Fraud Detection in FinTech Using Machine Learning: A Four Step Process

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Fraud Detection in FinTech Using Machine Learning: A Four Step Process

Fraud detection using Machine Learning

With an increasing number of FinTech solutions offered across a variety of industries, we’re witnessing an unprecedented rise in security threats. Industries involved in financial transactions open themselves up to the risk of suffering from various fraud related issues and damages, not only including financial loss but also the potential for massive customer data leaks. Fraud detection using machine learning algorithms is one solution various companies are pursuing to address this serious concern. Algorithms that drive artificial intelligence and machine learning tools are becoming more and more efficient in identifying and highlighting fraudulent activities within the FinTech ecosystem.

Machine learning depends on a four step process to detect fraudulent activities in the FinTech ecosystem.

1. Input Data

In 2021, data is the ultimate arbiter of all intelligent business decisions, as well as being a pivotal part of improving machine learning tech. Information about a client making a transaction such as their name, amount of money transferred, time of transaction, past history, and more can be fed into a machine learning algorithm to detect irregularities and further train your tool. In order for this process to flow seamlessly it’s essential that you ensure that no missing, erroneous, or false data is submitted during user onboarding.

2. Extract Features

From the data provided by the users, there are some fields which might be helpful in differentiating between fraudulent or genuine transactions. Some features include:

  • Identifying digital identity via email IDs and verified documents
  • History of past orders
  • GPS checkpoints
  • IP addresses used for transactions

The above parameters form fundamental data points for a transaction, and on the basis of these inputs we can mitigate risks that could lead to financial loss or data theft.

3. Training Your Algorithm

After feature extraction, a machine learning algorithm has to be trained using previously acquired data and features. There are various algorithms that can help in fraud detection like classification, logistic regression, and neural networks, amongst others. Each of these algorithms have their own unique use cases. Classification is used when you want to determine if the transaction was genuine or fraudulent. Logistic regression can be used to identify the probability of a transaction being fraudulent. Neural networking on the other hand is a deep learning technique which tries to identify relationships between data points by mimicking the working of the human brain. So, a deep learning algorithm would recognize patterns in a transaction and give output based on patterns recognized as fraudulent or genuine. Choosing an appropriate algorithm is crucial to derive accurate results. Each dataset is then split into a training set and a testing set. The training set is the portion of data which is fed to the algorithm while enhancing its efficacy, whereas the test data is used to measure the accuracy of the trained model. From here, the algorithm can be tweaked until a desired accuracy percentage is achieved.

4. Creating a Model

Once an algorithm is trained well it becomes capable of working with new and live data. The developed model can be integrated with an application and be used to flag fraudulent activity at banks and financial institutions. There are two ways to integrate a machine learning model; API or SDK implementation. API is just like a service or a feature but SDK is a tool in itself. Once integrated, the model should work well in real-time situations to detect prior frauds, and ensure no further fraudulent activity occurs.

Machine learning algorithms for fraud detection can solve problems like email phishing, credit card theft, document forgery, payment frauds, and more. Noteworthy, well trained algorithms can reach up to 95% accuracy! Achieving such a high correct percentage is an achievement for technological advancement as it can prevent huge financial and reputation damages. The more efficient these algorithms work, the safer the internet will be for financial transactions.

Want to know about how Invoid is using machine learning to help over 50 companies prevent fraudulent activity on their platforms? Book a crisp demo call here to learn more about our product.


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