See how financial institutions can use Progress Corticon for a multi-layered fraud strategy, with custom rules and models, resulting in an enhanced customer experience.
Fraud is an unfortunate reality in the financial services industry. Billions of dollars are lost each year to fraud, and financial institutions are constantly working to detect and prevent fraudulent activity as quickly and accurately as possible. The stakes are high, as failing to catch fraud can damage customer trust and loyalty, not to mention direct financial losses. At the same time, incorrectly flagging legitimate transactions as fraud also erodes the customer experience and wastes resources.
Advancements in AI and ML are enabling more sophisticated fraud detection systems that analyze huge volumes of data in real time and make highly accurate decisions instantly. One such system, Progress Corticon, uses a rules engine and predictive analytics to detect fraud at the point of transaction across multiple channels. With Corticon, financial institutions can implement a multi-layered fraud strategy, using customized rules and models tailored to their unique needs. The result is fewer false positives, faster detection of emerging fraud patterns, and an enhanced customer experience.
Traditional fraud detection systems have some significant limitations that prevent them from effectively stopping fraud in real time. These legacy systems were designed mainly for detecting fraud after it has already occurred, not preventing it from happening in the first place.
They rely heavily on rules-based engines and threshold-scoring models that can’t adapt quickly enough to new fraud trends or patterns. Fraudsters are constantly changing their tactics, but it can take weeks or months for teams of analysts to manually update rules and models in traditional systems. By the time the updates are made, the fraudsters have usually moved on to something new. These systems also typically only analyze data in silos, missing the connections between data sources that could reveal complex fraud schemes. They don’t have a holistic view of customer interactions and behaviors across channels.
When it comes to fraud detection, time is of the essence. The faster suspicious activity can be identified, the less damage can be done. This is why real-time decisioning is so critical for fraud prevention in financial services.
Real-time decisioning leverages advanced analytics and machine learning to analyze transactions and user behavior in real time and determine the likelihood of fraud. Rather than reviewing transactions after the fact, real-time decisioning allows financial institutions to take action immediately when anomalous activity is detected. This could include temporarily freezing an account, notifying the customer or flagging the transaction for further review by an analyst. This is important because it allows you to:
With real-time decisioning, financial institutions can detect fraud at the first sign of suspicious activity rather than after funds have already been stolen. They can identify unusual login locations, device fingerprints, transaction amounts, recipient details and more—and act quickly before the damage spreads. Early detection of fraud also allows organizations to limit losses and reduces the resources required for investigation and recovery.
Real-time decisioning allows you to establish a baseline of normal behavior for each customer. Deviations from established patterns can then be flagged for review. With a nuanced understanding of typical activity, the system can minimize false positives and avoid wrongly suspecting legitimate transactions. This helps create a frictionless experience for customers conducting normal business.
When anomalous transactions are detected, real-time decisioning prioritizes the riskiest events for review by analysts. This allows investigators to focus their efforts on the most urgent alerts rather than wading through volumes of raw data. Analysts can then leverage contextual data provided by the system to swiftly determine whether the transaction is fraudulent or legitimate. Streamlining the review process maximizes the productivity of analysts and accelerates resolution times.
Corticon’s business rules management system (BRMS) and decision management platform enable real-time fraud detection and prevention in several key ways:
Corticon allows you to create and manage business rules separately from application code. This means fraud analysts can quickly build, test and deploy new rules to detect emerging fraud patterns without IT involvement. Corticon’s decision tables and decision trees provide an easy-to-use interface for fraud analysts to codify their expertise into logical rules.
Corticon evaluates rules and makes decisions in real time. Fraud detection rules can be applied to transactions as they happen to identify potential fraud quickly. This real-time evaluation and alerting allows fraud analysts to take immediate action to prevent loss.
Corticon’s rules engine is highly scalable and can process large volumes of data in real time, enabling organizations to monitor and prevent fraud across multiple channels and systems. This includes monitoring transactions across web, mobile and in-store channels, as well as integrating with external data sources.
Corticon’s BRMS allows new rules and rule changes to be quickly deployed to production systems. This agility is essential for fraud management where new threats emerge frequently and the rules must adapt quickly. Corticon’s hot deployment of rules into live transactional systems powers an agile, responsive approach to fraud detection.
Corticon’s platform is highly customizable, allowing organizations to integrate with existing systems and data sources, and to create rules that are specific to their business needs. This includes integrating with CRM systems, financial systems, and other data sources to provide a comprehensive view of customer behavior and transaction pattern.
No matter how your fraud detection system is built, Corticon provides an easy way to incorporate its real-time decisioning capabilities. With its blend of rules and predictive analytics, Corticon allows you to codify your organization’s policies and strategies for detecting and preventing fraudulent activity. By embedding Corticon’s decision services, your system has access to both contextual rules and machine learning models for enhanced agility and accuracy.
Enhanced detection accuracy: By leveraging a wide range of rules and algorithms, Corticon can identify patterns and anomalies in real time, reducing both false positives and false negatives. This enhanced accuracy helps financial institutions effectively detect and prevent fraudulent activities, minimizing financial losses and protecting their customers.
Improved operational efficiency: Automating the decision-making process with Progress Corticon leads to improved operational efficiency in fraud detection and prevention. By eliminating manual effort and streamlining workflows, financial institutions can save time and resources. Corticon’s intuitive interface allows business users to easily create and modify rules, reducing the reliance on IT departments. This increased efficiency translates into cost savings and enables resources to focus on more high-priority tasks, such as investigating suspicious activities and enhancing fraud prevention strategies.
Proactive fraud prevention: Instant approve/decline decisions stops fraudulent transactions in their tracks, minimizing financial losses and protecting the institution’s reputation. This proactive approach also enhances customer satisfaction by reducing the likelihood of legitimate transactions being declined due to false alarms.
Compliance and auditability: Progress Corticon’s built-in audit trail functionality assists with regulatory requirement compliance and enhances the institution’s ability to demonstrate transparency and accountability in its fraud prevention efforts. Financial organizations can track and trace every decision made by Corticon, providing a comprehensive record of the fraud detection process. This auditability is crucial for regulatory compliance and can also aid in internal investigations and audits.
For traditional fraud detection systems, the lack of real-time decisioning capabilities means that transactions are analyzed after they have already been approved, leading to costly false positives, dissatisfied customers and substantial losses. Moreover, these outdated systems struggle to leverage advanced analytics techniques such as machine learning, predictive modeling and social network analysis. Their inability to handle the vast volume, variety and velocity of data hinders their effectiveness in combating fraud.
To address these challenges and stay ahead of fraudulent activities, financial institutions must adopt a modern decisioning platform designed specifically for fraud detection and prevention. Progress Corticon offers a solution that enables real-time, analytics-driven fraud decisioning. By leveraging this platform, institutions can make instant approve/decline decisions as transactions occur, reducing false positives and enhancing customer satisfaction.
Check out Corticon here.
John Iwuozor is a freelance writer for cybersecurity and B2B SaaS brands. He has written for a host of top brands, the likes of ForbesAdvisor, Technologyadvice and Tripwire, among others. He’s an avid chess player and loves exploring new domains.
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