A Combination of Mathematics, Statistics and Machine Learning To Detect Fraud (original) (raw)

Survey on Fraud Detection in banking using Novel Strategy

2020

Now a day’s online payment gaining popularity because of easy and convenience use of ecommerce. It became very easy mode of payment. People choose online payment and e-shopping; because of time convenience, transport convenience, etc. As the result of huge amount of e-commerce use, there is a vast increment in credit card fraud also. Machine Learning has been successfully applied to finance databases to automate analysis of huge volumes of complex data. Machine Learning has also played a salient role in the detection of credit card fraud in online transactions. Fraud detection in credit card is a big problem, it becomes challenging due to two major reasons–first, the profiles of normal and fraudulent behaviors change frequently and secondly due to reason that credit card fraud data sets are highly skewed. KeywordsData Analysis, Fraud in credit card, naïve bayes, Machine Learning, Security.

Emerging Approach for Detection of Financial Frauds Using Machine Learning

2021

The growth of regularly generated data from many financial activities has significant implications for every corner of financial modeling. This study has investigated the utilization of these continuous growing data by a means of an automated process. The automated process can be developed by using Machine learning based techniques that analyze the data and gain experience from the underlying data. Different important domains of financial fields such as Credit card fraud detection, bankruptcy detection, loan default prediction, investment prediction, marketing and many other financial models can be modeled by implementing machine learning models. Among several machine learning based techniques, the use of parametric and non-parametric based methods are approached by this research. Two parametric models namely Logistic Regression, Gaussian Naive Bayes models and two non-parametric methods such as Random Forest, Decision Tree are implemented in this paper. All the mentioned models are...

Study on Fraud Transaction Detection System

International Journal of Advanced Research in Science, Communication and Technology

Fraudulent transactions are very common problem in banking systems internationally, accounting for $5.1 trillion dollars every year. Many financial institutions are facing the common problem of being targeted by transactions of fraudulent nature and its becoming more and more obvious that advanced technology, such as Machine Learning (ML) is needed to counter such acts. Machine learning is the most effective technique against these complex bank frauds when approaches relying on fragmented and siloed data, rules-based approaches or traditional point-solutions are not only costly but also not as effective as needed. Complex algorithms powered by ML can be used to reduce manual investigations in Financial Institutions. Volume of these transactions is huge, lots of current solutions do not focus on big data the proposed model will work on big data with the help of ‘Apache Spark’ using latest machine learning technology. The proposed model will try to find pattern in given data set and f...

IRJET- A Quantitative Analysis to Estimate Transaction Fraud using Machine Learning

IRJET, 2021

Fraud is a highly nefarious and self-centered crime that is happening quite frequently on the various platforms. As the increase in the users has also led to an increase in fraud being committed on the financial portals. The fraud on financial portals is quite varied and is governed by a plethora of parameters that are highly difficult to ascertain. There is a wide variety of researches that facilitates the detection of financial fraud. But most of these approaches have been directed towards credit cards, money laundering, etc. these researches fail to consider the overall attributes specifically. Therefore, to combat this problem, this publication deals with the identification of fraud on a variety of transactions. The proposed system implements innovative concepts such as linear clustering, entropy estimation, and Frequent Itemset Mining along with the Hypergraph, Artificial Neural Networks, and Decision making for identification of transaction fraud.

Investigation of Financial Fraud Detection by Using Computational Intelligence

12th International Scientific Conference “Business and Management 2022”

Due to increasing technical capabilities, financial fraud becomes more sophisticated and more difficult to detect. As there are various categories and typologies of financial fraud, different detection techniques may be applied. However, based on the data generated daily by financial organizations, a technical solution must be implemented. This paper presents a comprehensive literature review of financial fraud, the categorizations of financial fraud, and financial fraud detection with the particular focus on computational intelligence-based techniques. As outlined in the reviewed literature, money laundering is a multilayered crime involving several fraud typologies; therefore, it was selected to be analysed in this research. The purpose of the research is to investigate the synthetic dataset of the money laundering scheme to see whether additional patterns could be outlined, which would help financial organizations to recognize suspicious activity easier. To achieve this goal, com...

Banking Fraud Detection using Python and Machine Learning

Zenodo (CERN European Organization for Nuclear Research), 2023

Credit card companies must have the ability to identify fraudulent credit card transactions in order to stop customers from being charged for goods they did not purchase. Data Science may be used to solve these issues, and coupled with machine learning, it is of utmost relevance. This study aims to demonstrate how machine learning can be used to model a data set by using the detection of credit card fraud as an example. The "Credit Card Fraud Detection Problem" includes modelling prior credit card transactions using data from those that were subsequently shown to be fraudulent. The outcome of this analysis is used to assess if a new transaction is fraudulent or not. The goal here is to minimize inaccurate fraud categories while detecting 100% of the fraudulent transactions. The identification of "credit card fraud" is an excellent illustration of classification. In this process, we have focused on data analysis and preparation. We have also used a number of aberration detection techniques, such as the "Local Outlier Factor" and "Isolation Forest" methods, on PCA transformed "Credit Card Transaction" data.

Performance evaluation of various data mining classification techniques that correctly classify banking transaction as fraudulent

Data mining is a sum of process to find anomalies, patterns, correlations which can assist banks to look for hidden patterns in a group and discover unknown relationship in the data. In this competitive world, governments, private companies, large organizations and all businesses, predict their future plans using various methods of data mining. Banks are an integral part of a country's economy, contributing to both people and governments. In case of money transaction bank is the most essential media in our country. In this circumstance, some vested peoples gratify their evil task. These individual acts make a big issue for our country's economy. In this paper, we have discussed about the comparative study on several data mining classification techniques that are generally used to classify suspicious transactions, included Naïve Bayes, MLP (is the particular feed forward method of ANN), sequential minimal optimization (SMO) and decision tree based J48 & random forest algorithm. we have found the random forest algorithm performance is better than others to classify banking transactions.

Financial Fraud Detection Using Machine Learning

Financial misrepresentation is a consistently developing danger with far results in the money related industry a basic job in the discovery of Visa extortion in online exchanges. Visa misrepresentation identification, which is an information mining issue, winds up testing because of two significant reasons-first, the profiles of ordinary and false practices change continually and also, Visa extortion informational indexes are exceptionally slanted. The exhibition of misrepresentation discovery in MasterCard exchanges is incredibly influenced by the inspecting approach on dataset, determination of factors and recognition technique(s) utilized.

A Comparative Study on Online Transaction Fraud Detection by using Machine Learning and Python

IRJET, 2022

The issue of transaction fraud could be a major source of concern. Because online transactions are becoming more widespread, the prevalence of online transaction fraud is increasing, which has a detrimental influence on the financial industry. Using a real user's MasterCard details, this fraud detection system can restrict and hinder an attacker's transaction. This solution was intended to solve these problems by allowing customers to make transactions that exceed their existing transaction limit. To detect fraudulent user behavior, we collect the necessary information upon registration. Every fraud detection system (FDS) at a bank that issues credit cards to cardholders is usually blind to the fine print of a transaction. BLA is being used to tackle this issue (Behavior and site Analysis). Every pending transaction is sent to the FDS for approval. To determine whether or whether the transaction is genuine, FDS receives the cardboard information as well as the transaction value. Keyword-Fraud detection, fully connected neural networks, online transactions, long bidirectional gated repeating unit and long bidirectional memory (Belts), KNN, NB, SVM, and so on.

AI-Driven Fraud Detection in Banking: Enhancing Transaction Security

Journal of Informatics Education and Research, 2024

An important step forward in risk management and fraud detection has been achieved with the integration of Artificial Intelligence (AI) in the banking sector. In this paper, we take a look at how AI has revolutionized various fields, shedding light on the benefits and drawbacks of this technology. The effects of AI on risk management are complex. More complex credit risk assessment models are made possible by algorithms that can see patterns in massive datasets that people might miss. When it comes to market and liquidity issues, real-time transaction monitoring is absolutely essential for quick risk mitigation. Automating compliance with regulatory norms is another critical function of AI, which helps to decrease human mistake and assures quick adaptability to changes in regulations. The automation of mundane processes and the reinforcement of cybersecurity measures further reduce operational risks. By examining client behaviour and transaction data, enhanced algorithms may adeptly spot anomalies that could indicate fraud. Artificial intelligence's capacity to foresee future events enables it to foil possible fraud attempts. The systems are designed to respond to changing fraudster strategies with its adaptive learning feature.