The right analytics are the foundation of a successful fraud detection and prevention strategy. By collecting and analyzing data about fraud incidents, you gain insights that you would not have if you looked at each case of fraud in a silo. You uncover hidden patterns, and you gain the ability to stop fraud before it occurs.
Ready to talk about how fraud analytics can help protect your bank? Then, contact us at SQN Banking Systems today or keep reading for a comprehensive overview of fraud analytics.
What is fraud analytics?
Fraud analytics is collecting and analyzing data to reduce, detect, and avoid fraud. Focusing on the right analytics improves fraud detection, but more importantly, it allows you to identify patterns so that you can stop fraud before it occurs.
How does it work?
Fraud analytics combines advanced technology and human interaction to detect fraud, either before or after the transaction takes place. The process involves collecting data, analyzing it for patterns, and identifying anomalies. For example, bank fraud detection software driven by analytics gets to know the spending patterns of account holders. Then, it flags transactions that don’t fall within the customer’s usual patterns.
The importance of fraud analytics in 2024
Traditional fraud detection methods are often still important, but they are not enough. The perpetrators of bank fraud know how to get around traditional fraud detection methods. They’re constantly evolving their practices, and if your bank isn’t keeping up, you are at risk.
Digital bank fraud presents a significant risk for banks. Account takeover, in particular, is a growing risk, and to combat it effectively, you must double down on data analytics.
Take identity verification for example. For in-person transactions, your tellers or bankers can easily verify your customers by checking their IDs. But what about when your customers call your customer service line, use your app, or engage with your bank online? To minimize the risk of fraud, you need tools that analyze multiple data points as your customers interact with your bank across multiple channels.
For instance, if your customer is calling your customer service line, can you check their return phone number and use biometric data such as their voice to identify them? If they’re using your app, do you only request a username and password? Do you also take into account other data points such as their IP address, device, or their ability to provide a code through multi-factor identification? Ideally, you need fraud mitigation tools that can help you analyze multiple data points and patterns.
Fraud metrics
Your bank should leverage fraud analytics to improve fraud detection, but how do you know if your efforts are working? That requires tracking the right metrics. Here are the most critical KPIs you should consider tracking to monitor the success of your fraud detection efforts:
- Fraud rate – The number or amount of fraudulent transactions that occur at your financial institution, measured as a percentage of the total number or amount of total transactions.
- Incoming pressure – The percentage of attempted transactions that were proven to be fraudulent.
- Approval rate – The percentage of transactions approved by your financial institution. The ideal rate varies. For instance, if you have a lot of incoming pressure, the approval rate may drop. On the other hand, a low approval rate may indicate excessive false positives.
- Precision — The percentage of declined transactions that were fraudulent. The inverse of this number is your false-positive rate. For instance, if you have an 80% precision rate, you have a 20% false-positive rate. That means that 20% of the transactions declined for suspected fraud were legitimate.
- Hit rate — The percentage of transactions flagged by your fraud software that were actually fraudulent.
- Catch-rate – Sometimes called recall, the catch rate is the percentage of fraudulent transactions that were declined. You want the highest catch rate possible, but you also need to ensure that you’re not catching too many legitimate transactions and adding friction to the customer experience.
- Decline rate – The percentage of transactions that were declined due to suspected or actual fraud. Note that this number doesn’t tell you much on its own. You must contextualize it by looking at it along with your catch and precision rates.
- Good user approval rate – Portion of approved transactions that were legitimate. You want this number to be as high as possible because the inverse is the number of approved transactions that are fraudulent. However, you don’t want to push this number higher by increasing your false positives.
When choosing a fraud solution, talk with prospective providers about the data used by the system to detect fraud. But also, ask them how you can monitor the efficacy of the system with KPIs like the ones listed above.
Types of fraud that you can detect with fraud analytics
As a banker, you need tools that focus on your most significant fraud risks. As noted above, digital risks are on the rise, but fraudsters are also continuing to exploit traditional payment methods such as checks. Use fraud analytics to detect and reduce the following types of bank fraud:
- Account takeover – When someone takes over a customer’s account and then uses their cards or checks to make purchases. For example, they might steal a customer’s logins through a phishing attack so that they can sign into their account.
- Check fraud – Thieves steal checks out of the mail or order them after taking over an account. Then, they forge checks at retailers, cash checks with forged identifications at checking cashing facilities, or deposit checks into fraudulent bank accounts for cash.
- Credit and debit card fraud – When thieves use stolen cards to make in-person purchases or they use card details for card-not-present transactions.
- New account fraud – To commit various types of fraud, thieves often use false credentials to open new bank accounts. They may also attempt to take out loans or commit mortgage fraud.
As you can see, fraud doesn’t just occur at the transactional level. If you’re only monitoring payments for signs of fraud, you risk missing the lead-up. You need tools that help you monitor multiple aspects of customers’ transactions with banks.
Data analytics goes beyond static rule sets:
Legacy fraud solutions used static rule sets. The tool flagged transactions as fraudulent if they fell outside of certain rule sets, such as if a check was over a certain amount, if an account had only been open for a certain amount of time, or if a transaction was initiated in another country. Then, based on these rules, the software would stop the transaction or let it go through.
This setup lets fraud through if it falls outside of expected parameters, and it creates high levels of false positives. A false positive is not a case of better-safe-than-sorry. It’s a situation that impedes your customers from using their account as desired, and by putting friction into the customer experience, you risk driving customers away.
The answer? Data analytics. Fraud detection solutions built around data analytics collect massive amounts of data. Then, they use machine learning to analyze the data and identify transactions with a high risk of fraud. Rather than issuing a yes-no decision, these solutions assess a risk score for every transaction. Then, they apply security based on the risk level. For instance, they may request step-up authentication from your customer or flag transactions for manual review by your fraud team.
How to deploy fraud analytics
Find a fraud prevention partner to help you. Look for solutions that cover all of your transactions and that can scale with you. Consider working with a company that offers hosted fraud solutions so that you can save on infrastructure and IT costs.
Why now?
Banks that let their customers fall victim to account takeover, data theft, and other types of fraud put their bottom lines at risk. They hurt their reputations and damage relationships with customers. To protect your financial institution, you need to embrace analytics and take a proactive approach to fraud.
Every fraud incident leaves a wake of data in its trail. The banks that harness the data and use it to protect their clients and their financial resources are poised for success, while the banks that overlook analytics risk bleeding customers and losing cash. Beyond that, a tightening regulatory environment and insurance requirements demand that banks take a stronger approach to fraud.
Are you using legacy anti-fraud systems that only detect fraud after it occurs? Does your fraud detection software use outdated static rule sets that miss fraud and add unnecessary friction to the customer experience? Are you wasting resources on labor-heavy fraud mitigation? If you’ve answered yes to any of these questions, then, it’s time to leverage data to reduce fraud, improve customer relationships, and optimize your labor resources.
To learn more, contact us at SQN Banking Systems today. We can start with an analysis of your risks, and then help you select the solutions and services you need to protect your financial institution.