Enterprise fraud has undergone major changes in the last years, and recent trends have made fraud management a top priority for financial institutions.
As cases of fraud have grown, those institutions have sought ways to streamline their fraud responses while expanding their capabilities. This is where the concept of enterprise fraud management becomes important.
Enterprise fraud management is when an organization’s fraud processes and platforms are united in one place, in a coherent way, to enable real customer centricity and create efficiencies across the whole business to fight fraud.
Enterprise fraud management, or EFM, is a holistic approach that encompasses fraud detection, prevention and responses across all customers, products, and channels.
In this article, we explore why it’s so important for enterprises to take a holistic view of fraud management, and what technologies give these organizations the fraud-fighting capabilities they need right now.
The importance of fraud management
Fraudsters are deploying increasingly complex, sophisticated tactics to steal money. Those tactics move across a variety of channels and often target specific customers.
This means financial institutions need fraud management solutions that can operate across channels, detect suspicious activity in real time and trigger an intervention on the right channel at the right time.
Historically, fraud solutions simply were not up to that task. It’s only through the maturation of technologies like machine learning and cloud computing that enterprises are able to deploy fraud management services that span the entire enterprise.
Siloed fraud systems that use simple scorecards and rules-based fraud detection cannot operate across all channels and products, nor can they process events or sufficient volumes of transaction data. It takes a fraud management solution powered by machine learning to support financial institutions’ fraud-fighting needs today.
A customer-centric view of fraud management
“Scale” is the key word when we talk about enterprise fraud management solutions. Such a solution needs to provide the widest possible perspective of all potential fraud activity across the entire organization, as well as the narrowest possible perspective including individual user behaviors.
With that capacity for scale, financial institutions attain customer-centric data views that allow them to profile each customer. They can see what the customer’s normal behaviors look like and understand when aberrant or anomalous activity occurs. That’s the real-time signal that someone may have become a victim of a scam or social engineering for example.
A machine-learning approach can then escalate that individual customer focus up to the entire organization. This is how you achieve fraud detection and fraud prevention across all channels and products at once.
Understanding enterprise fraud management
By deploying the kinds of technologies described above, financial institutions can develop EFM strategies that offer:
- Better fraud detection and prevention. Fraudsters move between channels and products looking for weaknesses. A siloed approach to fraud management can leave gaps for them to exploit. A holistic approach, by contrast, helps close those gaps.
- Improved customer experiences. A single customer-centric view helps people understand their interactions with the fraud management solution. This makes for consistent and coherent experiences, even when a customer is utilizing many products and channels at once.
- Reduced costs. As you break down silos, rationalize and simplify systems, and build teams that grow with the solution, you will achieve cost-saving economies of scale.
- Better cooperation between fraud and financial crime teams. As the links between these teams naturally grow stronger, an EFM built to spot cross-channel fraud can help financial crime teams do their jobs more effectively.
We built ARIC™ Risk Hub to help organizations realize these same benefits. The proprietary machine learning inventions that power ARIC Risk Hub, Adaptive Behavioral Analytics and Automated Deep Behavioral Networks, monitor real-time customer data to detect enterprise fraud and financial crime quickly so that interventions can be made on the customer’s behalf, often before any financial damage is done.
How have we helped financial institutions?
The move from siloed systems to holistic enterprise fraud management can take time. Organizations often need to restructure key processes so their teams can create new ways of working.
Deployment of the actual technology, by contrast, can happen quickly. Take our deployment at Contis for example. Contis provides end-to-end banking, payments, and processing solutions.
For years, Contis relied on an in-house fraud management system that employed simple rules to interrupt suspicious transactions. However, the company lacked a contextual understanding of transactional behavior. As a result, it relied on strict authorization rules to stop fraudsters, which generated lots of false positives and risked blocking genuine customer transactions.
The partnership with Contis and Featurespace is based on close collaboration. The Contis team needed to be able to integrate customer data, transaction data and third-party data, along with predictive analytics and the ability to set custom rules to target specific fraud typologies.
Our team deployed ARIC Risk Hub, then structured and integrated complex sets of data in just 90 days. By choosing ARIC Risk Hub for enterprise fraud management, Contis now enjoys one of the highest rates of fraud detection in the industry.
Our ARIC Risk Hub solution
In 2021, Forrester called ARIC Risk Hub “one of the most aggressive technical innovators in the EFM market.” Forrester’s 35-point rubric gave ARIC Risk Hub the highest possible score in several key areas, including:
- Model building.
- Supervised and unsupervised machine learning.
- Scalability in the number of transactions that could be monitored.
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