ABSTRACT

• Increase in UPI usage for online payments
• cases of fraud associated with it are also rising.
• Few steps involving UPI transaction process using a Hidden Markov Model (HMM)
• how it can be used for the detection of frauds.
•  An HMM is initially trained for a  cardholder.
• If a UPI transaction is not accepted by the trained HMM. It is considered to be fraudulent.
• People can use UPIs for online transactions as it provides an efficient and easy-to-use facility.
• With the increase in usage of UPIs, the capacity of UPI misuse has also enhanced.
•  UPI frauds cause significant financial losses for both UPI holders and financial companies.
•  In this Project, the main aim is to detect such frauds, including the accessibility of public data, high-class imbalance data, the changes in fraud nature, and high rates of false alarm.
• The main focus has been to apply the recent development of Machine Learning algorithms for this purpose.
• We have created 5 Algorithms to detection the UPI Fraud and evaluated results Based on that.

EXISTING SYSTEM

• To detect counterfeit transactions, three machine-learning algorithms were presented and implemented.
• There are many measures used to evaluate the performance of classifiers or predictors, such as the Gradient Boost Classifier, Vector Machine, Random Forest, and Decision Tree.
• These metrics are either prevalence dependent or prevalence-independent.
• Furthermore, these techniques are used in UPI fraud detection mechanisms, and the results of these algorithms have been compared.

PROPOSED SYSTEM

• Various modern techniques like artificial neural network
• Different machine learning algorithms are compared, including Auto Encoder, Local Outlier Factor, Kmeans Clustering.
• This project uses various algorithm, and neural network which comprises of techniques for finding optimal solution for the problem and implicitly generating the result of the fraudulent transaction.
•  The main aim is to detect the fraudulent transaction and to develop a method of generating test data.
•  This algorithm is a heuristic approach used to solve high complexity computational problems.
• The implementation of an efficient fraud detection system is imperative for all UPI issuing companies and their clients to minimize their losses.

PROJECT VIDEO

Software Requirements:

• Front End – Anaconda IDE
• Backend – SQL
• Language – Python 3.8

Hardware Requirements

• •Hard Disk: Greater than 500 GB
• •RAM: Greater than 4 GB
• •Processor: I3 and Above

Including Packages

=======================

• * Base Paper
• * Complete Source Code
• * Complete Documentation
• * Complete Presentation Slides
• * Flow Diagram
• * Database File
• * Screenshots
• * Execution Procedure
• * Readme File
• * Addons
• * Video Tutorials
• * Supporting Softwares

Specialization =======================

• * 24/7 Support * Ticketing System
• * Voice Conference
• * Video On Demand 
• * Remote Connectivity
• * Code Customization
• * Document Customization 
• * Live Chat Support

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