Real-time Credit Card Fraud Detection Using Machine Learning

In present scenario when the term fraud comes into a discussion, credit card fraud clicks to mind so far. With the great increase in credit card transactions, credit card fraud has increasing excessively in recent years. Fraud detection includes monitoring of the spending behavior of users/ customers in order to determination, detection, or avoidance of undesirable behavior. As credit card becomes the most prevailing mode of payment for both online as well as regular purchase, fraud relate with it are also accelerating. Fraud detection is concerned with not only capturing the fraudulent events, but also capturing of such activities as quickly as possible. The use of credit cards is common in modern day society. Fraud is a millions dollar business and it is rising every year. Fraud presents significant cost to our economy worldwide. Modern techniques based on Data mining, Machine learning, Sequence Alignment, Fuzzy Logic, Genetic Programming, Artificial Intelligence etc., has been introduced for detecting credit card fraudulent transactions. This paper shows how data mining techniques can be combined successfully to obtain a high fraud coverage combined with a low or high false alarm rate.

IRJET, 2022

Credit Card fraud has become increasingly more widespread in ongoing years. A credit Card is the most broadly utilized electronic instalment strategy due to the expanding volume of every day electronic exchanges, making it more helpless against misrepresentation. Credit card organizations are searching for the right innovations and situation to identify and lessening extortion of exchanges on the credit card. The objective is to distinguish and precise misleading extortion recognition. There are a few systems for distinguishing credit card misrepresentation, as neural organizations, Genetic Algorithms, k-means bunching. Consistently misrepresentation cost created in the economy is more than $4 trillion universally. The response lies in depending on cutting edge investigation and undertaking wide information stockpiling abilities that help the utilization of computerized reasoning (AI) and AI (ML) ways to deal with stay one stride in front of lawbreakers. ML abilities, misrepresentation and consistence units can invest their energy dealing with more-complex extortion issues.

International journal of computer applications, 2022

As the world is becoming more digitalized with every sector using the internet to flourish their businesses, online transactions have become an inevitable part of life. There has been a steady rise in the number of online transactions and this will continue to increase in the future as well. One of the major modes of online transactions is credit cards and along with its extensive use comes its major drawback, that is, credit card fraud. Machine learning plays a vital role in detecting credit card frauds as it is not possible for banks to monitor every transaction. This paper explores different machine learning algorithms used to detect credit card frauds.

IJCSMC, 2019

The speedy participation in online primarily based transactional activities raises the fallacious cases everywhere and causes tremendous losses to the personal and financial business. [1] Although several criminal activities are occurring in commercial business, fraudulent e-card activities are among the foremost prevailing and disturbed regarding by online customers. Data processing techniques were used to check the patterns and characteristics of suspicious and non-suspicious transactions supported normalized and anomalies knowledge. On the opposite hand, machine learning (ML) techniques were used to predict the suspicious and non-suspicious transactions mechanically by victimization classifiers [2][5]. This paper discusses the supervised based mostly classification. When preprocessing the dataset using normalization and Principal element Analysis, all the classifiers achieved over 95.0% accuracy compared to results reached before preprocessing the dataset.

The primary aim of this project is to make use of machine learning models for the detection and prevention of fraudulent credit card transactions, to ensure the security of customers' accounts against unauthorized use. With the increase in the rate of credit card fraud, it is necessary to implement measures to fight it effectively, to protect customers from charges for goods and services they did not authorize. To achieve this, four distinct machine learning algorithms: K-Nearest Neighbors (KNN), Logistic Regression, Decision Tree Classifier, and Support Vector Machine (SVM) were implemented, trained, and evaluated using a comprehensive dataset containing credit card transaction records, and the best of these four was selected.

2021

Using new spam technologies to carry out internet banking fraud refers to shifting and withdrawing money from the user’s balance account without it’s authorization. Credit card fraud pops into the mind so far in the current scenario when the concept of fraud bursts into some conversation. Credit card fraud has escalated tremendously in recent times due to the incredible growth in credit card purchases. In order to assess, identify or prevent undesirable conduct, fraud detection requires tracking the purchase behavior of users/customers. The purpose of this project is to predict the genuine and fraud transactions with respect to the amount of the transaction utilizing various machine learning approaches like Logistic Regression, Decision Trees, Support Vector Machine, Naive Bayes, Random Forest and K-Nearest Neighbor. The model built who has greater accuracy and precision is considered to be best fit for this system.

IRJMETS, 2020

The main problems within the credit card trade are ongoing a fraud. The credit card has made our life easy as we can pay easily and move without carrying any cash. Credit card gains its popularity and utilization has dramatically inflated in our day-to-day life, for the speedy advancement of electronic commerce technology. However, exploitation of credit card provides huge edges once used fastidiously and responsibly. Fraud activities are also increasing, and new techniques have been developed by criminals. Credit card and monetary damages are caused by fallacious activities. Such issues are tackled with Data Science, Machine Learning together with Deep Learning techniques, which cannot be exaggerated. This helps the bank and financial organizations, to detect the fraud at the early stage, and then they can reduce the ongoing fraud by not accepting the suspected transactions. The credit card company faces a huge loss if the cardholder does not detect the loss. An awfully very little quantity of data is needed by the assaulter for conducting any fallacious dealing in online transactions. During analysis work, numerous methods and outcome are reviewed, in terms of definite parameters.

2008 7th IEEE International Conference on Cybernetic Intelligent Systems, 2008

Credit card fraud is critical business risk that every stakeholder of financial system including issuer, acquirer etc. has to manage tightly to ensure business continuity and credibility of payment system. As the popularity of the credit card payment as one of the online payment mode is increasing more and more due to the revolution that has taken place in E-commerce sector. Traditional fraud models designed years back deliver near about 70% accuracy and were meeting business needs till this time. However fraudsters are increasing gaming the system to create new types of frauds which has resulted in consistent decrease in model accuracy. The fraudulent transactions and real transactions are scattered all around and there is very little difference to distinguish between them. Many techniques based on Artificial Intelligence, Data mining, Fuzzy logic, Sequence Alignment, Genetic Programming, Machine learning has evolved in detecting various credit card fraudulent transactions. This paper represents how the combinations of different clustering and machine learning algorithm which can best adapt to the changing scenarios taking place can be used and deployed on a very large scale to detect the fraudulent transactions and use to ensure the credibility of the payment system.

A credit card is a convenient tool that allows you to buy items now and pay for them later. Banking Sector involves a lot of transactions for their day to day operation and they have now realized that their main disquietude is how to detect fraud as early as possible. The credit card has become the most popular mode of payment for both online as well as regular purchase. Credit card frauds are increasing day by day regardless of various techniques developed for its detection. Fraud detection systems have become essential for all credit card issuing banks to minimize their losses. The most commonly used fraud detection methods are Artificial Immune System (AIS), Hidden Markov Model (HMM), Neural Network, Genetic Algorithms, Decision Tree and Support Vector Machine (SVM). These techniques can be used alone or in collaboration using ensemble or meta-learning techniques to build classifiers. The main objective of this paper is to review methodology of different detection methods based on credit card in terms of Parameter like Speed of detection, Accuracy and cost the comparison of mentioned approaches based on survey. This paper presents a survey of various techniques used in credit card fraud detection and prevention.