalability for banks' credit card fraud detection systems. We have compared the existing machine learning algorithms and methods that are available with the newly proposed technique. The goal is to prove that using fewer bits for training a machine learning algorithm will result in a more scalable system, that will reduce the time and will also be less costly to implement.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The use of credit cards has become quite common these days as digital banking has become the norm. With this increase, fraud in credit cards also has a huge problem and loss to the banks and customers alike. Normal fraud detection systems, are not able to detect the fraud since fraudsters emerge with new techniques to commit fraud. This creates the need to use machine learning-based software to detect frauds. Currently, the machine learning softwares that are available focuses only on the accuracy of detecting frauds but does not focus on the cost or time factors to detect. This research focuses on machine learning scalability for banks' credit card fraud detection systems. We have compared the existing machine learning algorithms and methods that are available with the newly proposed technique. The goal is to prove that using fewer bits for training a machine learning algorithm will result in a more scalable system, that will reduce the time and will also be less costly to implement.

Related papers

• Credit Card Fraud Detection: A Deep Learning Approach [4.0361765428523135]
  Substantial amount of money has been lost by many institutions and individuals due to fraudulent credit card transactions. This paper aims to understand & implement Deep Learning algorithms in order to obtain a high fraud coverage with very low false positive rate.
  arXiv  Detail & Related papers  (2024-09-20T11:13:16Z)
• Time-Aware Face Anti-Spoofing with Rotation Invariant Local Binary Patterns and Deep Learning [50.79277723970418]
  imitation attacks can lead to erroneous identification and subsequent authentication of attackers. Similar to face recognition, imitation attacks can also be detected with Machine Learning. We propose a novel approach that promises high classification accuracy by combining previously unused features with time-aware deep learning strategies.
  arXiv  Detail & Related papers  (2024-08-27T07:26:10Z)
• Verification of Machine Unlearning is Fragile [48.71651033308842]
  We introduce two novel adversarial unlearning processes capable of circumventing both types of verification strategies. This study highlights the vulnerabilities and limitations in machine unlearning verification, paving the way for further research into the safety of machine unlearning.
  arXiv  Detail & Related papers  (2024-08-01T21:37:10Z)
• Online Corrupted User Detection and Regret Minimization [49.536254494829436]
  In real-world online web systems, multiple users usually arrive sequentially into the system. We present an important online learning problem named LOCUD to learn and utilize unknown user relations from disrupted behaviors. We devise a novel online detection algorithm OCCUD based on RCLUB-WCU's inferred user relations.
  arXiv  Detail & Related papers  (2023-10-07T10:20:26Z)
• Credit Card Fraud Detection with Subspace Learning-based One-Class Classification [18.094622095967328]
  One-Class Classification (OCC) algorithms excel in handling imbalanced data distributions. These algorithms integrate subspace learning into the data description. These algorithms transform the data into a lower-dimensional subspace optimized for OCC.
  arXiv  Detail & Related papers  (2023-09-26T12:26:28Z)
• Using Machine Learning To Identify Software Weaknesses From Software Requirement Specifications [49.1574468325115]
  This research focuses on finding an efficient machine learning algorithm to identify software weaknesses from requirement specifications. Keywords extracted using latent semantic analysis help map the CWE categories to PROMISE_exp. Naive Bayes, support vector machine (SVM), decision trees, neural network, and convolutional neural network (CNN) algorithms were tested.
  arXiv  Detail & Related papers  (2023-08-10T13:19:10Z)
• Transaction Fraud Detection via an Adaptive Graph Neural Network [64.9428588496749]
  We propose an Adaptive Sampling and Aggregation-based Graph Neural Network (ASA-GNN) that learns discriminative representations to improve the performance of transaction fraud detection. A neighbor sampling strategy is performed to filter noisy nodes and supplement information for fraudulent nodes. Experiments on three real financial datasets demonstrate that the proposed method ASA-GNN outperforms state-of-the-art ones.
  arXiv  Detail & Related papers  (2023-07-11T07:48:39Z)
• Challenges and Complexities in Machine Learning based Credit Card Fraud Detection [0.0]
  Volume of transactions, uniqueness of frauds and ingenuity of the fraudster are main challenges in detecting frauds. The advent of machine learning, artificial intelligence and big data has opened up new tools in the fight against frauds. However, the developments in fraud detection algorithms has been challenging and slow due to the massively unbalanced nature of fraud data.
  arXiv  Detail & Related papers  (2022-08-20T07:53:51Z)
• Credit Card Fraud Detection using Machine Learning: A Study [2.5829043503611318]
  The world is rapidly moving towards digitization and money transactions are becoming cashless. We need to analyze and detect the fraudulent transaction from the non-fraudulent ones. We present a comprehensive review of various methods used to detect credit card fraud.
  arXiv  Detail & Related papers  (2021-08-23T08:30:24Z)
• Deep Learning Methods for Credit Card Fraud Detection [3.069837038535869]
  This paper presents a study of deep learning methods for the credit card fraud detection problem. It compares their performance with various machine learning algorithms on three different financial datasets. Experimental results show great performance of the proposed deep learning methods against traditional machine learning models.
  arXiv  Detail & Related papers  (2020-12-07T14:48:58Z)
• Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection [66.05992706105224]
  In this paper, an effective anomaly detection framework is proposed utilizing Bayesian Optimization technique. The performance of the considered algorithms is evaluated using the ISCX 2012 dataset. Experimental results show the effectiveness of the proposed framework in term of accuracy rate, precision, low-false alarm rate, and recall.
  arXiv  Detail & Related papers  (2020-08-05T19:29:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.