How AI is helping with Credit Card Fraud?
by Team Assurety
Published on June 25, 2024
5 min read
Unveiling the Future
Part 02 of a ten-part series on Leveraging AI to solve complex business problems.
As part of our commitment to keeping you informed on the latest advancements in AI technology, we’re excited to share our article in the AI Insights series.
Introduction
Welcome to part two, as per the Security.org report on credit card fraud, published in May 2024, 52 million Americans experienced fraud in the past year, leading to over $5 billion in unauthorized charges. The study found that 60% of U.S. credit card holders have faced fraud, with 45% encountering it multiple times. According to Security.org, this number is set to rise even further in the coming years.
Credit card fraud is a big problem for consumers and financial institutions, as fraudsters adopt more sophisticated techniques to bypass legacy detection systems.
So, what is Credit Card Fraud?
Any unauthorized use of a credit card through a payment tool can be classified as credit card fraud. These frauds result in a loss for consumers and commercial entities alike, however, there is an added frustration for the cardholders.
Due to COVID, more users turned to the internet, and e-commerce activities saw a tremendous surge. This surge exposed vulnerable users and acquiring and abusing cardholder’s information became easier for fraudsters. These complex breaches can be stopped by preemptively detecting anomalies and responding/verifying users at lightning speeds. That’s where AI and machine learning are a game changer, with quick analysis capabilities and pattern recognition that identify probable breaches and attacks.
How to Prevent Credit Card Fraud
The easiest way is to safeguard your credit card information. However, it is seen in many cases that fraudsters may open a completely new account using your personal information. So, secrecy is not the answer as it restricts a consumer’s ability to use their credit card independently on digital and non-digital platforms.
Traditional Methods to Prevent Credit Card Fraud
There are many traditional fraud detection and prevention methods, mostly rules-based identity verification systems. These are programmed rules that stop transactions based on anomalies in user behavior. For banks and financial institutions, these traditional systems worked wonders for years.
Although with increased sophistication and the changing nature of credit card fraud, these traditional systems are mere obstacles that fraudsters can easily jump over.
These pre-programmed rules behave reactively at best, and their limited processing capability usually misses fraudulent transactions as fraudsters keep changing their tactics and hiding their activities using robust means.
Role of Financial Institutions
Having a robust risk management platform to reduce fraud, including multiple user roles, limits on amount, close monitoring and biometric or facial recognition-based authentications certainly helps. These policies complemented by predictive analytics and ML can develop a regime that prevents frauds more efficiently.
AI to Prevent Credit Card Fraud
A set of algorithms that simultaneously process incoming behavioral and transactional datasets to stop fraud before it even happens. This is the power of AI, it continuously improves and reduces false positives, by learning and improving the rules. In doing so it does not compromise seamless customer experience and the speed of the process.
The detection capability of AI systems improves over time as they process more and more transactions, their capability to accurately predict increases. To speed up companies share knowledge of their AI systems with each other, so they get into preventive mode from reactive mode quickly without being at risk and incurring huge costs. AI companies are developing systems for fraud detection at scale with greater accuracy, reduced manual effort, fewer false positives with the ability to dynamically learn and evolve. AI systems specifically developed to detect and prevent fraud use multiple preprocessing phases before model training. Once models are trained, as many as 20,000+ different blends of algorithms run simultaneously to process, match, and detect quickly.
Business Intelligence & Analytics | Investigate, Report, Adapt and Succeed
Assurety offers a comprehensive suite of AI-driven solutions for financial services
- Fraud Detection & Prevention: Identify and stop fraudulent transactions in real-time.
- Data Management & Reporting: Gain a holistic view of your data and ensure compliance.
- Governance, Risk & Compliance (GRC): Protect your brand and meet evolving regulations.
AI Systems in Action
AI systems process large amounts of datasets, and sometimes it becomes hard to understand its working, especially when AI is used in conjunction with ML and neural networks. To keep it reliable and personalized, companies can now customize such models to include or change existing rules and view results more closely. As many users have started using VPN on a routine basis blocking false positives can result in frustration for customers, with AI capabilities false positives can be reduced to a small number.
Fraudsters do not obtain information or jump obstacles manually; they use intelligent bots to slow down and crack payment gateways to obtain card information that can be used for their gains. AI detects this type of fraud by recognizing the bot’s machine-like behavior and blocking them, in some cases, it may use CAPTCHA to confirm its observation. These bots can create fake accounts with stolen information and may result in a slowdown or fake transactions, AI can recognize these bots quickly and block them before they perform any fraudulent actions.
Besides fake account creation, fraudsters can take over real accounts and obtain personal data or carry out transactions. These takeovers hurt the reputation of the platform and are harder to detect as many users don’t opt for multifactor authentication. With the help of AI, companies nowadays detect the takeover and prevent it from ever happening.
Credential stuffing is another way for bots to enter such portals, using different combinations or previously stolen personal data fraudsters try to break your password and once logged in they can carry out fraudulent transactions. AI when applied correctly, can detect this high rate of login failures and prevent bots from accessing your user profile or landing page.
An Example of Online Retailers
It has been reported that just one popular online retailer was attacked 200,000 times monthly by fraudsters. Using AI APIs, in a short time the retailer was able to detect 73% of fraudulent activities by using historical data related to customer buying patterns, demographics, and transaction details. On top of that, their sales increased 10 times while reducing several costs.
AI is saving billions of dollars by preventing fraudulent transactions. Financial institutions, banks, and e-commerce platforms with the help of these dynamic systems are adopting a more agile defense that can adapt to creative ways that fraudsters use. Artificial Intelligence will be a significant contributor to the cybersecurity of such firms, which can reduce the ever-increasing amount lost to credit card frauds.
Upcoming in the series
Part 03: Five ways AI can help Detecting and Preventing Cross Border Fraud
Part 6: Demystifying the Maze- Navigating the Types of AI and Recent Advancements
Part 7: How AI is helping with Credit Card Fraud?
Part 8: Revolutionizing Postal Security- AI’s Role in Combating Fraud
