What are the Features of Enterprise Fraud Management?

What are the Features of Enterprise Fraud Management
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Now that we have established the fact that fraud management is a necessity for each organization, diving towards a deeper end of this would be appropriate. EFM gives access to address all aspects of fraud including comprehensive data, data analytics, and investigations.

Here the discussion would be more on the features of this sophisticated platform which has elevated the working process of an organization and has made fraud management comparatively easy as it was earlier.

Based on the requirement of your organization, you can either have an end-to-end EFM system or can design a multi-layered, tailored EFM system. Both the systems have some features which are working as its strength, a few of these are discussed below:

  • Advanced AI and machine learning-based fraud algorithms

Machine learning (ML) models that use a range of functions can efficiently detect new fraud patterns and prevent various sorts of fraud. Models learning-based, for example, are well suited to processing payments, where laws demand a high level of explanation. To combat group fraud, models based on knowledge graphs disclose intricate relationships. Customers expect providers to deliver a variety of algorithm-based machine learning models as well as model-building capabilities out of the box.

  • Versatile and adaptable models

Customers are expecting providers to offer easy-to-use visual model-building tools so public data scientists and enterprise customers can develop, instruct, and administer out-of-the-box and custom-built models and model ensembles, thanks to the low-code and no-code trends. To improve data scientists’ efficiency and augment business and non-technical users’ skills, vendors should give end-users the ability to develop models based on rules, AI, and ML in an integrated, unified platform.

  • Behavioral Analytics

It helps in uninterrupted observing of behavioral patterns such as hovering of the mouse, typing speed, and activity time with the history. This provides a vigorous verification process if any unusual activity is detected.

  • Real-time detection and Integration

It also leverages artificial intelligence and machine learning technology which aids in identifying concealed abnormalities and planning action in real-time to make better decisions and provides a real-time analytics of the fraud activities and detection.

  • Investigating Procedures

Financial institutions need to assess risks across a range of platforms and organizational situations because of the rapid rise of digital financial services and lifestyle products, therefore data insights from a variety of perspectives are quite beneficial to them. However, in many countries, strict rules prohibit these organizations from sharing data with outside parties, including vendors. In a software-as-a-service deployment architecture, this makes it impossible to exploit advanced analytics from providers, such as risk scores. To address this issue, groundbreaking distributors are constructing data protection techniques such as homomorphic encryption and federated learning, which allow financial firms to share sensitive files while compliant with regulations, allowing them to gain advanced analytics from multiple networks and scenarios to detect potentially fraudulent transactions.

  • Visualization

To assist enterprises in navigating enormous datasets, these functionalities consist of a single view of network data, user activity patterns, system data, and application data. The feature collects and integrates data from a variety of sources, and the resulting information is displayed in a single visual format to make it easier to interpret and analyze. To examine large datasets and spot any unusual activity, EFM solutions employ a variety of data visualization techniques, such as link analysis, graph analysis, and so on.

  • Reporting

Reporting is an important feature of an EFM solution. It mainly refers to the creation of reports to obtain a comprehensive perspective of risk categories, fraud behaviors, abnormalities, probable malware-infected devices, ongoing trends, and so on. These reports are employed for internal audits and compliance with external regulations, and they are also used for reporting purposes to upper management.

These features have made it easier for an organization to monitor potential fraud and administer proper fraud management activities. These features have made it a necessity for any system to have these all to provide a better experience and solution. The next blog of the series will why be so important for an organization to have proper and functioning fraud management.


Read: Enterprise Fraud Management: A Brief Introduction


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