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Machine learning is quickly becoming key for payment fraud detection strategies of large enterprises.
By 2027, e-commerce will make up close to a quarter of total global retail sales.1 This digital transformation gives your customers the ability to pay for products when, how, and where they want.
It could also expose your business to a greater risk of fraud. In one survey, 42% of respondents said their organizations are much more vulnerable to an online fraud attack due to digital transformation.2
Customers may also be concerned. One study revealed that two-thirds of consumers would switch providers due to fraud.3 This has accelerated the race to better protect customer data — and in the world of payment fraud detection, machine learning and other automated solutions have emerged as critical tools for the digital transformation.
Understanding how artificial intelligence (AI) and machine learning algorithms for payment fraud detection can help you detect fraud and unlock operational efficiencies is the first step to improving your customers’ comfort levels and driving revenue.
AI is a general term that refers to a computer system’s ability to mimic human cognitive functions like learning and problem-solving. It relies on math and logic to learn from new information and make informed decisions based on that information.
Machine learning is a subset of AI that uses algorithms to scan vast amounts of data for patterns and insights. It then applies that learning to make increasingly better decisions. It enables programmers to improve the perception, cognition, and decision-making power of a computer system.
In our day-to-day lives, AI and machine learning help us do everything from detecting email spam to anticipating traffic on our commutes. In the context of e-commerce, machine learning is a type of AI that businesses can use for payment fraud detection. There are also various subtypes within payment fraud detection machine learning.
While all machine learning models help businesses in the fight against fraud, some models are especially helpful.
One of the most common ways of using machine learning for payment fraud detection is via supervised learning models, which are trained to run predictive analysis with historical data tagged as good or bad. While that analysis is typically faster, more accurate, and more cost-effective than human analysis, its success depends on the quality of the data being used to train it.
In unsupervised models, machine learning algorithms process and analyze untagged data to identify patterns of normal buying activity and detect potentially fraudulent anomalies. The process is fully autonomous and removes humans — and human error — from the equation. However, it also has no human oversight, which can be problematic at times.
As its name suggests, semi-supervised machine learning models split the difference between supervised and unsupervised approaches. They use fraud detection algorithms to analyze small amounts of tagged data along with larger amounts of untagged data.
With the reinforcement approach, machine learning models learn optimal behaviors within specific environments. As the environment responds to various interactions, the models can analyze, evaluate, and learn.
The applications of machine learning in payment processing are far-reaching. Without any human intervention, the algorithms can find patterns — or pattern deviations — in huge amounts of historical data.
This can help your business in several ways. It enables you to identify customers you may be at risk of losing and act quickly to retain them; build dynamic models that better segment delinquent customers and improve collection strategies and on-time payment rates; and optimize your pricing and improve customer segmentation.
Machine learning for payment fraud detection can help you uncover and mitigate some of the most common types of fraud:
The ability of machine learning to rapidly analyze vast amounts of data, identify patterns, and detect anomalies has catapulted it into many facets of everyday life. For payment fraud detection, machine learning may be a good defense against increasingly sophisticated bad actors because it could enable you to:
In the world of cybersecurity, things happen fast. Machine learning algorithms run hundreds of thousands of queries in milliseconds and can often assess individual customer behaviors in real-time. This makes it possible to quickly differentiate legitimate customers from fraudulent ones, helping you quickly approve authentic transactions and create a seamless experience for trusted customers.
Effective machine learning is powered by robust, proprietary data. The more data machine learning models have to work with, the better they can distinguish between normal and fraudulent behavior. That may make it a good fit for enterprise businesses that process hundreds of thousands or even millions of transactions per month.
It doesn’t have to stop with your own data. Shared intelligence could make machine learning algorithms for payment fraud detection even stronger. For instance, PayPal’s two-sided network is a rich source of transaction and risk data from millions of active global accounts that may help enhance fraud detection.
Payment processors aren’t the only ones employing automated technologies to advance their goals. Fraudsters are, too — and advanced tools enable them to find your weaknesses and hide their domains, devices, and even IP addresses. The rules-based systems that have traditionally helped mitigate fraud sometimes can’t keep pace.
With machine learning, you can process large datasets with multiple variables and quickly uncover correlations that might indicate more sophisticated fraud attempts. Not only do these models identify trends and patterns that human eyes might miss, they’re also able to continuously adapt to the ever-changing fraud landscape.
Many types of fraud tools use filters, including rules-based solutions. Unfortunately, scammers are constantly testing filters and designing new attacks to circumvent them. Machine learning can provide deeper insights that may help you customize those filters and rules.
A combination of machine learning and rules-based models may prove especially effective. You could use machine learning algorithms to suggest new rules for analysts to create. You could also use rules to help train machine learning systems to see certain patterns, creating a layered detection system that relies on both known rules and adaptive machine learning.
Machine learning can be pivotal to a company’s e-commerce fraud strategy.
Payment fraud detection and machine learning often go together, helping businesses improve customer satisfaction and lower costs.4
As e-commerce continues to grow, it’s likely that machine learning and other emerging AI technologies will continue to play larger roles in payment fraud mitigation.
PayPal can help take your business into the future. Explore Fraud Protection Advanced to see how we leverage machine learning, automation and 20+ years of experience building risk models to help you streamline fraud analysis, improve decisions, and positively impact your bottom line.
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