3 Cas d'utilisation pour l'automatisation de la gestion des dossiers

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Aklima@411
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3 Cas d'utilisation pour l'automatisation de la gestion des dossiers

Post by Aklima@411 »

Organizations around the world, in nearly every industry, use case management software to boost productivity and make more informed decisions. However, case management is not right for every task, especially for processes that cannot be 100% automated. In other words, the discipline of case management always involves some level of human interaction or judgment.

About 10 years ago, Forrester Research divided case management use cases into three categories: service requests, incident management, and investigative uses. Gartner also identified distinct categories of use cases for case management automation. These include cases such as human resources lifecycle management, regulated cases in banking, and customer-facing cases such as insurance claims. To better understand the potential uses of case management automation, we’ll explore three different applications in three different industries.

Case Management Use Case #1: Information Security in Banking
Fraud prevention is one of the biggest indonesia phone number example challenges facing banking institutions. In 2018, bank deposit account fraud amounted to $25.1 billion. The EU budget for 2007-2013 is $6 billion, an increase of $6 billion compared to 2016. Added to this are imposter scams and identity fraud cases, which also represent billions of dollars in losses each year.

Banks use a variety of automation solutions to identify and prevent fraudulent activity. Over the past decade, large banks have relied on anomaly detection, which is an artificial intelligence (AI) technology that identifies discrepancies. Banks are using this technology to automate fraud, cybersecurity, and anti-money laundering business processes.


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For example, anomaly detection is often used to detect fraudulent payments. Automation software notifies human monitors when it detects a deviation from normal patterns. The monitor then approves or rejects the notification, and the system improves its detection ability over time using machine learning (ML). However, it is not possible to completely remove the human element from this process.

Another example of banks using automation is to detect fraudulent login requests for online banking. While banks can automate much of the verification process, there will be suspicious activity that deviates from the normal verification process. In this case, banks can use case management automation to quickly resolve these cases without having to bolster their security and compliance teams.
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