The Ultimate Guide to Automating Fraud Detection
Financial institutions have been worrying about debit and credit card fraud for as long as e-commerce has existed—something our generation actually remembers coming to pass—and these digital criminals seem to find new methods to steal payment card information, just as quickly as we can figure out how to stop the last one.
Annual fraud losses are a major budget drain for financial institutions and a considerable inconvenience to their cardholders. Prioritizing a more effective and efficient approach is critical to staying competitive.
Read on for the top five things you can do to put smarter fraud risk management strategies into place >>>
Proactive vs Reactive
The first step in overhauling your fraud risk management is shifting from a reactive mindset to one focused on prevention. Classic fraud detection tools don’t alert you until a significant amount of damage has been done, meaning many of your cardholders’ information has been stolen and enough time has passed that they’ve likely been used for fraudulent purchases—exponentially increasing your losses.
If you’re able to quickly and accurately identify a breach at the point of sale (POS), the compromised cards can be canceled and reissued, all before racking up millions of dollars in damage.
Predictive Fraud Analytics
Analytics should be more than a summary of data points, instead empowering you to predict an outcome and help you take action.
Your analytics should include metrics like:
- False Positive Rate (FPR) - the ratio of actual fraudulent transactions detected vs. the number predicted
- Common Point of Purchase - the common merchant location where payment information is being stolen.
- Percent Fraud Capture - the estimated amount of fraud that has been prevented by declining risky transactions or re-issuing a fraudulent card.
Almost as important as having access to this data, is having a dashboard or report that clearly and concisely presents the critical information you’ll use to guide your fraud risk management initiatives, as well as one that updates automatically. Say goodbye to messy spreadsheets now and thank us later.
Fraud Detection Rule Writing
The time-consuming and painfully-manual process of writing fraud detection rules that we’ve all known (and hated) is difficult to efficiently scale and slow to address new types of attacks. Payment card fraud simply evolves too quickly for this reactive process to be effective in the modern world.
Combining rules with artificial intelligence is the future of fraud prevention. Let computers comb through that data, identifying higher-risk merchants or zip codes, for example, and automatically recommend data-driven rules that will work best for your business and your customers.
Gone are the days when you have to rely on your call center or wait for fraud losses to grow to massive proportions to initiate the rule-writing process.
Better AI Models
Artificial intelligence and machine learning are taking fraud detection and prevention to new heights, enabling your tools to review millions of transactions, detect events, and self-learn patterns.
Using AI, financial institutions can automate the updating of fraud models, often daily, and continually adapt to new fraud patterns. Timely analytics produce more effective rules, fewer false positives (i.e. mistakenly blocking transactions and reissuing cards to frustrated clients), and faster threat detection.
By incorporating machine learning and automation into your processes, you will transform your back office, create more effective fraud prevention strategies, save hundreds of work hours, and reduce operational costs.
People, Technology, and Data
One of the most pivotal pieces of getting fraud prevention right is having the right balance between a knowledgeable team, the tools they need, and the data to power them. Neglecting any part of this trio and your fraud prevention initiatives will suffer.
While we’ve gone over many of the features your fraud prevention technology should have, the way these tools integrate and their ability to access critical data is pivotal. The greatest artificial intelligence modeling solution is useless without massive data input. Is someone from your busy IT staff having to manually download data from one system and upload it into another, before any analysis can take place? Do you even have access to the data needed for this level of analysis?
If the answer is “yes,” it’s time to focus on integrating your tools and streamlining processes, allowing your team to focus on putting their new, data-driven fraud prevention strategies into play.
People, Technology, and Data
Keeping your financial institution competitive means being open to new technology and tools. The transition can feel daunting, but the resulting efficiencies and loss prevention are more than worth your time.
And remember, it doesn’t require expensive and complex solutions to get the job done. Many providers offer pre-packaged tools that address specific segments of this process, comprehensive, end-to-end solutions, and/or API plugins that connect to systems you already have in place. The options are endless.