Fraud and risk managers and analysts at community banks and credit unions face similar hurdles when selecting a fraud analytics tool that meets their fraud management goals.
They are equipped with droves of transaction, merchant and cardholder data, but don’t quite have the time or resources to know if their fraud detection efforts are effective. Nor do they always know what to do with all that data. They also don’t always have access to enough data, or the right tools. The end result is a gap in fraud detection and prevention efforts that makes it difficult to balance fraud costs and expense ratios.
The pain points among fraud professionals are also common. They are burdened with evolving fraud threats, compliance challenges, and an inability to know what fraud risks are coming and what high-dollar events might hit next.
Luckily for financial institution leaders, the evolution of cloud technology, enhanced data security and the application of AI and machine learning technology has paved the way for fraud analytics tools to address these pain points.
To help guide your decision making process as you ramp up your fraud mitigation efforts, we’ve broken down 5 questions worth asking yourself when determining how to choose a fraud analytics tool.
The ability to move from a reactive to proactive fraud analytics tool can make all the difference in your ability to get ahead of the spread of fraud when small and big incidents occur. PG Research & Advisory Services’ data indicates it takes an average of 3.6 fraudulent card transactions before a fraud incident is caught and stopped. The fraud losses that can occur during that string of transactions build quickly, not to mention the reputational costs.
The only way to overcome these reactive measures is to rely on a fraud analytics tool that leverages machine learning technology built with automated, predictive pattern analysis. This comprehensive approach to fraud data provides visibility into risk both in and outside your financial institution. Actionable fraud analytics tools can spot trends to identify where threats are forming, what transactions are probable versus confirmed fraud, and where/how a financial institution needs to adapt its fraud strategies.
Speaking of data visibility, this is another key question to ask yourself as you evaluate fraud analytics tools. Does your team have access to your own cardholder and merchant transaction data — or do you rely on your processor or call center to alert you about threats across your own card portfolio? Not having access to enough data leaves an incomplete picture of your true risk, and leaves your institutions vulnerable to risks that may be about to hit your institution. Rely on a fraud analytics tool that brings together pattern-driven analysis in one place.
A Fortune Business Insights report underscores the need for fraud detection and prevention tools to “provide a versatile mix of features to collect and analyze the data, produce correct conclusions, take actions based on results, and finally produce a comprehensive result.” Not finding a fraud analytics tool that lives up to this standard significantly impacts data visibility that often leads to false positive outcomes, the report notes.
Banks and credit unions are getting faster at detecting fraud, but fraudsters are getting faster at committing widespread types of financial fraud. Fraud analytics tools put your financial institution’s fraud mitigation efforts on an even playing field. Weekly and monthly fraud alerts aren’t close to fast enough for today’s fraud ecosystem.
Getting daily, automated reports that add consistency and data-driven tactical insight into your fraud management strategies helps ensure your team is getting ahead of incidents before they escalate. Wouldn’t you want to be alerted on a transaction that is only suspected fraud instead of learning about a card that went fraudulent days ago? Or a high risk merchant that is pushing fraudulent purchases through despite your efforts? Relying on fraud analytics tools that can provide daily and real-time data helps reduce fraud losses and future risk exposure.
Finding a fraud analytics tool that can supplement your rule writing and fraud management goals with robust AI and machine learning is the best bet in finding a solution that can help you meet your goals. We recommend taking a blended approach between rules and machine learning. Rules analysis is a powerful tool, but only when automated by sophisticated machine learning technology. Invest in AI and machine learning powered analysis tools
A new banking study provided insight from 300 banking executives to conclude that new technologies such as AI and machine learning will continue to drive the global banking ecosystem for the next five years. In fact, the study concluded that 77% of bankers believe that unlocking value from AI will be the differentiator between winning and losing banks.
Too often, fraud professionals are stuck relying on their own expertise and an entire team of other fraud analysts to manually sift through intel from call centers, processors and their own calculations. Internal analysis doesn’t provide intel into your full risk. Rely on wider network analysis that gives a comprehensive outlook.
Your fraud detection analysis shouldn’t be dependent on limited intel, as it makes your efforts less effective. If you don’t know risks you are building models around, or writing rules for, how can you effectively execute on your fraud detection and mitigation strategies? The best fraud analytics tools have access to data across the financial ecosystem to provide insight into where fraud trends exist so your team can get proactively spot threats before they hit your institution and tarnish your reputation with cardholders.
Learn More About Rippleshot Can Help Your Fraud Team Get Ahead Of Fraud Threats With Rules Assist.
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