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Last updated: Apr 28, 2022

Data science and machine learning for fraud detection

Author: Maria Tsarouva
Last updated: Apr 28, 2022
What's inside

Organizations lose, on average, about 5% of annual revenue to fraud. Worldwide, that results in more than $3.7 trillion in annual losses.

In most cases, the fraud is an inside job, conducted by an employee, manager, or business owner. Or, if you deal with a large volume of customers, there may be numerous customers conducting fraudulent returns.

And it can be difficult to detect: When fraud is discovered, it's most frequently due to an employee blowing the whistle on a coworker or manager, as happened in nearly half of all cases. Internal and external financial audits accounted for only 17% of all fraud detection.

But with today's data analytics solutions, it's becoming easier to monitor and identify fraudulent activity within a business or organization in real-time. Artificial intelligence can be used to monitor algorithms and easily identify anomalies that can point to fraudulent behavior, either within an organization or perpetrated by an outside party, such as a vendor or customer. By setting up dashboards for monitoring activity, you can instantly get alerts when something is outside of the normal spectrum of activity, and gain evidence that you can use to build a case against the perpetrator and stop them in their tracks.

Common types of fraudulent behavior


Email phishing is a common way of obtaining confidential data or financial information from an unsuspecting individual. While it's important to ensure that your entire team is trained and on the alert for common phishing scams, and to set up email filters that can flag or block untrustworthy IP addresses, machine learning algorithms can also be used to classify data and identify possible phishing scams in your organization’s email data.

Payment fraud

Payment fraud includes credit card fraud, bank fraud, retail fraud, and B2B vendor/supplier fraud, and perpetrators' methods are incredibly varied. Banks and other financial institutions need sophisticated controls in place to block and identify fraudulent transactions and protect their customers, while retail brands and other types of businesses need the ability to identify both fraudulent and erroneous charges, intentional overages, kickback schemes, and other types of payment fraud conducted by employees, customers, and vendors.

Identity theft

Organizations need to be on the alert for their customers falling victim to identity theft crimes, which might include account takeover of legitimate accounts, as well as the creation of fake accounts that use real details. Data science can be used to provide better customer protection, by verifying identity documents and data against secure databases in real time to ensure accuracy, ensuring that the customer's identity can be authenticated prior to further action.

These are just a few of the types of fraud that an organization or individual may fall prey to; new scenarios are arising with great frequency, and standard cybersecurity tools won't offer much protection. In order to assess fraud risk in real-time and block it in its tracks, it's important to use machine learning technology.

Let's take a look at how AI data science tools can be used to detect fraud.


Rule-based scenarios

By using a rule-based approach, organizations can process large data sets and run a variety of algorithms against them to monitor for common fraud scenarios. Legacy systems today run approximately 300 different scenarios before approving a transaction, such as a credit card charge. For example, if a large purchase is made from a different country without the card being present, this is a common fraud scenario that will often result in a block on the card.

While rule-based scenarios will catch the most common types of fraud, they don't always identify more sophisticated efforts to initiate a fraudulent activity, so many types of fraud can go undetected – sometimes for periods of months or years. They can also create a lot of false positives: After all, how many times have you had your credit card declined when you simply try to make a purchase while on an international vacation?

Relying only on rule-based scenarios can also place a heavy burden on your finance team, which will need to frequently conduct manual reviews.


Machine learning fraud detection

As a result, many organizations have shifted to machine learning fraud detection models. These types of solutions use AI to not only monitor for rule-based fraud detection scenarios, but to learn and uncover new anomalies as they continue to gather and process data.

AI-driven data science solutions can assess individual behavior as it happens, and the more data it gathers, the better it gets at identifying potential anomalies. As transactions are flagged and your analysts determine whether a certain behavior is or isn't normal, your solution will be able to develop its own new algorithms and rules for assessing the risk of fraud in real time. You can also submit historical data that showcases known fraudulent behavior, which it can use to optimize its algorithms.

With machine learning, you can uncover subtle patterns that a human analyst may not pick up on, and you'll be able to integrate real-time streaming data from outside sources, such as a credit score database, so that you'll be able to raise alerts on potentially fraudulent activity and give it a closer look based on outside evidence – or even track specific accounts more closely before any fraudulent activity has even happened.

For example, if one customer has many different credit card accounts already, you can track their transaction and credit history to determine if they have a history of chargebacks – ensuring that if they issue a chargeback on your credit card, you'll pay closer attention to the transaction before offering the refund, and ensure that the chargeback reason is valid.

Machine learning enables your business to create custom models that are unique to your business. You can draw from your business' historical data to create an algorithm, and update it frequently based on new inputs. As the technology analyzes your data, it will send prompts requesting your analyst team to classify behavior, and rely on your insights paired with its own logic to continually learn from the data.

With machine learning and data science, it's becoming easier than ever before to identify fraud before it has a material impact on your business. If you haven't evaluated your options yet, it's time to consider how to protect your organization with a smart data science solution.

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