You cannot read about fraud detection and prevention without seeing the phrase “real-time”. To be effective, anti-fraud solutions need to work in real-time, synthesizing thousands of data points, monitoring transactions across multiple channels, and analyzing patterns. But why? Why are real-time solutions so critical? Let’s take a look.
Conventional Fraud Detection Systems
Traditional fraud detection systems analyze historical data to create predictions about when fraud is likely to occur, and although these systems can detect fraud, they are slow, ineffective, and allow a significant amount of fraud to sneak through the cracks. They also typically look at fraud in silos, analyzing transactions from each payment channel on their own.
Often, these systems flag transactions that fall outside of certain parameters and present them to the bank’s fraud prevention team for manual review. For instance, if a check presented on a new account is for over a certain amount, the system may sense potential fraud and alert your team. Or if a customer is using their card in a different city than usual, the system may assume fraud and deny the transaction.
Friction for Customers
Unfortunately, the conventional approach to fraud detection creates friction for customers. When your fraud detection system rejects a customer’s purchase when they’re on the road, for example, your customer has to deal with the inconvenience and embarrassment of having their transaction denied.
They might turn to a card from another financial institution, potentially disrupting your relationship and convincing them to rely on that financial institution instead of yours. Alternatively, they may need to call your customer service line, sit on hold, explain the situation, and hope the transaction goes through when they try again.
In all cases, the customer experiences friction which directly reduces their satisfaction with your financial institution.
Access for Criminals
At the same time, conventional fraud detection systems also increase access for criminals. Skilled scam artists can easily figure out a financial institution’s parameters for fraud detection and focus on scams that aren’t likely to arouse suspicion.
The siloed approach to transaction analysis also helps to increase criminal access. Here’s a very simple example. Imagine a scam artist uses stolen social security numbers to open several new accounts, but they put their own P.O. box number on every single one of the accounts. Then, they write checks from one account to the other, taking out the cash when it’s available in one account and leaving the other depleted.
A siloed approach isn’t likely to catch the signs of fraud in this type of scam because it looks at account opening and checks in different silos. In contrast, an integrated fraud detection solution looks at countless data points across multiple transactional channels and is much more likely to sense the red flags in this scenario.
The Need for Proactive Fraud Detection
Looking at historical data in silos reduces cooperative learning and impairs decision making. The conventional approach to fraud detection overlooks a lot of fraud and increases payment denial for false positives. This simultaneously degrades the customer experience, reduces customer retention rates, and increases the risk of fraud at your financial institution.
To avoid these negative business outcomes, financial institutions need to move away from ineffective fraud detection systems that analyze historical data and rely on time-consuming processes such as moving data from operational databases to data warehouses for analysis. And they need to move toward systems that analyze live transactional data in real-time.
Real-time analysis of live data is the key to effective fraud detection and prevention.
Analyzing Billions of Data Points
Billions of data points move through your financial institution every day, and to detect fraud, you need the ability to analyze all of them. Effective real-time fraud analysis solutions look at data points such as behavior, devices used, user actions, customer preferences, geo-locations, third-party consumer information, e-commerce transaction profiles, and more.
They harness these data points, analyze them instantly, and make micro-decisions that guide your fraud prevention efforts. Relying on machine learning, these systems work with high levels of accuracy and at near-instant speed.
To get a sense of how real-time solutions work, imagine a customer signs into their online account using a different mobile device than usual or makes two transactions in a row when they typically only engage in those types of transactions once a month. A conventional fraud detection system is likely to flag both of these transactions as fraudulent which places friction in the customer experience.
In contrast, a system that relies on real-time analysis looks deeper than the single pattern aberrations, makes a variety of micro-decisions, and then calculates a risk score.
In these situations, the micro-decisions may deal with the location of the mobile device, the profile of the card used and account holder, the IP address, and device fingerprints. Additionally, they can also look at other features of the transactions. For instance, if someone is doing a card-not-present (CNP) transaction, the system can look at transactional data such as shopping cart info, shipping and billing information, and product details, and it can take all these data points into account when deciding whether or not to flag the system for fraud.
Real-Time Fraud Detection for Your Financial Institution
Are you mired in conventional fraud detection systems that analyze historical data? Are you impairing the customer experience while increasing your risk of fraud? Then, you need to upgrade to real-time fraud detection and prevention solutions. To learn more, contact us at SQN Banking Systems today.