🧠 AI Strategies Against Digital Deceit

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In today’s Sunday Special:

• 🚨Fraud Detection 101

• ⚙️Machine Learning in Fraud Management

• 🥸4 Emerging Arenas for Fraud

Read Time: 6 minutes

🎓Key Terms

• Machine Learning (ML): an application of Artificial Intelligence (AI) that provides systems with the ability to automatically learn and improve from experience using a large volume of training data.

• Unsupervised Learning: finding patterns or structures in data without labeled guidance. For example, this algorithm might uncover indications that a user may have committed fraud without actually knowing if fraud occurred.

• Blackbox ML: machine learning models that generate a result or reach a decision without explaining or displaying how they did so. In other words, we cannot see how machine learning systems make their decisions.

• Whitebox ML: machine learning models that allow humans to easily examine how the models produce outputs and draw conclusions.

• Confusion Matrix: a performance measurement for machine learning algorithms that presents a summarized table of the correct and incorrect answers yielded by a classifier for binary classification tasks.

🚨FRAUD DETECTION 101

Put simply, fraud detection prevents criminals from gaining money, information distribution channels, or other valuable assets through pretenses. While the internet has revolutionized distribution, it has also amplified vulnerabilities. Fraud prevention refers to the countermeasures established to mitigate future fraud attempts. Detecting fraud is the first step in identifying where the risk lies. You can then prevent it automatically or manually using fraud detection software, RiskOps tools, and other risk management strategies, including machine-learning techniques.

Fraud is more common than most people think. At most, you probably only know a couple of friends or family members who have been victims of crimes. The opposite is true of most businesses you buy from. According to PwC's 2022 Global Economic Crime and Fraud Survey of about 1,300 business executives worldwide:

• 46% of companies experienced fraud within the last 24 months.

• 65% of technology companies experienced fraud within the last 24 months.

• 30% of businesses believe it disrupted business.

• 23% of businesses claim it lowered employee morale.

With billions of dollars stolen, what methods did criminals employ? Credit card fraud, account takeover (i.e., stealing a user's identity to access an existing account), and fake accounts (i.e., falsified information using stolen IDs) topped the list. The end goal, however, is still the same: steal money or personal data from the original user. An increasingly prevalent strategy is affiliate fraud, where affiliates intentionally direct fake traffic to your site.

Fraud detection and prevention typically require a three-pronged approach. First, prioritize employee education about fraud risks. This strategy requires continuous investment and fails to operate at scale. Second, implementing user fingerprints, which involves matching users to real social media profiles or ensuring emails are associated with legal internet activity. Third, rule-based fraud prevention tools that block specific IP addresses or analyze how a user acts, like logging into a website. These tools calculate risk scores, which managers track over time to validate successful fraud prevention approaches. Another method is transaction monitoring, or catching fraudsters at the payment stage with stolen credit card information. For instance, a card BIN lookup can instantly verify credentials.

⚙️MACHINE LEARNING IN FRAUD MANAGEMENT

Benefits

• Higher Accuracy and Efficiency: While large datasets can make it challenging for humans to find patterns, unsupervised learning uses math and statistics to find hidden patterns in a sea of unstructured data. According to a whitepaper by computer scientists from the University of Jakarta, ML algorithms achieved up to 96% accuracy in reducing fraud for eCommerce businesses. In addition, the software runs twenty-four seven (i.e., 24/7), and attacks, especially from operators in different time zones, don’t adhere to the nine-to-five.

• Cost-Effective Solution: Unlike hiring more RiskOps agents, ML software can parse through all the data you throw at it, regardless of the volume. This software is ideal for businesses with seasonal ebbs and flows in traffic, checkouts, or signups.

Drawbacks

• Less Control: This is especially true of Black Box ML engines, which can make mistakes without anyone noticing them.

• False Positives: If a legitimate action is marked as fraud, the customer experience suffers. Fortunately, astute ML practitioners can fine-tune algorithms to uncover more false positives or more false negatives, as no technique is perfect.

Nevertheless, human reviewers often usurp automated systems for Anti-Money Laundering (AML) detection and reviewing high-value, low-volume transactions, such as payment for a jewelry store or high-end electronics. Plus, White Box ML techniques can give fraud detectors control over the system by describing why a risk rule was suggested. However, if these prove too inefficient, Black Box ML may be a better choice. They offer “set and forget” mode, a perfect solution for small businesses and nonprofits that don’t frequently tweak their risk rules. Regardless of the ML method, AI-driven fraud prevention is industry-agnostic. For instance, fraud detection only needs transactions (e.g., dollar amount, credit card number, etc.) and user data (e.g., IP address, VPN or proxy, device type, etc.) to work.

🥸4 EMERGING ARENAS FOR FRAUD

1. Online Stores and Transaction Fraud: Retailers are projected to lose $50.5B in fraud next year. So, after letting a White Box ML system run for a while, one can identify high-risk items, shipping information, and, of course, card payments.

2. Financial Institutions and Compliance: Fintech companies, brokerage firms, investment banks, and even insurance providers have strict compliance requirements to avoid regulatory fines, which have reached record heights in recent years.

3. iGaming and Bonus Abuse or Multi Accounting: Online gaming companies, casinos, and betting platforms must do their best to ensure all the players are real. They also tend to offer high-value rewards to new customers. This reward system creates a double incentive for fraudsters to create multiple accounts (i.e., multi-accounting) to claim the signup bonuses as well as engage in collusive play. 2021 saw a 43% increase in online gambling identity fraud, which proves that measures are needed more than ever.

4. Buy Now, Pay Later (BNPL): Last year, the share of purchases using BNPL increased by 14%. BNPL options typically involve four separate involvements, quadrupling opportunities for fraudsters. Since BNPL accounts are essentially online digital wallets, all that's required is an Account Takeover (ATO) to acquire user credentials. Synthetic identity fraud is the most popular acquisition method, where scammers paste together pieces of information stolen from real people (e.g., a name, a birthday, and a social security number). One report from Sift, an industry leader in digital safety, found that fraud attacks on BNPL have increased by 54% since 2021.

Ultimately, fraud prevention protects consumers just as much as large companies. Though the amorphous blobs (i.e., no shape or structure), also known as America’s beloved corporations, may not care about you individually. They cherish their reputation and the almighty bottom line. The cynic might say Machine Learning (ML) for fraud is just another AI application to achieve their selfish ends. As is often the case, the cynic’s observation is accurate, though the motives ascribed to America’s corporations may not be as valid. Efficient, scalable fraud detection, especially in areas with less sophisticated users such as gambling and BNPL, simultaneously protects the most vulnerable consumers and the least vulnerable corporations. The world is a weird place.

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