Frauds and malware cost companies a whopping $42B worldwide. Enterprise fraud management (EFM) software facilitates the detection, analytics, and management of fraud across clients, accounts, products, and processes that are part of an organization. Using any channel applicable to a user, the solution tracks and analyses user activity and actions at the application level (rather than at the device, database, or network level). Since rule-based detection is not enough, these solutions monitor within accounts. It also analyzes actions, looking for organized crime, scam rings, corruption, or misuse among related users, accounts, or other entities.
Rules-Based Detection Maybe Inadequate
Conventional fraud management is based on rules; it only has specific knowledge of past fraudulent attacks. It relies on manual intervention by experts to create, apply, and continuously evolve these rules. When such a system is deployed, companies might stay one or two steps behind fraudsters. The traditional approach to Fraud detection has been through Rule Engines, which could be:
- If-Else Conditions
- Thresholds
- Expressions
- Analyzes Data Patterns
These are widely known as deterministic solutions where an action is triggered by an event.
Real-Time and Predictive Fraud Identification Solutions
Rule-based fraud identification can be enhanced by combining sophisticated predictive fraud models and real-time analysis of enormous data. Instead of manual input of domain information or thresholds, Machine Learning Algorithms extract data from past fraudulent behaviors and build models. These models are then used to analyze real production datasets to determine whether certain activity is fraudulent or not. An advantage of using models is the capability for simultaneous multi-perspective analysis to conclude whether an event is a fraud or not. To make models more accurate and enhance fraud detection. Analytic techniques such as pattern analysis to single out anomalous behavior and link analysis to analyze hidden frauds can put companies one step ahead of fraudsters. Fraud solutions like Subex Fraud management are powered by machine learning (ML), artificial intelligence (AI), and real-time transactional data analysis can also help identify and prevent real-time fraud.
Eliminate Silos in Risk Monitoring
A crucial aspect of effective enterprise fraud management innovation is the comprehensive capture and integration of enterprise data across data silos. This is important because anomalies cannot be easily identified using data from one department alone. A surge in purchase activity can only be anomalous if there’s no relevant surge in order placing. A common data silo that can be accessed by all could be the solution to this.
Next-Generation Authentication Mechanisms
Leveraging AI and ML technologies, organizations can detect data anomalies in real-time and make timely decisions. This empowers them to take proactive action and avert significant losses. For example, AI/ML techniques can use facial recognition technology to identify high-risk blacklisted individuals. Companies need to verify customer identities while complying with high customer expectations. Technologies like voice and speech recognition and desktop analytics can help prevent fraud. Such next-generation enterprise fraud management solutions will offer diverse benefits that include the lower total cost of ownership, enhanced staff productivity, and protecting brand reputation. Since malicious attackers will always find better ways to commit fraud, companies need future-proof enterprise fraud prevention solutions to avoid loss in revenue and reputation.
Find out how customer protection is more critical than ever.