AI and the Future of Fraud Detection

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AI and the Future of Fraud Detection

Reduced cybercrime and happier customers.

Photo by Bermix Studio on Unsplash

Fraud has been a critical issue for financial services institutions. And as global transactions continue to increase, so does the danger. Fortunately, Artificial Intelligence has enormous potential to reduce financial fraud.

With automated fraud detection tools getting more intelligent and machine learning becoming more powerful, the outlook should improve exponentially. Therefore, we’re going to investigate Artificial Intelligence and the Future of Financial Fraud Detection.

Online transaction growth

The McAfee security firm estimates that cybercrime currently costs the global economy about $600 billion, or 0.8% of global gross domestic product. One of the most prevalent forms of cybercrime is credit card fraud, exacerbated by the growth of online transactions. In addition, the speed with which financial losses can occur due to credit card fraud makes intelligent fraud detection techniques increasingly important.

Irregular Patterns

Due to the availability of large volumes of customer data, along with up-to-date transactional data as transactions occur, Artificial Intelligence can be used to efficiently identify patterns of irregular credit card behavior for specific customers.

Users Fingerprints

Cybersecurity companies can focus on implementing Deep Learning to create fingerprints of users and transactions.

For example, identifying the relationships between data points and reducing them to their main components can be grouped using mathematical models by creating clusters (groupings by similar characteristics) to identify the pattern of behavior towards other users in the cluster at any given time.

Spending habits

An added advantage of a more sophisticated model is its potential ability to use a wide variety of data points (as Mastercard has already done) to continuously adjust different customers and transactions across clusters best suited for accurate comparison. So as a customer’s life circumstances and spending habits change, the model automatically adjusts what it sees as potentially fraudulent transactions. As a result, we can reduce actual fraudulent transactions and minimize false positive fraud flags.

Signals anything outside a certain standard

False positives occur regularly with anti-fraud measures based on traditional rules, where the system signals anything outside a certain standard.

For example, if we plan a trip abroad and start buying airline tickets and accommodations, this may cause a fraud notice. However, as previously described, a smarter system that can better understand the underlying patterns of human behavior could use new customer data (travel purchases) to match a different group of users (vacation travelers).

It can then test our behavior against transactions typical of the new cluster of users, vacation travelers in this example, before automatically generating a fraud flag on your account.

The potential for electronic fraud is increasing with the increased use of advanced technology and the global nature of many transactions. Therefore, it is clear that it is imperative to use the most advanced techniques to combat cybercrime.

Algorithms based on the way people think

The most exciting thing for those who hope to reduce fraudulent activity further is that we now see a new generation of algorithms based on the way people think. For example, we can cite the Convolutional Neural Networks based on the visual cortex, a small segment of cells that are sensitive to specific regions of the visual field in the human body. Neural networks use images directly as input, functioning similarly to the visual cortex. It means that they can extract elementary visuals such as oriented edges, endpoints, and corners.

Reducing economic losses

This new development in Artificial Intelligence makes algorithms that were already intelligent infinitely smarter. For example, this technology can study an individual’s spending data and determine, based on this information, whether they performed the most recent transaction on their credit card or if someone else was using the credit card data.

The significant potential lies in the ability of neural networks to learn relationships from modeled data, as mentioned in this world academy of science study — the implementation of this type of solution to contain cybercrime, for example, drastically reducing economic losses.

Reduce costs, improve reputation

Fraud has occurred throughout human history and has become more complex and challenging to stop as technology evolves. Fortunately, we are now in a position where we can leverage technology — especially new neural networks — to identify these fraudulent activities and stop them before they cause damage.

Achieving this will reduce the overall costs of banks and improve their reputation with customers, who are likely to be more loyal to an institution that better protects their money. And banks even can channel some of the cost savings they do by reducing customer fraud in the form of lower transaction fees or reduced interest rates. So ultimately, AI is likely to create a radical change across the banking industry, leading to reduced cybercrime and happier customers. That’s truly a win-win situation.

Finally, we saved the exit of the Decision Tree. I hope you have found this helpful. Thank you for reading. 🐼


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  • Artificial Intelligence And The Future Of Financial Fraud Detection
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  • Machine Learning and AI for Fraud Prevention
  • Machine Learning for Fraud Detection — Modern Applications and Risks


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