Use Case > AI enabled

Classifying Email Intent

CURRENT STATE

Thousands of incoming client emails

For many banks, the present solution to processing thousands of incoming client emails containing margin calls, netting and standard settlement instructions is a to hire staff to do review each one manually. The emails typically contain short hand comments which the staff need to read and then push to the relevant team to action.

CHALLENGE

Manually reviewing each email prolongs the process 

With many banks receiving 3-5000 emails every day, current manual solutions are not scalable. The risk of multiple errors is high and it also means valuable staff are unable to be deployed on higher value activities. With every division in banks still under pressure to reduce costs and improve efficiency, and regulations such as CSDR further piling on the pressure, the momentum to speed up the settlement process is relentless. 

XCEPTOR SOLUTION

Combining natural language processing (NLP) with simple rules  

Xceptor deploys rules-based functionality to send the emails and NLP to 'read' the emails to extract intent, ensuring the right technology is deployed for the right task from our broad set of native capabilities in a single system. Xceptor's native artificial intelligence functionality trains algorithms to assess the intent in each email. Xceptor classifies the emails, assesses the right priority and sends the email to be actioned by the right downstream team. The bank is able to automate a previously laborious process while putting the power of AI in the hands of operational users.


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