Fraud Detection: How to use machine learning in fintech?

archer how to use machine learning in fintech

The good thing the technological developments, such as Artificial Intelligence (AI) and machine learning algorithms, are now used for fraud detection in banking to identify suspicious transactions in real-time more accurately and with a lower rate of false declines.

Quick navigation

Do you know how much money banks lose every year due to fraud? The Financial Regulation News data say the banking industry lost $2.2 billion in fraud losses in 2016, 58% of which were related to debit card fraud. ATM Marketplace states that card fraud losses escalated in 2017 and it is projected that card fraud will grow an additional 42% by 2020.

Moreover, there’s also a problem of false-positive transactions in e-commerce, i.e. wrongly rejected legitimate transactions due to suspected fraud. The 2015 study by Javelin Strategy revealed that such false-positive declines in the U.S., which account for $118 billion of dollars in annual losses for retailers may represent a serious threat for businesses not only because of the retailers’ lose money, but also because they lose their customers due to erroneous declines.

What is fraud detection?

Fraud detection touches many industries including banking and financial services, insurance, healthcare, government agencies, etc. In simple words, fraud detection is the system for identification and blocking suspicious activities to prevent such activities endanger the business.

Before computers and computer technologies have become smart the traditional method of detecting fraud was to analyze a lot of structured data against rule sets using computers. This method requires complex and time-consuming investigations as fraud often consist of many instances or incidents involving repeated transgressions using the same method.

Fraud instances can be similar in content and appearance but usually are not identical that is why this type of structured data analysis often gives too many false positives. Rule-based method of fraud detection is capable to catch obvious fraudulent scenarios and requires a long time for processing with much manual work.

When we talk about big data, we need to understand that the learning algorithm that deals with anomaly detection should use the possibilities of predictive analytics and exclude human interference as much as possible. These systems should be learned to make predictions.

Fraud is a very adaptive and tech-savvy crime. That is why the more technologies are in the market the more advanced should be the tools for fraud identification and preventing fraud. The state-of-the-art intelligent data analysis methods for fraud detection systems include Knowledge Discovery in Databases (KDD), Data Mining, Machine Learning, and Statistics.

According to Wikipedia, the key AI techniques used by fraud detection software companies are:

  • Data mining – the method which is used to structure the data (classify, cluster, and segment) and automatically find associations and rules in the data that may signify interesting patterns, including those related to fraud.
  • Expert systems to create rules for detecting fraud.
  • Pattern recognition to detect approximate classes, clusters, or patterns of suspicious behavior either automatically (unsupervised) or to match given inputs.
  • Machine learning techniques to automatically (without being guided by a human analyst) identify unusual patterns in datasets which can be characteristics of fraud.
  • Neural networks can learn suspicious patterns from samples and used later to detect them.

Why fraud detection in fintech is important?

As the volume of electronic transactions grows onward and upward, fraud identification and detection becomes a great challenge when using conventional methods and via data analysis. Fraud becomes increasingly sophisticated and technologically advanced while designing products that is why end-users are unable to protect themselves against it.

Fraud prevention laws, such as to name a few, Fraud Act 2006 in the UK, 18 U.S. CODE, Insurance Frauds Prevention Act in the US, state that providers of financial services are legally responsible for fraud damages, which increases the cost of doing business.

The amounts of data in every industry are growing exponentially and, thus, grows the challenge of detecting fraud for fintech projects. To cope with vast amounts of data it is necessary to build machine learning systems. Deep learning fraud detection using lots of different machine learning-based methods (both supervised and unsupervised) allows finding hidden fraud scenarios and well-disguised correlations in data.

What to expect and how to get prepared for the economic recession?

How to build fraud detection with machine learning in fintech?

You should keep in mind that fraud prevention is a dynamic process. It is a cycle that involves monitoring, detection, decisions, case management, and learning. Your fraud detection system must constantly learn from incidents of fraud and use the obtained results in monitoring and detection processes.

When building fraud detection machine learning algorithms you have to build such a model that will distinguish legitimate and fraudulent behaviors and which will be able to adapt to new and unseen fraud tactics. That is your machine learning algorithms have to learn the right things.

There is no one-size-fits-all analytic technique – your strategy has to integrate supervised and unsupervised AI models. They have to capture and unify all available data types from all data channels and incorporate them into the analytical process.

Supervised models are used in the majority of practical machine learning cases. According to the Machine Learning Mastery, it is called supervised learning “because the process of an algorithm learning from the training dataset can be thought of as a teacher supervising the learning process”. It is trained on a rich set of properly “tagged” transactions - either fraud or non-fraud.

The process of learning is based on massive amounts of tagged transaction details to define patterns that best reflect legitimate behaviors. The model accuracy depends on the amount of clean, relevant training data.

Unsupervised learning is designed to identify anomalous behavior in cases where tagged transaction data is relatively thin or non-existent with the help of clustering algorithms. The goal here is to model the underlying structure or distribution in the data to learn more about the data. Unlike supervised learning, the unsupervised model has no correct answers and no teacher. Self-learning algorithms are to be employed to find patterns in the data sets that are invisible to other forms of analytics, i.e. they find new, previously unseen forms of fraud.

As a rule, a good machine learning fraud detection system is a blend of supervised and unsupervised AI techniques, behavioral analytics, predictive models, and adaptive analytics to enable real-time decision-making and forecasting.


An effective fraud detection and prevention solution must be able to capture fraud and flag transactions that need review. Data analytics should be the basis of your solution as the machine learning fraud detection system should be able to learn the right things from the complex data patterns you have.

Well architected machine learning models should enable the use of rich information after fraud events to build better models. It should generate financial trends and forecasts and help your company analytics determine possible weaknesses of new products and lines of business and get insights for better operational safety.

If you are concerned about the future of your business and need a reliable AI fraud detection system, feel free to contact us at for a consultation.