How AI Detects Check Fraud

Artificial intelligence (AI) has reshaped the finance industry. It’s a key part of the growing financial technology (fintech) movement, and while it has many applications, fraud detection is one of the most promising.

AI fraud detection can uncover scams that traditional methods struggle to catch. Check fraud is the perfect example.

What Is Check Fraud?

Check fraud covers any scam involving fake, bad, or forged checks. One of the most common forms is a scheme where criminals ask for some form of payment but offer a check for a much larger sum in return. However, once victims attempt to cash it, they discover it’s fraudulent, leaving them without reimbursement.

The law requires banks to make funds available within two days of a deposit. Consequently, a bad check may initially clear, but once it bounces, the financial institution will take the money back. This delay makes it hard to spot such schemes.

Other examples involve paying for goods with a fake check, altering legitimate checks to steal money, or impersonating bankers to use their funds. Whatever the specifics, millions of consumers fall victim to these scams. Even though checks are less common than they once were, they’re still a favorite of scammers because conventional prevention measures often can’t stop them in time.

Benefits of AI Check Fraud Detection

Given the slow nature of check transactions, fraud detection must identify suspicious documents before anyone cashes them. AI’s speed and accuracy make it an ideal tool for that purpose.

An AI model can determine if something doesn’t fit a pattern within seconds. In the context of check fraud, that can mean identifying legitimate signatures, verifying a check’s authenticity, or comparing a transaction to a user’s history. While a human expert can technically do the same, it’d take far longer, and people may miss crucial details AI picks up on.

As a testament to AI’s advantages here, 75% of large banks already use the technology, and fraud detection is a big reason. Detecting bad checks requires careful attention to detail and real-time results, and AI delivers both.

How to Use AI to Detect Check Fraud

Because check fraud is such a broad category, the AI models that detect it vary widely. However, building and deploying these algorithms follows a few consistent steps.

1. Choose an Appropriate Model

Like all machine learning applications, AI check fraud detection begins with selecting the optimal model. The ideal technique for this use case depends on the types of scams the end user hopes to detect.

Solutions scanning physical checks must be machine vision models. As such, K-means clustering is a solid choice and easy to implement. However, more complex algorithms may be necessary to reduce false positives, given the sensitivity of the application.

2. Select and Tune Features and Parameters

Once data scientists decide on a machine learning technique, they must determine what features to analyze. Common ones in check fraud applications include routing numbers, check amounts, bankers’ names, spelling, and a user’s transaction history. Including more features can improve model reliability but will also increase complexity.

Businesses must also rank features so the model can interpret them effectively. Unexpected deviation for a check number is less suspicious than the spelling of the user’s name, so it shouldn’t carry the same weight. Similarly, data scientists must tune the model’s parameters and hyperparameters to balance accuracy and false positives.

3. Collect Relevant Data

Organizations must then collect enough relevant data to train these AI solutions. This step can be difficult in fraud detection for a few reasons. First, while information abounds today, between 80% and 90% of it is unstructured. Consequently, it may take cleansing and enriching before it’s ready for use.

Collecting financial info also introduces privacy concerns. Machine learning needs enough examples to be reliable, but providing real bank information is risky. Synthetic data may be an ideal solution. Alternatively, teams can anonymize real-world details.

4. Train the Fraud Detection Model

After the business has gathered enough data, it can train the model. Because clustering and deep learning are often unsupervised, this process can be less involved than other AI applications. However, it may take additional time and adjustment to achieve desired results, as fraud detection algorithms should achieve an unusually high level of reliability.

Given these models’ complexity and high standards, training can be a long, expensive process. Still, the long-term benefits are often worth it. The U.S. Treasury recovered over $375 million in 2023 after deploying an AI check fraud detection solution.

5. Deploy and Refine

Banks can deploy the algorithm once it reaches an optimal level of accuracy and false positives. This often looks like placing checks under a machine vision camera before accepting them or continuously monitoring user accounts to detect potential fraud. However, the AI solution will need ongoing adjustment in all use cases.

All machine learning applications are challenging, and check fraud includes too many variables for AI to be perfect immediately. Consequently, it often requires additional supervision in its early stages. As organizations use and refine it, though, it will become accurate and reliable enough to work on its own.

AI Is the Future of Fraud Prevention in Fintech

Check fraud has grown in recent years, but so have the tools to detect it more accurately. AI provides the speed and accuracy banks need to protect themselves and their customers from these scams. While the technology still requires careful implementation and adjustment, it could revolutionize fraud detection and prevention.

Originally posted on OpenDataScience.com

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