Fraud detection using machine learning

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The analysis of a company's payments or transactions data allows us to classify which ones are fraudulent and which ones are not.

Knowing this classification, we can take action to prevent them in our company.

The data science and machine learning platform Neural Designer helps banks, insurance, telecommunications and retail companies to detect and avoid these fraudulent payments.

Contents:

  1. Objectives.
  2. Benefits.
  3. Approach.
  4. Results.
  5. Conclusions.

Objectives

Fraud detection has became one of the biggest challenges for all companies. Frauds represent significant problems for any businesses and specialized analysis techniques for detecting them are required.

However, it is not easy to develop these techniques in order to detect a fraud.

By harnessing the power of fraud data sets, companies can develop predictive models that enable the detection of any possible fraud.

Some of the factors that can influence in committing fraud are the following:

The objective is to develop a program which can detect and prevent future frauds.

To do that, it is essential to develop new methods that can identify which are the variables that make a payment fraudulent.

Benefits

Some of the many benefits of using machine learning for fraud detection are the following.

STOP FRAUD QUICKLY

Detect fraud quickly and early enough to take action.

AVOID ECONOMIC LOSSES

Avoid losses due to fraudulent payments.

IDENTIFY COMPANY WEAKNESS

Identify the weakness why a fraud could be commited and fix them.

Approach

Neural networks can create the model in order to detect fraudulent transactions and avoid them.

The following graph illustrates an example of a neural network for fraud detection:

The data science and machine learning platform Neural Designer guides you through this process so that you can focus on your business and not on the details behind machine learning.

Results

As we have explained before, the aim of fraud detection is to find out which payments are fraudulent and which ones are not.

The next graph is obtained from the example credit card fraud detection. We can observe the rates with the model and without the model.

As expected, we can see how the positive rate increase, initially we have a rate of 15% and after applying the model we have a rate of 90%.

Conclusions

Machine learning allows companies to find frauds on time using the available data.

Companies can take advantage of these techniques and avoid losses due to fraudulent payments and also improve all the weakness the company could have in the payment area.

Neural Designer uses artificial intelligence to discover the variables that influence whether or not a payment is fraudulent.

Related solutions:

Related examples:


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