Can Machine Learning Prevent Fraud In Banking Industry?by@sushreeanud
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Can Machine Learning Prevent Fraud In Banking Industry?

by Alka DhingraMarch 11th, 2019
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Machine learning is one of the most trending things in the current tech world. A number of businesses from e-commerce to banking &amp; finance app development solutions are looking to hire ML developers from top companies who can develop amazing ML apps for their business. According to <a href="" target="_blank"></a>, 45% of technology companies prefer to use AI and machine learning for their ongoing projects.

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Machine learning is one of the most trending things in the current tech world. A number of businesses from e-commerce to banking & finance app development solutions are looking to hire ML developers from top companies who can develop amazing ML apps for their business. According to, 45% of technology companies prefer to use AI and machine learning for their ongoing projects.

The power of machine learning improvising various industries across the world even without being explicitly programmed. So, the finance industry is at the forefront of enfolding innovation.

In this blog, we will discuss how machine learning apps can prevent fraud in finance & mobile banking development companies. Before this, you must be aware of the machine learning app basics as discussed below.

What is a machine learning app and how it works?

When it comes to machine learning concept, as the name suggests, it is the power of machines to learn and improvise things accordingly. A machine learning app learns from its own experiences without being explicitly programmed. These apps can access information and use this data to learn & improve themselves.

Several industries also use ML for operations such as identifying unwanted email, providing an adequate recommendation of the product to customers and offering an accurate medical diagnosis. For an instance, Coca Cola company is using machine learning for product development. Using the data they collected from various dispensaries of soda sources, they were able to tell what flavor was preferred by maximum people. This is what helped them launch the ‘Cherry Sprite’ in the nation.

Here is another example of how ML applications are used to mitigate fraud. Huawei Technologies is using the analytical database to identify fraud in real time. They are using an automatic learning model that analyzes approved or rejected transactions. It is easy for the system to discover transactions that are fraudulent using this data.

So, a machine learning app is instrumental in solving the significant fraud of any business. The advent of machine learning and artificial intelligence has made a lot easier to prevent fraud in businesses today. Online money transactions are secure now and risk-free.

Read More: Which are Top Machine Learning GitHub Repositories To Seek In 2019?

The process of fraud detection using machine learning is explained below:

The process starts collecting and segmenting the data. After this, the machine learning model is fed with training sets in order to predict the fraud probability. It is a 3-step process explained below:

First Step: Extracting Data

The extracted data will be divided into three different segments: training, testing, and cross-validation. The algorithm will be trained in a partial set of data and adjust parameters in a test set. The performance of the data is measured using the cross-validation set. High-performance models will be tested for several random divisions of data to ensure consistency in the results.

Second step: Providing Training Sets

Prediction is the main application of machine learning that is used in fraud detection. The data used to train the ML models consist of records with the two output values for several input values. Records are often obtained from historical data.

Third step: Building Models

Model building is an essential step in predicting fraud or anomaly in data sets. First, determine how to make that prediction based on previous examples of input and output data. Now, you can further divide the prediction problem into two types of tasks:

– Classification — Regression

Let’s come to the list of points on how it is preventing fraud in finance & Mobile Banking Development companies:

1) Cost Effective & easy to maintain

A machine learning app can perform better when you enter a large amount of data. In systems that rely on rules, to maintain a fraud detection system, Finance & Mobile Banking Development companies have to spend a lot of money.

But, when it comes to ML, things will be much easier and more profitable. The more data you are going to feed the systems will help the machines run more efficiently. Differentiating good and bad transactions become much simpler when you do this.

2) Fast verification

In a system that relies mainly on rules, things can get too complicated and checking big data takes a lot of time. Merchants prefer to get their money faster and will be super happy when there is an implemented system that can verify huge volumes of data in just a few milliseconds.

Fraud detection will be easy and simple when you choose this option. Real-time verification of a large number of transactions is only possible with the ML-based system.

3) Futuristic solution

When it comes to cyber criminals, they are smart and use advanced tools & strategies to carry out their fraudulent activities. No matter how efficient your internal fraud team is, you will not find fraudulent transactions easily, as things will get more complicated.

Artificial intelligence and machine learning are the future and, therefore, financial institutions and other industries must rely on ML when it comes to preventing fraud. These systems can quickly learn the patterns and behavior of people who commit fraud and protect their organization against such things.

4) Efficient

Machines that receive the proper training will perform better than humans. They can do the repetitive work of data analysis with ease. The machines will scale all the cases that need human intervention promptly. Preventing fraudulent transactions from happening will be easy because they will recognize non-intuitive and subtle patterns without any difficulty.

5) Scalable

Algorithms in machine learning models become more effective with increasing data sets. While in rule-based models, the cost of maintaining the fraud detection system multiplies as the customer base increases.

Custom banking & finance software development services along with machine learning improves with more data because the ML model can detect the differences and similarities between multiple behaviors. Once they are informed which transactions are genuine and which are fraudulent, the systems can work through them and begin to select those that fit either of them.

They can also predict them in the future when dealing with new transactions. There is a risk on the scale at a rapid pace. If there is a fraud not detected in the training data machine, learning will enable the system to ignore that type of fraud in the future.

Wrapping Note:

There are many companies which are still wondering if it is worth to invest in AI and ML. Then, the answer is a big yes. In fact, it will be a fruitful investment for businesses in 2019. However, seeing all the benefits that an organization or institution will obtain and the money they will save in the future using banking mobile app solutions is incomprehensible. Therefore, it is time to adopt ML to safeguard the money and customer data and the reputation of the brand.

Now, if you are curious to develop your next project using machine learning or any of its frameworks, then it is the right time to start with. You can also hire skilled ML web developers from a reliable software development company like ValueCoders.

Originally published at on March 5, 2019.