A Comparative Approach to Predictive Analytics with Machine Learning for Fraud Detection of Realtime Financial Data

Project Code :TCMAPY279

Objective

In this project, we are comparing predictive analytics with machine learning techniques for fraud detection in real-time financial data. Although predictive analytics and machine learning uses some of the same algorithms, predictive analytics in certain cases can outperform machine learning since it is designed specifically for the task in hand.

Abstract

Fraud has become a trillion-dollar industry today. Some finance companies have separate domain expert teams and data scientists who are working on identifying fraudulent activities. Data Scientists often use complex statistical models to identify frauds. However, there are many disadvantages to this approach. Fraud detection is not real-time and therefore, in many cases fraudulent activities are identified only after the actual fraud has happened. These methodologies are prone to human errors. In addition, it requires expensive, highly skilled domain expert teams and data scientists. Nevertheless, the accuracy of manual fraud detection methodologies is low and due to that, it is very difficult to handle large volumes of data. More often, it requires time-consuming investigations into the other transactions related to the fraudulent activity in order to identify fraudulent activity patterns.

Keywords: Financial transactions, Fraud, Patterns etc..,

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.