• 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.


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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