Fraud Detection on Bank Payments Using Machine Learning
Fraud Detection on Bank Payments Using Machine Learning
IEEE BASE PAPER ABSTRACT:
Fraud Detection on Bank Payments Using Machine Learning: This paper consists of fraud detection and measures to automate it fully. For every bank, it has become essential for Fraud detection. Fraud is rising significantly, which ends in many damages for the banks. Transactions create unique challenges for fraud exposure due to the lack of short-term processing. The foremost task is a feasibility study of chosen fraud detection methods. With the help of models, these transactions are to be tested individually and further proceeded. We first define a detection task: attributes of the dataset, the metric choice, and any techniques to control such unbalanced datasets. This leads to the fact that the underlying pattern generating the dataset results: For example, cardholders may improve their purchasing habits over periods, and fraudsters may change their tactics. Later, we highlighted several methods used to obtain the sequential features of credit card transactions.
PROJECT OUTPUT VIDEO:
ALGORITHM / MODEL USED:
Random Forest Classifier.
OUR PROPOSED ABSTRACT:
Fraud Detection on Bank Payments Using Machine Learning: The practice of obtaining financial gains by dishonest and unlawful means is known as financial fraud. Financial fraud, which is defined as the use of dishonest methods to obtain financial gains, has recently grown to be a serious threat to businesses and organizations. Despite several initiatives to curtail financial fraud, it continues to negatively impact society and the economy since daily losses from fraud amount to significant sums of money.
Several methods for detecting fraud were first introduced many years ago. The majority of old procedures are manual, which is not only time-consuming, expensive, and inaccurate, but also unworkable. There are more studies being done, however they are ineffective at reducing losses brought on by fraud. Conventional methods for detecting these fraudulent activities, like human verifications and inspections, are inaccurate, expensive, and time-consuming.
Machine-learning-based technologies can now be used intelligently to identify fraudulent transactions by examining a significant amount of financial data, thanks to the development of artificial intelligence. As a result, this study seeks to offer a novel model of fraud detection on bank payments utilizing the Random Forest Classifier Machine Learning Algorithm.
Our suggested system makes use of the Banksim dataset, and we have demonstrated that it is more effective than the current system by achieving train and test accuracy of 99%.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- Monitor : 15’’ LED.
- Input Devices : Keyboard, Mouse.
- Ram : 4 GB.
SOFTWARE REQUIREMENTS:
- Operating system : Windows 10 / 11.
- Coding Language : Python 3.8.
- Web Framework : FLASK.
- Frontend : HTML, CSS, JavaScript.
REFERENCE:
Pinku Ranjan; Kammari Santhosh; Arun Kumar; Somesh Kumar, “Fraud Detection on Bank Payments Using Machine Learning”, 2022 International Conference for Advancement in Technology (ICONAT), IEEE Conference, 2022.