- 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.
- 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.
- 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.
- Front End – Anaconda IDE
- Backend – SQL
- Language – Python 3.8
- •Hard Disk: Greater than 500 GB
- •RAM: Greater than 4 GB
- •Processor: I3 and Above
- * Base Paper
- * Complete Source Code
- * Complete Documentation
- * Complete Presentation Slides
- * Flow Diagram
- * Database File
- * Screenshots
- * Execution Procedure
- * Readme File
- * Addons
- * Video Tutorials
- * Supporting Softwares
- * 24/7 Support * Ticketing System
- * Voice Conference
- * Video On Demand
- * Remote Connectivity
- * Code Customization
- * Document Customization
- * Live Chat Support