Fraud detection using Machine Learning

Published on 21 Mar 2022. Written by Amitesh Kumar

Machine learning refers to the intelligence which the computer learns by itself to take some decisions on its own. The system which we will work is on the fraud detection project which involves machine learning concepts. Machine learning concepts have now been used in every field such as commerce, medical, banking, insurance, etc. The project aims to detect anything frauds while shopping online, or doing transaction. The system will read the malicious pattern and then display it to the administrator.

Read more..

SLNOTE


Procedure description

There are a lot of algorithms used for detecting frauds which are as follows: -

• Rule-based or traditional approach in fraud detection: This approach is based on the algorithms which are written by the fraud analyst. The whole algorithm is based on their expertise and experience and let’s be real it can be expensive to hire a well-trained hacker.
• ML based approach to fraud detection: This approach is quite easy and accurate to include for this project. The system needs to be stay updated as the fraudsters approach new ways to breaking into your system. Through machine learning, we can build strict rules to bind the system. As machine learning is based on training the system with new data all the time, it can help the system to track any malicious activity.

The flow of the project is as follow:

• Feeding data
• Extracting features
• Training algorithms
• Creating a model

SLLATEST
The first step is the feeding data process which is the most crucial part of the project as the machine will learn better with huge and improved datasets. This will train our system and it will be very helpful.

Then comes the extraction features a process which basically works on different threads and the information associated with them. The threads can be anything of vital information such as location, identity, mode of payment, network, etc. The information with all these factors is very important and should be concealed from any unauthorized access.

Then we will need a proper algorithm which you can learn at Skyfi Labs and then train your system with many customers data. More the data more will the accuracy. Then, at last, we will need a model to work properly with the proposed algorithm. The algorithm will contain concepts such as matrix confusion, selectKBest features; Mean squared error function, Gaussian naïve Bayes algorithm, and linear regression. All these concepts will be taught to you whom you can apply in many other projects.  

The following project will be build using the python language and to know more about this course you can get this training at Skyfi Labs.

Conclusion

The system if made properly will be used to detect any dangerous activity on your computer while you are surfing the internet. The developer should possess knowledge about python language and some basics of machine learning. The system has a huge scope nowadays and if made properly it can be used commercially. The project is easy to make and handle.   

SLDYK
Kit required to develop Fraud detection using Machine Learning:

• No kit required

Technologies you will learn by working on Fraud detection using Machine Learning:

• Machine Learning

Any Questions?