Customer support · Production

Middle Eastern Commercial Bank resolves customer messages 50% faster with SS&C Blue Prism AI agents

The problem

A Middle Eastern commercial bank faced growing volumes of secure customer messages in English, Arabic, or mixed language that overwhelmed call center staff, caused slow response times, and left urgent issues such as stolen card reports unresolved until the next business day.

Workflow diagram · grounded in source
1
Customer sends secure message
trigger
“they can send a secure message via the bank's mobile app. These messages are often detailed and lengthy and can arrive in either English, Arabic or a mixture of the two”
2
AI agents retrieve from mailbox
integration
“AI agents now retrieve emails from a central mailbox and send them to the LLM to interpret and summarize them in both English and Arabic”
3
LLM interprets and summarizes
ai_action
“send them to the LLM to interpret and summarize them in both English and Arabic”
4
AI agent determines course of action
ai_action
“The AI agent then uses the summary to determine a course of action and acts on the customer's request”
5
Automated request resolution
output
“if a customer reports a lost credit card via a secure message, the AI agent will immediately block future charges to prevent fraud”
Reported outcome

AI agents now respond to customer requests 50% faster with 100% accuracy, significantly improving customer satisfaction and retention while allowing call center staff to focus on complex issues.

Reported metrics
Customer request response speed50% faster
Response accuracy100% accuracy
Customer satisfaction and retentionsignificantly improved customer satisfaction and retention
Reported stack
SS&C Blue Prism AI agentslarge language model (LLM)generative AI
Source
https://www.blueprism.com/resources/case-studies/commercial-bank-customer-service-inquiries-ai-automation/
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

AI agents now respond to customer requests 50% faster with 100% accuracy, significantly improving customer satisfaction and retention while allowing call center staff to focus on complex issues.

What tools did this team use?

SS&C Blue Prism AI agents, large language model (LLM), generative AI.

What results were reported?

Customer request response speed: 50% faster; Response accuracy: 100% accuracy; Customer satisfaction and retention: significantly improved customer satisfaction and retention (source-reported, not independently verified).

How is this customer support AI workflow structured?

Customer sends secure message → AI agents retrieve from mailbox → LLM interprets and summarizes → AI agent determines course of action → Automated request resolution.

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