Fraud Prevention: Definition & How It Works

Fraud detection aims to identify fraudsters and fraudulent activity while preventing the loss of money and/or property. There are a variety of fraud detection systems and tools used to keep up with the ever-evolving digital threats, especially in the financial, health care, government, insurance, and retail industries. 

Adaptive analytics and machine learning are vital components of fraud detection that use data analysis techniques to detect fraud. The Federal Trade Commission (FTC) reports that there were more than 2 million reports of fraud from consumers in 2020. 

Fraud detection is important for protecting both consumers and companies alike.

What is fraud detection?

Fraud detection is incorporated by businesses into their policies, security measures, and websites to help identify unauthorized activities. It involves a set of analyses and processes to both prevent and detect fraud. 

Fraud can include a variety of different methods and techniques, such as the following:

• Identity theft
• Stolen and fraudulent credit card transactions
• Insurance scams
• Account takeovers
• Health care fraud

Companies use fraud detection systems to ensure that transactions are legitimate, and users are really who they say they are. Effective fraud detection uses a multifaceted approach to prevent and identify unauthorized actions.

How fraud detection works

Due to the variety of fraud and the adaptation of threat actors and cybercrime, fraud detection must be adaptive and often uses machine learning, or AI (artificial intelligence) to prevent and identify unauthorized transactions. Fraud detection works by looking for behaviors or actions that are out of the ordinary for a user or matching patterns of known fraudulent techniques. 

Fraud detection systems can trigger an automated response to either ask for further verification and increase the authentication security or, depending on the risk, shut down and block the transaction completely. Fraud monitoring and detection systems typically run in the background, so a consumer’s experience is not impacted unless an action or behavior is flagged for further investigation or authorization.

Who benefits from fraud detection?

Any business or company that conducts online transactions can be at risk for fraud. Fraud detection in banking and the financial sector is extremely beneficial to these businesses that have a high risk for being victims of fraud. Health care, insurance, and financial companies all benefit from fraud detection systems as do government, law enforcement, and public sector agencies. 

Cases of fraud continue to rise. On average, it costs organizations about 5% of their annual revenue. 

Consumers also benefit from fraud detection programs, as it can protect individuals from account takeovers, identity theft, and financial loss. Identity theft (a form of fraud) costs Americans $56 billion in 2020. 

In addition to financial loss, it can take a lot of time and effort to undo the damage done by cyber criminals to regain accounts and restore identities. Fraud detection can prevent this and work to minimize potential losses.

How do you detect fraud?

Fraud detection uses both statistical techniques and machine learning. Statistical techniques use data collection, data preparation, data analysis, and data reporting methods to identify fraud and unauthorized behaviors. Groups of data can be analyzed to predict outcomes and determine legitimate actions. 

Examples of statistical data analysis include:

• Computation of user profiles.
• Data preprocessing to detect, validate, and correct errors.
• Matching algorithms for behaviors of users compared with older user profiles or models to detect anomalies.
• Regression analysis to look at relationships between two variables.

AI, or machine learning, uses data analysis and data mining techniques that are either classified as supervised learning or unsupervised learning. Data mining sifts through vast amounts of data to find meaningful patterns. 

Machine learning can predict the likelihood of activities or behaviors being fraudulent, which can then trigger a further response or investigation within the system. Machine learning can help to reduce the number of “false positive” fraud reports and balance the friction of the user’s experience versus risk calculations.

Supervised learning techniques for fraud detection

Supervised learning techniques using AI are able to detect fraud based on patterns that have already been recorded. Once a pattern of fraud is recognized, it can be input into the system. The AI will sort through all of the thousands of types of fraud that have been logged and can match fraudulent behaviors this way. 

Cases of fraud are manually entered initially by a human operator. Then, machine learning can flag when these patterns are repeated or show up again.

Examples of supervised learning data analysis for fraud detection include:

• Bayesian learning neural networks. The Bayesian network can be designed to determine legitimate actions versus fraud based on the likelihood of an event with specific set data points. This type of supervised learning data is helpful with auto claims, for example, as it can tell the probability of the claim being valid based on imputed data sets. 

          This method is also helpful for detecting credit card fraud, medical insurance fraud, and telecommunications fraud.

• Supervised neural networks. These adaptive networks can work to generate a fraud risk score based on expected behaviors. If the behavior of a user does not fall within set parameters, it can be flagged and scored based on the perceived possible risk.

• Link analysis. This type of supervised learning can link people together based on social networking and record linking. For instance, known fraudsters are input into the system, and link analysis will match up any related users or transactions.

• Hybrid knowledge/statistical-based systems. These use data mining of a large database of customer transactions where knowledge is integrated with statistical power. It can detect anomalies and flag potential fraudulent behaviors.

The use of unsupervised learning methods

Unsupervised learning differs from supervised learning in that the AI is looking to detect new patterns of fraud and seeks outliers, or things that are outside of the typical and recorded fraudulent behaviors. In this sense, the AI “learns” to adapt and find novel types of fraud, as bad actors are consistently evolving their approach.

Unsupervised learning can be blended with supervised learning techniques for a more comprehensive approach. Take link analysis, which can look for relationships between known threat actors and other potential fraudsters. This can be taken a step further into link discovery that seeks to match groups and behaviors that are seemingly unrelated to fraudulent activities. 

Machine learning can flag suspicious behaviors or cases of potential fraud for further investigation by a human analyst.

Fraud prevention as a dynamic approach

Fraud prevention efforts can save consumers and companies time and money in the long run. Optimal fraud detection systems will include a comprehensive approach to minimize potential losses. 

Fraud is always going to be an issue, as cybercriminals continue to find and use new methods. Fraud detection and prevention models have to grow and adapt. 

Limitations to fraud prevention methods include a lack of enough data in public datasets. Sharing data between organizations and globally can help to lower and thwart future fraud behaviors.

References

New Data Shows FTC Received 2.2 Million Fraud Reports from Consumers in 2020. (February 2021). Federal Trade Commission (FTC).

2020 Report to the Nations: Organizations Opting for More Civil Litigation, Internal Punishment. (July/August 2020). Association of Certified Fraud Examiners (ACFE).

Total Identity Fraud Losses Soar to $56 Billion in 2020. (March 2021). Business Wire..

5 Keys to Using AI and Machine Learning in Fraud Detection. (July 2018). Fico.

Identifying Fraudulent Transactions in Mobile Payments. Stanford University.