Analysis Fraud (original) (raw)

A Combination of Mathematics, Statistics and Machine Learning To Detect Fraud

Bangladesh Mathematical Society National Mathematics Conference, 2018

Fraud detection which is a discussible phenomenon to many bounds together with financial sectors, banking, insurance as well as diverse forms of industries. Nowadays fraud endeavors are being amplifying with rampant pace especially via the development of technology, so building fraud discovery more significant than ever before. In this paper, we analyzed the fraud operations by managing "Banking Fraud Detection" database by the combination of mathematical, statistical as well as machine learning ways and tried to spectacle a comparison among this ways. We narrated the elementary explorations by the combining methods such as mathematical, statistical as well as machine learning to equip the ways of insidious banking transactions.

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.

The Study of Fraud Detection in Financial and Credit Institutions with Real Data

Global journal of computer science and technology, 2015

This paper presents a review of data mining techniques for the fraud detection. Development of information systems such as data due to it has become a source of important organizations. Method and techniques are required for efficient access to data, sharing the data, extracting information from data and using this information. In recent years, data mining technology is an important method that it has changed to extract concepts from the data set. Scientific data mining and business intelligence technology is as a valuable and some what hidden to provide large volumes of data. This research studies using service analyzes software annual transactions related to 20000 account number of financial institutions in the country.The main data mining techniques used for financial fraud detection (FFD) are logistic models, neural networks and decision trees, all of which provide primarysolutions to the problems inherent in the detection and classification of fraudulent data. The proposed meth...

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...

A Machine Learning-based System for Financial Fraud Detection

Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021)

Companies created for money-laundering or as a means for taxevasion are harmful to the country's economy and society. This problem is usually tackled by governmental agencies by having officials to pore over companies' financial data and to single out those that exhibit fraudulent behavior. Such work tends to be slow-paced and tedious. This paper proposes a machine learning-based system capable of classifying whether a company is likely to be involved in fraud or not. Based on financial and tax data from various companies, four different classifiers – k-Nearest Neighbors, Random Forest, Support Vector Machine (SVM), and a Neural Network – were trained and then used to indicate fraud. The best-performing model achieved a macro-averaged F1-score of 92.98% with the Random Forest.

An Efficient Study of Fraud Detection System Using Ml Techniques

2020

The growing world has the transactions of finance mostly done by the transfer of amount through the cashless payments over the Internet. This growth of transactions led to the large amount of data which resulted in the creation of big data. The day-by-day transactions increase continuously which explored as big data with high speed, beyond the limit of transactions and variety. The fraudsters can also use anything to affect the systematic working of current fraud detection system (FDS). So, there is a challenge to improve the present FDS with maximum possible accuracy to fulfill the need of FDS. When the payment is made by using the credit cards, there is chance of misusing the credit cards by the fraudsters. Now, it is essential to find the system that detects the fraudulent transactions as a real-world challenge for FDS and report them to the corresponding people/organization to reduce the fraudulent rate to a minimal one. This paper gives an efficient study of FDS for credit card...

Enhancing Performance of Financial Fraud Detection Through Machine Learning Model

Journal of Contemporary Education Theory & Artificial Intelligence, 2023

Despite attempts to reduce it, financial fraud continues to be a major problem in many industries, including healthcare, banking, and insurance. Traditional fraud detection techniques, which are often manual, are inefficient, time-consuming, and costly. As a result, methods that use AI and ML have been implemented to improve fraud detection procedures. This study examines the application of ML algorithms for credit card fraud detection using a dataset consisting of 284,807 transactions made by European cardholders in 2013, out of which 492 were fraudulent. Preprocessing steps, including Label Encoding, SMOTE for handling class imbalance, and PCA for feature reduction, were applied to the dataset. On the training dataset have applied ML based classification models like DT, SVM, and ANNs were employed to evaluate their performance. The models were assessed using accuracy, precision, and recall as key metrics. The ANN model emerged as the best-performing model, achieving 98.41%precision, 98.69%accuracy, and 98.98%recall, outperforming both Decision Trees and SVM. This study highlights the effectiveness of ML models, particularly ANNs, in improving financial fraud detection.

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...

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.

Fraud Detection in a Financial Payment System

Human Interaction, Emerging Technologies and Future Applications III, 2020

Many financial payment systems have to face fraudulent activities due to the fast-paced development of the technology. Fraud detection is essential for the proper management of fraud control. It automates the manual checking processes and helps the detection be done conveniently. It is important to research and find ways and means of proper methodologies which will help serve the purpose of fraud detection effectively. Machine Learning Approach becomes more popular and accurate compared to a rule-based approach in this scenario. This paper presents such a performance comparison among a few methods which were tested with a dataset.