Running in real-time to help you stop fraudsters in their tracks, fraud monitoring is important, and associated alerts can help keep you informed – and your defenses strong.
We’ve prepared this guide as a rundown of the basics of fraud monitoring and fraud alerts, both in banking and other sectors.
What Is Fraud Monitoring?
Generally speaking, the term “fraud monitoring” refers to the real-time surveillance of operations in order to detect possible instances of fraud events and stop them.
Fraud alerts are what is generated by the system when a fraud event has been detected, to let the person who oversees it know.
In practical terms, a fraud alert is generated and sent in order to provide information about fraud-related activity taking place in the background. For example, that a user has been blocked from signing up because they were deemed too suspicious.
They can also alert fraud analysts that there are cases awaiting manual review. For instance, that a shopper trying to check out received a medium risk score and the case should be investigated by a human.
In this way, one could think of fraud monitoring and fraud alerts as similar to a firewall, because the system is being monitored for instances of fraud and alerts are sent out when one is found and blocked.
For most modern fraud monitoring and prevention solutions, fraud alerts are customizable to the company’s needs and requirements, thus allowing for minimal disruption while important information still reaches the team members it needs to.
How Does Fraud Monitoring Work?
Fraud monitoring is designed to run 24/7, observing and assessing user behavior and analyzing data, with a focus on specific touchpoints – such as sign-up and payments. Here are the three key steps:
1. Gathering User Data
The first step is to ensure you have access to the right information that could point to fraud. This may vary depending on your business vertical, but most companies tend to look at:
- IP addresses
- Device data
- User actions (clicking on links, filling out forms, etc.)
- Payment information
- Transaction data
Note that the data can be taken in its raw form, or enriched via other methods to gather extra information. For instance, a reverse email address lookup can give you a user’s social media profiles, which is helpful for the next step.
2. Analyzing the Data/ Feed it Through Risk Rules
Once you have the data, it can be combined into detailed reports that can be manually reviewed by humans or, more frequently, fed into rulesets that define what happens next.
A simple example would be to compare the credit card country and IP address. A rule could decide that a mismatch increases the risk of fraud.
A more sophisticated example could take multiple data points such as login attempts, IP data, a timeframe, and create what is known as a velocity rule:
“If more than 4 accounts have logged in from the same IP address within 10 minutes, block the IP for 1 hour and generate an alert.”
This leads us to the third step of the process, which is all about the monitoring itself.
3. Triggering Alerts and Actions
A company will have hundreds of rules mentioned above. They are not mutually exclusive either: A specific interaction can trigger several rules, each increasing the risk score more, pushing the event onto a higher level of risk.
What is important is to decide what to do when a rule is triggered. You can simply mark the action as risky, calculate a risk score, or even issue real-time blocks, such as a declined payment.
You can also set up alerts that will notify a fraud analyst and ask them to manually review the action, to decide if it is indeed risky or not.
Why Are Fraud Monitoring and Alerts Important?
In simple terms, without real-time fraud monitoring tools and the alerts they generate, we wouldn’t be able to catch and stop fraud as it happens. And fraudsters continue to become bolder and more sophisticated in their methods.
Fraud and scam trends reveal that fraudsters are no longer solely targeting big companies with a lot to lose. Fraud has become a real threat to organizations of all sizes and types, worldwide. During the pandemic, analysts have noted an increase of 46% in fraud globally, which led to a surge in fraud attacks for three-quarters of merchants.
Meanwhile, spend on fraud management has grown 5x in just three years to 2022. In the UK, 1 in 2 businesses are victims of fraud, while Americans have lost up to $1.8 billion to individual instances of fraud, according to the FBI.
Overall, fraud in its various guises costs the global economy over $5.127 trillion each year according to estimates by Crowe.
How Does Fraud Monitoring Work in Banking?
Banks, neobanks, challenger banks, and financial institutions have their own unique fraud monitoring challenges. It often falls under the term account monitoring, but the steps are the same, as they include:
- Continuous transaction monitoring: banks use this term to refer to the continuous and close monitoring of transactions and online/in-app banking sessions. It is not only useful to combat fraud, but also a regulatory requirement to monitor transactions over a certain threshold in the context of AML.
- Continuous session monitoring: depending on the systems installed, this normally also extends to bank accounts themselves, including all moves to change associated address information, beneficiaries, issue new cards, etc.
Additionally, banking fraud monitoring may also include identity verification for KYC and AML, as well as login verification to ensure only the rightful account holder has access to features such as money transfers, adding new beneficiaries, or taking out loans.
What’s the Role of Machine Learning in Fraud Monitoring?
Machine learning systems can analyze historical fraud monitoring data in order to suggest new risk rules. Because the system is trained on existing fraud cases, it can extract patterns that may have been missed by human analysts.
Fraud detection with machine learning is an increasingly important strategy for a number of organizations, especially those that deal with thousands of data points and hundreds of potential attack vectors.
It should be noted that machine learning for fraud monitoring has some limitations. Because the algorithm will only learn if there is sufficient data, you may need to train it on data from other businesses at first. However, its efficiency increases the longer you keep the system running – as long as you label fraud cases properly.
Top Features of a Fraud Monitoring and Alert Solution
Fraud monitoring and alert platforms should be flexible to adapt to the customer’s needs, scalable to plan for the future and constantly in development to respond to the ever-changing fraud landscape.
Organizations that employ fraud monitoring solutions want convenience, so this process should be made as easy as possible, with try-before-you-buy solutions, free support, quick deployment, and transparency in processes (and pricing models).
Some key features to look for in your fraud monitoring software include:
- real-time monitoring
- email and phone data enrichment
- machine learning
- custom rules
- behavior tracking
- risk-based authentication
- fraud scoring
- manual queries
- graph visualization
- sandboxing capacity
- reporting and alerts
We’ll look at examples of how these are used below, while you can browse the individual topics on SEON’s website for further information.
The SEON Approach to Fraud Monitoring
At SEON, our goal is to fight fraud.
But in order to do so, we seek to empower decision makers and fraud analysts to adjust and fine-tune the level of fraud detection automation to their comfort level and to their industry and the threat landscape that surrounds it.
For challenger banks and neobanks, this means more flexibility and less customer churn, allowing them to better serve customers who seek to be free from the cumbersome processes associated with traditional banks.
In fact, SEON’s dynamic friction approach can help any business that wants to keep the customer journey pleasant and effortless, no matter the sector because it ensures legitimate customers are approved with minimal disruption. Only suspicious individuals will be asked for additional verification.
SEON’s holistic fraud prevention platform provides real-time monitoring of 100+ individual data points gathered via advanced methods including digital footprint analysis, device fingerprinting, behavior analysis and more.
They are then combined using the data enrichment module into easy-to-read profiles complete with risk scores.
These risk scores give a quick assessment of the risk presented by each user and/or action, ready for human decisioning and/or automatic actions based on customer-specific rulesets.
At SEON, risk rulesets can be selected and set in a number of ways, to provide for maximum flexibility:
- generated using whitebox machine learning from historical data
- adopted from industry presets made to respond to each sector’s most common pain points
- custom rules created manually, from scratch by humans
- combinations of the above
It should be noted that the above rules can be changed or expanded at any stage of the process from the user-friendly dashboard.
Frequently Asked Questions
This term is reserved for professional organizations that collect and maintain lists of individuals who may pose money laundering or other fraud-related risks, including PEP lists. They are linked to satisfying KYC and CDD mandates for companies in finance and fintech, and usually also run credit checks to verify individuals.
Normally used as a synonym of fraud monitoring, fraud risk monitoring highlights the risk management aspect of this process, sometimes being an integral part of an organization’s risk management software and/or strategy.
This largely depends on the risk landscape in your sector, your risk tolerance and a few more factors but in general terms, sophisticated end-to-end fraud monitoring solutions can be made as hands-on or set-and-forget as the customer would like. Minimal involvement is possible, though one should take into account whether it is suitable and safe too. Vendors such as SEON can help gauge and set this up for you.
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