Leveraging Machine Learning to Boost Fraud Detection in Fintech

PayPal holds a unique position in Silicon Valley mythology. The PayPal Mafia—Former PayPal employees and founders—have gone on to found YouTube, Tesla, Inc., LinkedIn, Palantir, SpaceX, and other ventures.

And while it may be taken for granted today, PayPal revolutionized online payments and essentially became the payment system of the internet. People and businesses were quickly and freely sending money online—a fintech breakthrough that has only been further enhanced by mobile technology, social media, and data encryption.

15 years after being bought by eBay, PayPal passed 200 million users.

And yet, despite growing close to a half billion users today, PayPal, like many fintechs, has been plagued by one huge problem—fraud.

PayPal CEO Dan Schulman recently reported that the firm had identified and removed 4.5 million illegitimate user accounts—and Paypal was forced to end its incentivized account opening program in 2021. The fintech fraud woes, of course, are not limited to PayPal.

Fintech companies like neobanks and robo advisors have an average fraud rate of roughly 0.30%—which is as much as double credit cards’ historical rates of 0.15% to 0.20% and three times higher than debit cards. Due to these increasing incidents of fraud, some merchants have begun limiting or even blocking the debit and credit cards being offered by Chime, Cash App, and other neobanks.

So—what can fintech do about its fraud problems? The answer starts with the right machine learning solutions. Machine learning can significantly enhance fraud detection in fintech companies by leveraging advanced algorithms and automated decision-making processes.

In this blog post, you’ll see:

• How fraudsters work
• The ways machine learning can effectively counterattack fraud in fintech
• The benefits of machine learning in fraud detection

How Do Fintech Fraudsters Run Their Scams?

The COVID-19 pandemic led to a rise in the use of fintech services—but also brought with it a lot of fraud. In fact, payment fraud attacks against fintech companies soared by 70% in 2021, according to a study by GlobeNewswire. In 2022, hackers also accessed nearly 35 K PayPal accounts.

So, how might a payment fraud scam go down? Here are a few examples:

• Money Laundering: A fraudster might get hold of a credit card information via a data breach or by buying it on the dark web. The scammer then creates an account with a fintech company and uses the stolen credit card information to make payments or transfer funds.
  • The fintech might then process the payment or transfer and send said funds to the fraudster’s account. The fraudster then withdraws the funds or transfers them to another account—thus effectively laundering the stolen cash.

• Fraudulent Vendors: Another popular scenario is when legitimate users willingly give their primary credit or debit card info to pay fraudulent vendors they don’t know well.
• Social engineering: Do you trust everyone you meet online? You shouldn’t. Fraudsters use social engineering techniques such as manipulating or tricking individuals into giving away their sensitive information.
  • Phishing: A fraudster might send a phishing email that appears to have come from a legitimate fintech company, such as a bank or payment processor—with the aim of tricking the recipient into providing their login credentials or other sensitive information.
    • The scammer then sends an email that appears to be from a legitimate fintech company, such as a bank or payment processor, asking the recipient to click on a link or provide login credentials or other sensitive information.
    • The link leads to a fake website that looks like the legitimate fintech company’s website and the recipient enters their login credentials or sensitive information on the fake website, which the scammer then captures. The scammer then uses the stolen information to access the recipient’s account and steal money or other sensitive information.

Other fintech scams include malware, mobile fraud, web skimming, and botnet or bot attacks.

Analyzing Data Is Key to Identifying Patterns of Potential Fraudulent Behavior with Machine Learning

Many fintechs are still using outdated traditional methods, such as the rule-based method (think: if-else or if-then rules). In contrast, the AI-approach utilizes advanced algorithms and automated decision-making processes powered by machine learning to significantly enhance fraud detection for fintechs.

Leveraging machine learning starts—as with most machine learning—by analyzing new and historical data to identify patterns. Machine learning algorithms can analyze large volumes of data to identify patterns that are indicative of fraudulent behavior. Changes in regular patterns are known as data-specific anomalies, or irregularities in the data that are indicative of fraudulent activity. These can be further broken down into two categories:

• Behavioral anomalies: Changes in a customer’s spending habits or patterns that are not consistent with their historical data, such as:
  • Sudden spikes in spending
  • Changes in the frequency, location, or timing of transactions
• Transactional anomalies: Irregularities with transactions, such as:
  • Unusually large or small transaction amounts
  • Transactions made outside of normal business hours
  • Multiple transactions made from the same IP address or device

Here are a few ways that machine learning can identify patterns and help fintech companies augment their fraud detection:

• Data Mining: More than just structuring and classifying data, this process involves using statistical and computational techniques to analyze large datasets and identify patterns, trends, and anomalies.

• Behavior analysis: It’s important to analyze customer behavior over time in order to establish a baseline of normal activity. By detecting anomalous behavior, such as an unexpected transaction, the algorithm can flag the activity as suspicious and trigger more investigation.
• Predictive modeling: Building predictive models that assess the likelihood of fraudulent activity based on factors—such as the customer’s transaction history, location, and other data points—is a beautiful application of machine learning.
• Automation: Cutting down on the need for manual intervention with machine learning saves time and resources in fintech’s mission to improve fraud detection.

Thus far, you have seen how fraudsters work and some ways that machine learning can combat their nefarious deeds. Now, let’s look at the tangible benefits of using machine learning to root out fraud detection for fintechs.

Machine Learning Reduces Security Breaches, Delivers Faster Data Collection, and Accelerates Efficiency

Listing all the features that machine learning has to offer is interesting for many—but remember that fintech companies want to see tangible results that help their brand—and their bottom line.

If you might still be unsure about how machine learning can help fintechs enhance their fraud detection efforts—please consider the following:

• Reduced Security Breaches: More than 4,100 publicly disclosed data breaches occurred in 2022—equating to approximately 22 billion records being exposed. Machine learning can detect suspicious circumstances by comparing each new transaction with the preceding (personal information, data, IP address, location, and so on). As a consequence, financial units can prevent payment or credit card fraud.

• Faster Data Collection: Commerce moves fast these days. People want to make purchases with their phones, watches, or one click of a button. It’s imperative to have faster machine learning algorithms capable of analyzing massive volumes of data in a relatively short period. They can continually gather and analyze data in real-time—thus detecting fraud faster than ever.
• Enhanced Scaling Ability: The amount of data that is being produced is growing at an obscene rate—by 2025, global data creation is projected to grow to more than 180 zettabytes. But, with bigger datasets—machine learning models and algorithms become more effective.
  • With more data, machine learning improves because the ML model can identify similarities and differences across numerous actions. As authentic and fraudulent transactions are identified, the system may sort through them and begin to identify those that fall into the right category.
• Enhanced Efficiency and Bigger Cost Savings: Robots are able to do repetitive jobs and identify changes in enormous amounts of data—unlike humans. This is crucial for detecting fraud when there is a time crunch. Algorithms can evaluate hundreds of thousands of payments every second with pinpoint accuracy—and with none of the pesky mistakes that come with human error.

This minimizes expenses as well as the amount of time required to review transactions, making the process more efficient. And let’s be honest. Merchants globally are predicted to suffer fraud losses exceeding $343 billion over the next five years as the volume and sophistication of ecommerce fraud increases. Reducing the amount of fraud is a huge cost savings for fintechs.

Renew the Lost Trust That Gave Birth to the Fintech Industry

The over-centralized financial system and bank collapses in 2008-09 bank collapse led to the rise of fintech for several reasons:

• Lack of Trust: If people lose trust in traditional financial institutions due to their failure or misconduct, they may look for alternative solutions. A lack of trust can lead to a demand for new players in the financial industry who can offer more transparency and accountability.

• Need for Innovation: The financial industry has historically been a snail when it comes to adopting new technologies and innovating. Fintech fills the void.
• Unserved Markets: Fintech companies can leverage new technologies and business models to serve these previously unserved markets.
• Lower Costs: Fintech companies can often operate with lower costs and better terms than traditional financial institutions.

With all of these tremendous positives—why let fraud spoil the party? ML Studio understands the opportunities that fintech has to offer the world—and the importance of accompanying it with state-of-the-art fraud detection with a built-in explainability module.

Get access to explainable AI and solve fraud detection crises and other real-world business problems with a robust and ready-to-use End-to-End AI platform that doesn’t go off budget.

Get in touch for a personal demo.