Fighting Financial Crimes with AI: The Importance of Fraud Prevention

AI for Fraud PreventionAI for fraud prevention refers to the use of artificial intelligence technologies such as machine learning, predictive analytics, and anomaly detection to detect and prevent fraudulent activities in various industries such as banking, e-commerce, and insurance. By analyzing large amounts of data in real-time, AI can identify suspicious patterns and behaviors, helping organizations prevent financial losses and protect their reputation.

AI and Fraud Detection

AI for fraud detection can adapt its algorithms to counter dangers it may have never encountered before by learning from primary data, something that regular fraud software cannot accomplish.

Models of AI have the capability to assess the probability of fraud, decline transactions completely, or highlight them for further investigation when there is suspicion of fraudulent activities. This enables investigators to concentrate their efforts on the most promising cases.

For the detected transaction, the AI model can additionally provide cause codes. These codes inform the investigator of the areas they should search for. In addition, the AI model can provide cause codes for the transaction flagged. These cause codes help to expedite the inquiry by pointing out where the defects are.

What Is Fraud Detection with Machine Learning?

Machine learning is a group of artificial intelligence algorithms trained using your previous data to advise risk criteria for online fraud identification and prevention. The rules can prevent fraud or permit specific user actions, like shady login attempts, identity theft, or fraudulent transactions.

To prevent false positives and to increase the accuracy of your risk rules, you must mark prior instances of fraud and non-fraud while training the machine learning engine. The rule suggestions will be increasingly precise as the algorithms run longer.

Differences Between Blackbox and Whitebox Machine Learning

Differences Between Blackbox and Whitebox Machine Learning

Not all fraud protection solutions are equal, even if machine learning is typically a selling point for most vendors. Notably, there is a significant distinction between blackbox and whitebox machine learning.

  • Blackbox machine learning: This type of decision-making is automated and meant to operate in a “set and forget” manner. It can be excellent for small organizations that can avoid getting into the specifics of adjusting their risk regulations.
  • Whitebox machine learning: The program will explain why there is a proposed risk rule. This simplifies the process of recognizing areas of risk and provides fraud managers with more flexibility to improve their fraud prevention approach.

The Benefits of Machine Learning for Fraud Management

  • Faster and more effective detection: The technology can detect suspicious patterns and activities that may have taken human agents several months to uncover.
  • Decreased manual review time: Similarly, letting computers analyze all the data points for you can significantly reduce the time spent manually examining information.
  • Larger datasets yield better predictions: A machine learning engine gets more proficient the more data it gets.
  • Cost-effective solution: Regardless of the data you feed a machine-learning system, you need one to process it instead of hiring more RiskOps agents. A machine learning system can help your business grow without significantly raising risk management expenses simultaneously.

How to Use Machine Learning for Fraud Prevention

How to Use Machine Learning for Fraud Prevention

1. Feeding the input data

Every artificial intelligence or machine learning system needs data. In this case, it will be transactional information, such as trade-off value, brand SKU, credit card type, etc. But we’ll also include information about how users access the website, like, IP information, device kind, use of VPN, proxy, or Tor, and so on.

2. Generating the rules

Machine learning can produce two different categories for each rule:

  • One example of a single parameter rule, commonly called a heuristic rule, is a block if the IP is X.
  • Rules with many parameters are complex.

You can alter the accuracy thresholds to tighten or relax the triggering conditions.

3. Reviewing and activating the rule

Using fraud prevention technologies, the rules can be filtered by any data point, including their nature and expected correctness. The accuracy component, calculated using a challenging confusion matrix, is beneficial.

Machine-learning suggestions disable by default. The ON/OFF switch will enable them instantly. Also, it is possible to establish and modify the rule’s triggering thresholds manually.

4. Training the algorithm

The secret to improving accuracy and modifying rules is to provide feedback data. There are two ways to mark the actions and offer feedback:

  • Using the GUI: an easy, aesthetically pleasing method to note actions
  • You may programmatically keep operations by making API calls and using the Label API.

Regardless of how you label the activities, they must be either APPROVED, REVIEWED, or DECLINED. The algorithms retrain each day based on the data collected over 180 days. They are always accessible in your scoring engine and backend.

5. Testing rules on historical data

In a good fraud prevention program, you should be able to review previous cases to determine whether the rules would have been beneficial. The rules can turn on and off in a sandbox setting where you can see how accurate they are.

A confusion matrix based on prior transactions over the chosen period generates when a test runs, highlighting the rule’s projected accuracy rate. A confusion matrix, or an error matrix, is a table design for machine learning to display an algorithm’s performance. This allows you to determine the accuracy over a range of dates, which you can choose from the most recent hour to the most recent year.

Strategies for fraud detection and prevention using AI

Strategies for fraud detection and prevention using AI

Using Supervised and Unsupervised AI Models Together

Defense strategies based on a single, all-encompassing analytic technique will fall short due to the cunning and flexible nature of organized crime operations.  To fully implement next-generation fraud techniques, both supervised and unsupervised models are usable in fraud detection. Supervised learning is the most common machine learning across all areas, where a model develops using many accurately “labeled” transactions.

One of two categories—fraud or non-fraud—applies to each transaction. The algorithms are taught by consuming enormous amounts of labeled transaction details to identify patterns that best indicate legal activity.

A supervised model’s accuracy highly correlates with the volume of clean, pertinent training data used in its creation. When labeled transaction data is limited or nonexistent, unsupervised models are used to identify unexpected behavior. Self-learning must be applied to find patterns in the data that traditional analytics have buried in these situations.

Behavioral Analytics in Action

In behavioral analytics, machine learning evaluates and forecasts behavior at a detailed level across all facets of a transaction. Each user, merchant, account, and device has a profile that describes their routines.

Every transaction updates these profiles in real time, enabling the computation of analytical characteristics that provide precise predictions of future behavior. The profiles go into great information about both financial and non-financial interactions. Non-monetary transactions include but are not limited to address changes, requests for more cards, and password resets. A reliable corporate fraud solution includes several analytical models and profiles to monitor real-time transaction trends.

Developing Models with Large Datasets

According to studies, machine learning models’ effectiveness depends more on the volume and variety of data than on the algorithm’s intelligence. In terms of computation, it is analogous to human experience.

Thus, it follows that, wherever practical, increasing the dataset used to generate the predictive characteristics employed in a machine learning model could improve prediction accuracy. A large amount of transactional data needs evaluation to perform improved fraud detection and more accurate risk measurement for each individual.

Self-Learning AI and Adaptive Analytics

Machine learning excels at securing customer accounts, which fraudsters make exceedingly challenging and dynamic. Fraud detection experts should consider adaptive solutions intended to accelerate reactions, notably on marginal judgments, for continual performance development.

Accuracy is essential where there is a thin line between a false positive event and a false adverse event. Adaptive analytics, which provides a current understanding of a company’s risk characteristics, emphasizes this contrast.

Disadvantages of Machine Learning for Fraud Detection

The following are the disadvantages of Machine learning in fraud detection:

  • Reduced control: This is particularly true of blackbox machine learning engines, which may commit errors undetected.
  • False positives: The entire system will suffer if a simple action is mistakenly flagged as fraud without your knowledge. In that regard, a poorly calibrated machine learning engine might produce a feedback loop in which the future accuracy of your results decreases as more false positives go undetected.
  • No human comprehension: Good old psychology is hard to overcome if you’re trying to figure out why a user’s activity is suspicious.

5 Use Cases of Machine Learning for Fraud Detection

The use of AI to combat fraud transcends industries. Because all it needs to function is data, it has been implemented in a wide range of industries, including:

Online Stores and Transaction Fraud

Many fraud controllers use machine learning for many large eCommerce companies to determine why some transactions are not recognizable as fraudulent. After letting your machine learning system run for a time, you can find out which products fraudsters target the most, what shipping information poses the most significant risk, which credit card fraud transactions are not to mind to prevent excessive chargeback rates and more.

Financial Institutions and Compliance

To avoid regulatory penalties, fintech businesses, well-established financial institutions, and even insurance providers must adhere to stringent compliance rules. By implementing a machine learning system, many companies can obtain crucial information about distinguishing an accurate user profile from a phony one.

iGaming and Bonus Abuse or Multi Accounting

Casinos, betting sites, and online gaming organizations must make every effort to ensure that all participants are authentic. Also, they frequently provide valuable benefits to new clients. This gives fraudsters a double incentive to set up many accounts to engage in collusive play and claim the signup bonuses. Using a machine learning system, it is possible to examine data points that suggest questionable user behavior. This can help you catch cheating players, poker bots, and even bad affiliates that send lots of low-quality traffic to your website.

BNPL and Account Takeover (ATO Attacks)

Accounts with Buy Now Pay Later are evolving into online digital wallets. A user account is accessible by a fraudster, who can then use it to make unauthorized purchases of products and services.

Knowing how people access your site is an excellent method to safeguard accounts. You can learn how to verify your clients to protect their online accounts by executing a machine learning algorithm on the login data points.

Payment Gateways and Chargeback Fraud

Using human agents to review every transaction would be impractical, given that payment gateways must handle countless transactions as rapidly as possible. You may teach a machine learning engine to recognize fraudulent transactions that might otherwise result in chargeback fees, acting as a fraud-monitoring analytics system.


How can AI be used in fraud detection, and how do you detect transaction fraud?

Companies may save time and money using AI software to help them identify suspicious activities quickly and correctly. AI fraud detection is for input data provision, rule sets, rule reviewing, rule activation, model training, testing, and deployment.

Artificial intelligence can be a valuable technique for detecting fraud, whether for transaction anomaly detection or identity verification.

What are AI-based fraud detection and trends?

AI aims to identify patterns that could potentially result in fraud so that alerts raise immediately, accurately, or not. It enables the in-charge analyst to look into things more closely, which can result in acquittal or fraud prevention.

What are the advantages and disadvantages of AI in fraud detection and prevention in accounting?

Financial institutions can benefit from artificial intelligence and machine learning by reducing false positives and manual intervention. AML solutions built on artificial intelligence help improve the data quality and can detect suspicious transactions not detectable during the manual monitoring procedure. The benefit of adopting artificial intelligence and machine learning is that robots can learn independently. AI systems can track the transaction’s data points from start to finish, making it easier to spot deviations from the norm.

What potential drawbacks can employ AI Fraud Detection have? AI-based models give the user less control, are more likely to produce false positive results, and need more human comprehension.

How does artificial intelligence help banks reduce fraud?

Artificial intelligence allows for the programming of machines to enable self-learning. So, if any transaction does not fit the established pattern, it can be quickly classified as suspicious. The automatic signature verification and check fraud risk program built on artificial intelligence assists in lowering false positives through its self-learning mechanism.