How AI Helps Prevent Payment Fraud
Artificial intelligence has emerged as the latest payment fraud prevention tool. How can businesses use it to prevent losses and protect their customers?
What Is Payment Fraud?
Payment fraud is a type of financial fraud involving unauthorized or illegitimate transactions. It consists of a fraudster who leverages falsified or stolen payment information to make a purchase or initiate an unauthorized electronic funds transfer. However, the culprit is sometimes the legitimate cardholder.
Fraudsters are growing increasingly bold and skilled as technology advances. As a result, payment fraud is becoming increasingly common. It was responsible for $48 billion in e-commerce losses in 2023, up $30.5 billion in only three years.
Payment fraud is a monumental issue for companies, as banks and card brands rarely side with the merchant on the issue of fraud. After a successful fraud attempt, 44% of companies recover nothing — and most recoup less than 10% of their total losses.
In the past, payment fraud often involved fake checks or physical credit card theft. The explosion of digitalization and financial technology (fintech) has complicated this once-simple act, lowering the entry barriers for would-be fraudsters and making pinpointing the source of fraudulent activity more challenging.
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Common Types of Payment Fraud
While there are many types of payment fraud, some impact enterprises more than others.
Chargeback Fraud
A cardholder disputing a charge after a fraudster uses their stolen credit card to receive a product or service is known as chargeback fraud. This term also applies to situations where the cardholder makes a false claim of fraud after legitimately using it themselves.
Credit Card Fraud
Credit card fraud involves a fraudster stealing card details to make purchases. These cases increased by 10% from 2020 to 2021, totaling over $30 billion in losses globally. Unfortunately, it is becoming more common.
Clean Fraud
The point of clean fraud is for illegal transactions to appear legitimate. The fraudster goes to great lengths to impersonate the cardholder. For example, they might send a purchase to the cardholder’s previous address. When it inevitably gets sent back, they request the next delivery to go to their P.O. box, making the transaction appear legitimate at first glance.
Triangulation Fraud
There are three moving parts to a triangulation fraud attempt — the current customer, the past customer, and the fraudster. It begins when someone buys from an illegitimate online store. The fraudster steals their card details while simultaneously fulfilling the order using another person’s stolen information.
Here, the fraudster’s goal is to make the transaction appear legitimate so as not to raise any red flags with the current customer or the merchant. This way, they won’t be interrupted by card freezes or fraud flags. Naturally, the merchant who processed the stolen credit card has to forfeit the money they received even though they delivered a legitimate product.
Skimming
Fraudsters often pose as customers or abuse their privileges as employees to slip specialized devices onto card readers at ATMs, fuel pumps, or point-of-sale machines. This way, they can retrieve scans or pictures of cards to streamline credit and debit card theft.
How Can AI Prevent Payment Fraud?
Artificial intelligence excels at detecting fraudulent activity because it is precise, scalable, and swift. For these reasons, fraud detection is one of its leading applications in the finance sector. Of course, it also has applications in any industry where payment fraud is an issue.
Payments process within seconds, so businesses must be prepared to address potential fraud as soon as possible. Fortunately, AI’s automation capabilities allow for real-time monitoring without manual intervention. Of course, it can also operate on a trigger-based system to carry out actions like notifications, card freezes, or account flags.
Since AI is capable of unparalleled pattern recognition, it can automatically recognize and respond to potential fraud more precisely than its counterparts. Unlike humans, it can monitor numerous accounts simultaneously and still be able to identify even the most minor behavioral inconsistencies.
AI’s consistent performance and meticulous pattern recognition make it the ideal tool for predictive analytics. With this technology, companies can analyze past and current records, enabling them to anticipate and defend against future fraud attempts.
AI technology shows substantial potential, which is why many enterprieses are eager to adopt it. Research shows 71% of financial institutions plan to increase their use of these solutions within the next six to 12 months.
Use Cases for AI Fraud Prevention
Organizations can leverage multiple types of AI in various use cases to combat payment fraud.
Educate and Instruct Users
Businesses can leverage an AI chatbot for internal or client-side fraud prevention purposes. It can hold numerous conversations simultaneously, evolve with every interaction, and direct users to helpful resources, lowering the chance of payment fraud attempts being successful.
Review Past False Positives
Machine learning models can review past false positive alerts to understand what actual payment fraud looks like better. Since they become more accurate over time as they absorb more information, brands benefit from a future-proof tool that can adapt to new fraud trends.
Verify Online Transactions
Companies can use AI for biometric identity verification during online transactions to prevent unauthorized access attempts. These tools are highly precise — facial recognition payment algorithms have an error rate of 0.08% when identifying users — making them ideal for this application.
Identify Emerging Fraud Trends
Machine learning algorithms can monitor a combination of internal and external factors like market trends, shopping seasons, and dark web activity to identify and categorize emerging payment fraud trends. This way, businesses can prepare their defenses in advance.
Conduct Risk Assessments
Businesses can conduct AI-driven risk assessments to determine how susceptible people are to payment fraud using factors like purchase, location, chargeback, and banking history. Since this technology is scalable, they can produce personalized results for every client.
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AI Is the Future of Payment Fraud Prevention
With the help of AI, enteprises can prevent payment fraud from driving away customers or impacting their bottom line. Whether they use chatbots, machine learning, or AI-powered integrations, they’ll experience this technology’s benefits.
Originally posted on OpenDataScience.com
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