Staying Ahead of the Game: The Evolution of Fraud Detection Management with Next-Gen Technology
In today's world of digital transactions, fraud has become one of the biggest challenges faced by financial institutions, retailers, and other businesses that deal with sensitive customer information. Fraudsters have become increasingly sophisticated, using advanced techniques to evade detection and steal personal and financial data. To counter this, the state of fraud detection management has evolved significantly over the last few years but so has the complexity of fraud and fraudsters.
As fraudsters become more sophisticated, the need for issuers to keep up with these changes has become increasingly important. While issuers have rightfully evaluated rules and brought in new technologies like AI, none of these will be able to have the impact of good data, which is comprehensive, contextual, timely, and easy to add, update and upgrade. Unfortunately, issuers and fraud engines have traditionally struggled with accessing good data. Access to data has been based on batch-files, limited sets of transactions and customer information that are often difficult to update and generally static. Models used to detect fraud have been static and often consistent across the industry, which means they lack context.
Real-time data access is very crucial in detecting fraud, as it allows issuers to detect and respond to fraudulent activity as it occurs. Access to real-time data also helps issuers to identify patterns and trends that may indicate fraud. By analyzing real-time data, issuers can quickly identify anomalies and take action to prevent fraud before it occurs. In order to stay abreast of the ever-changing nature of fraudulent activities, issuers must allocate resources towards acquiring technology that furnishes up-to-date, all-inclusive, contextually relevant data that can be effortlessly updated and is also delivered promptly.
The use of next-gen technology service providers can help issuers address these challenges. One such technology is event-based data access, which provides access to data for system, customer, and transaction events. This allows issuers to capture data at various touchpoints, providing a more complete picture of customer behavior.
In addition to event-based and real-time data access, issuers need a data management system that is easy to update and upgrade. This can be achieved by using tags, vectors, and other similar techniques. This approach makes it easier to add or remove new data points without having to rebuild the entire data infrastructure. It also allows for easier data integration with third-party systems, making it possible to leverage data from a wider range of sources.
A Next-gen technology service provider can also help issuers stay ahead of fraudsters by using machine learning and artificial intelligence algorithms to detect patterns and trends in customer behavior. Machine learning algorithms can analyze large volumes of data and identify patterns that humans might miss. By using machine learning algorithms, issuers can detect fraud faster and more accurately than traditional fraud detection methods.
Another way that next-gen technology can help issuers stay ahead of fraudsters is by using biometric authentication, dynamic PINs and dynamic CVVs that change after every transaction. Biometric authentication uses physical characteristics such as fingerprints or facial recognition to verify the identity of customers. Biometric authentication, dynamic PINs and CVVs are more secure than traditional authentication methods such as static passwords or PINs, which can be easily stolen or hacked. By using biometric authentication, dynamic PINs/CVVs issuers can reduce the risk of fraud and protect their customers' assets.
In conclusion, fraud detection management has evolved significantly over the last few years, but so has the complexity of fraud and fraudsters. To keep up with the evolving complexity of fraud, issuers need to invest in technology that provides comprehensive, contextual, easy-to-update data that is also timely. Event-based data access, real-time data access, and easy-to-update data management systems can help issuers stay ahead of fraudsters. Machine learning algorithms can also help issuers detect fraud faster and more accurately than traditional fraud detection methods. By using these next-gen technologies, issuers can protect their customers' assets and maintain trust in their brand.
About Author:
A 20+ year silicon valley industry veteran, Gary holds multiple patents in the mobile and wireless industry. Prior to joining Zeta, Gary was the Chief Revenue Officer at Ondot Systems. He has also held executive level positions at Obopay, Nokia Financials Services and Aruba Networks. He comes with over a decade of experience at Zebra (through multiple acquisitions — Motorola Solutions enterprise division & Symbol technologies), where he helped pioneer the WiFi market to automate supply-chain operations. At Zeta, Gary is responsible for the company’s go-to-market, operations, growth and overall financial performance.