According to more than 5,000 respondents from PwC’s 2020 Global Economic Crime and Fraud Survey, 47% of global organizations reported they were victims of fraud. Within that group, 13% said they had lost over $50 million to fraud. These are very sobering statistics, indicating that financial fraud is a continually growing threat on a global scale.
The biggest challenge is that fraudsters are getting increasingly sophisticated. They keep varying their modes of attack, so businesses can no longer rely solely on known attack parameters and patterns identified through past cases. Existing solutions need to evolve and use their data to predict and identify fraud patterns before they happen. Fortunately, financial institutions and other digital organizations are recognizing and rising to the challenge with ever evolving fraud management solutions.
Read on to learn how to protect customers from account hacks and scams through enterprise fraud management (EFM), and explore some of the industry trends related to EFM that could potentially be applied to an organization’s fraud processes.
What is enterprise fraud management?
Enterprise fraud management (EFM) is defined as the real-time screening of transaction activity across accounts, users, products, processes, and channels. It is used to detect and prevent fraud, both internal and external to an organization.
EFM software is used to support the detection, analytics, and management of fraud. It monitors and analyzes user activity and behavior, in addition to activity between related accounts. Unusual behavior can be detected that could indicate fraud, corruption, or other organized criminal activity.
What does an enterprise fraud management system do?
An effective EFM solution addresses all aspects of fraud management, including the collection of data from all possible sources, data analysis, and investigation. It should also be able to use company data to develop patterns and improve its fraud detection capabilities.
EFM systems often use a linked and layered approach in dealing with complex and sophisticated fraud, such as cross-channel fraud where fraudsters exploit phone, web, and other channels. The layered approach applies several tiers of protection with fraud detection capabilities and multiple analytical approaches to assess user activity in real-time.
A five-layer model is common and often includes the following:
- An endpoint-centric layer. This is used to secure the point of access. It uses a two- or three-factor authentication and includes device ID, geolocation, and authentication systems.
- A navigation-centric layer. This monitors, analyzes, and compares user and account behavior with expected patterns.
- A channel-centric layer. This monitors all user or account activity in each specific channel. It compares the behavior against models and rules based on the channel.
- A cross-channel-centric layer. This examines behavior across multiple channels. It looks across products and channels for suspicious user behavior and correlates activities for each entity.
- An entity link analysis layer. This analyzes the activities and relationships between related entities.
These advanced technologies help make an EFM solution effective. But it also requires skilled staff to manage and troubleshoot the systems. Knowledgeable technicians are needed to configure the rules and alerts in the system, and to create reliable models. A holistic fraud management combines all these key attributes: balance, convenience, usability, efficiency, and security.
Four emerging trends in enterprise fraud management
In recent times, EFM solutions have evolved from basic, rules-based detection systems. They are now able to employ predictive risk assessment using big data, advanced analytics, as well as machine learning to better detect and manage the growing fraud problem. The new solutions are shaped by the four emerging trends, giving financial institutions and businesses more protection than ever:
- Use of advanced analytics
- Real-time monitoring
- A behavioral analytics-based approach
- Next generation authentication
Use of advanced analytics
Prior to recent technologies, it was impractical and time-consuming to analyze all of an organization’s relevant data to detect fraud. But today, high performance analytics tools enable companies to rapidly analyze massive amounts of information to uncover suspicious patterns that might lead to fraud.
New solutions combine advanced analytical approaches to identify subtle and non-intuitive patterns in behavior to detect fraud and even predict future risks. Examples of techniques include pattern analysis, which compares user activity with past behavior and that of their peer group to identify outliers, and model development, in which statistical analysis is used to provide quantitative insight into suspicious activity.
With hundreds of thousands of transactions taking place every minute, financial service institutions are no longer content with just using data from past transactions to fight fraud. They are also collecting and analyzing data from third party vendors and social networking sites to improve their fraud detection capabilities. With rapid data collection and processing systems now available, all this data can be collected, assimilated, and processed in real-time, making fraud detection and management faster than ever before.
A behavioral analytics-based approach
Rules-based fraud detection systems have many flaws that cause fraudulent activity to slip through the cracks and go undetected. Fraudsters are getting more sophisticated, so it’s essential that fraud management systems improve at a faster pace.
EFM systems are now making use of adaptive analytics that can use machine learning to detect unknown risks and new fraud techniques before they happen. A behavioral analytics approach helps this endeavor by collecting behavioral data from all sources and channels and comparing it against each new activity.
The end goal here is to use all the data available to identify fraudulent behavior before the fraud actually occurs and stop it before a customer’s account is compromised. This involves the use of all data to build deep historical profiles for each entity or user and to then build a massive data set of these profiles. The more profiles available, the better will be the predictions.
Next generation authentication
Cyber crime often gets committed as a result of the most trivial missteps, like a customer using a weak password. Financial institutions are now striving to improve the security of transactions through stronger authentication techniques like two-factor authentication or biometric authentication enabled through mobile technology. The tricky part is getting the right balance of improving security and the authentication process while still being able to provide a seamless customer journey.
These four trends outline the capabilities that will define the future of enterprise fraud management solutions and the decline of fraudulent attacks. By adapting to these trends and using advanced technologies, financial institutions can combat the ever-growing fraud problem and safeguard their customers’ data. Fraudsters will continue to find new ways of committing financial crimes. But, equipped with predictive technology and next generation security solutions, financial institutions can stay one step ahead of them.