Fraud Monitoring: Definition, Importance & Defences

Fraud monitoring is a fraud prevention strategy that works by continuously monitoring digital actions to detect fraud, recognise risks, and stop and prevent fraud attacks. It is regularly used by financial institutions to protect both customers and banks alike. 

Using artificial intelligence (AI), fraud monitoring programs can sift through large amounts of data in a short time, learning along the way to recognise threats as they emerge.

In 2020, there were well over 2 million fraud reports made to the Federal Trade Commission (FTC), a number that continues to rise as people shift to an even bigger digital presence. Fraud monitoring can help to minimise the risks and losses related to digital threats.

What Is Fraud Monitoring?

Monitoring behaviours and activities can help to prevent and detect fraud by continuously analysing all of the actions throughout an entire session. This goes beyond just a financial transaction and looks at the login, changes to account profiles, and any activities that are done on a customer’s behalf. 

Fraud monitoring can deal with current and evolving threats, as it looks at the whole picture to determine if something seems off. These anti-fraud systems can then react in real time to manage the threat and prevent losses to both company and customer. 

Fraud monitoring can:

• Prevent fraud.
• Flag suspicious behaviour.
• Detect fraud.
• Recognise emerging threats.
• Stop a threat in real time.

Types of Fraud Monitoring

Fraud monitoring looks for various types of fraud through detection and prevention measures. Continuous fraud monitoring looks at a typical user’s digital footprint (how they interact online) and continually watches for anomalies. Bad actors often use software bots to perpetuate fraud, and fraud monitoring systems can identify when there may not be a real person behind the actions being taken — when fraud is being committed. 

Continuous transaction monitoring looks at all of the user’s actions, from sensitive to non-sensitive ones. These anti-fraud programs will look at everything from start to finish to detect patterns of fraud. Fraud often follows specific patterns, and continuous monitoring can identify and flag these patterns as potentially fraudulent. 

Evolving Fraud Calls for Adaptive Methods

Standard methods of detecting fraud are not always enough, as bad actors are getting smarter and adapting to get around traditional measures. For instance, fraudsters are creating synthetic identities that can often pass a credit check and can go unnoticed as a legitimate customer is not being defrauded, which can be caught. 

Fraud detection and fraud monitoring tools need to constantly evolve to keep up with the bad actors. Ideally, they can stay one step ahead of them.

Detection of Fraud

Fraud detection prevents bad actors from making financial or other transactions through false means. It is an important aspect of fraud monitoring. 

One of the most basic forms of fraud detection is identity verification, which ensures that the user is who they say they are and actually a legitimate customer making the transaction.

Fraud can be perpetuated in a variety of ways, from taking over an account (identity theft) to stealing credit card information to embezzlement. Fraud detection methods must then also be dynamic and go beyond just verifying identity at the customer login. 

Fraud can impact many different industries and sectors, including these:

• Banks
• Health care
• Insurance
• Government
• Retail establishments

Fraud monitoring programs can screen for fraud and fraudulent activities often by using analytical models that can identify predictors of fraud based on patterns and models that fraudsters have used in the past. Fraud often follows historical patterns, and fraud detection watches for these patterns to spot potential takeovers or hackers in the system.

Role of Machine Learning

AI, or machine learning, can read patterns and data, using analytics to distinguish between fraudulent behaviours and legitimate customer interactions. 

Machine learning is efficient and can read vast quantities of data quickly without human interaction. It can also adapt and “learn” patterns over time to aid in spotting new and evolving threats as they arise. Machine learning can also decrease the number of “false positives” that are flagged.

There are several components to machine learning for fraud detection and monitoring.

• Behavioural profiles: Machine learning can learn and interpret the way that individuals, merchants, devices, and accounts act to recognise legitimate b