How Machine Learning Facilitates Fraud Detection?

Digitization and the growth of the internet have played an important role in redefining the future. With the use of technology, it has become easier for companies to do transactions in a jiffy, but at the same time, this has exposed us to data threat and breach. In the recent past, we have witnessed a huge loss of money because of fraudulent activities on the internet.

In the year 2018, the global economy suffered a loss of  3 trillion dollars. And this amount is growing every year. To overcome this, there have been new developments that will act as a cover to such digital attacks. Artificial intelligence and machine learning are going to transform the industry. In this blog, we will be answering how machine learning will help in fraud detection.

Machine learning for fraud detection

Machine learning works on data; these models learn from the data available from patterns of normal behaviour. It can easily detect any change in the normal pattern, and this method can be useful in detecting fraud or unauthorized entry. 

Machine learning models are designed to study the data and adopt the changes, which makes it easier for it to detect the changes in the system and identify patterns of fraud detection. 

Here is why machine learning is suited for fraud detection:

1. It is fast- You need fast results when it comes to detecting fraud transactions. Results have shown that the longer time it takes, the less is the probability that the customer will checkout. When you have a machine learning tool working for you, it is like a team of analysts working for you; a simple algorithm of machine learning will have the efficiency to resolve hundreds of queries and compare the results before delivering the final result. And all this takes place only in milliseconds. 

2. Scalability- Whenever we adopt a tool for any task, it must have the capacity to handle a large volume of work without any flaw. The machine learning algorithm is designed to handle such a large volume. Machine learning systems start working more efficiently when we have a larger volume of data. It makes the tool study more data, and based on it; the tool can take more precise decisions. It will be easier for the machine learning tool to assess the difference between the good and the bad. And this learning will be useful in making future fraud detection, thus enhancing the safety and security of the system. 

3. More efficient- The reason we are harping so much on technology is that we want faster outcomes with minimal or no errors. Machine learning algorithms are designed with the intent to make the system far more efficient. We have already mentioned in point number 1 that a machine learning tool can handle a large volume of transaction and figure out the result in just milliseconds. This eventually enhances the efficiency of the system. 

Moreover, machine learning tools are going to work round the clock, unlike humans. Machines can perform the repetitive task with ease.

4. Accuracy– Data forms the core of machine learning. If you are feeding a  lot of data into the system, the machine learning tools have more information to assess, and it eventually enhances the efficiency of machine learning. The more the machine learning tool can assess the patterns of normal behaviour, the better it would be able to detect fraudulent activity.  It means that machine learning tool can easily identify suspicious customer

The above discussion highlights how machine learning is helping in the detection of fraud and making the system foolproof and infallible. 

As our dependency on technology increases, so would be the need to have a system that is secured. Online transactions are going to be the harbinger of change, and we will be adopting it, but to make it reach the masses, the system has to be secured, and this is where the machine learning tools come into the picture.

Machine learning experts design algorithms that are strong enough to read from the past data, and based on it, they can make future decisions. 

Simple representation of how machine learning is used for fraud detection:

The basic steps of fraud detection are :

1. Gathering of data 

2. Feeding this data to the machine learning model 

3. This machine learning model is put to training sets to predict the probability of fraud.

Some of the common test used in fraud detection are:

1. Logistic regression

2. Decision tree

3. Random forest test

4. Neural network

Concluding notes

A machine learning expert will have proficiency in all these tests. They will be able to create a system that is far more safe and secure. Although there are certain drawbacks of the present system, as and when we start feeding more and more data into the system, it is going to be improvised. 

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