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Expert.ai > Case Studies > Smarter and Faster BNL Employees via a Semantic Search Engine and Customized Taxonomy
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Smarter and Faster BNL Employees via a Semantic Search Engine and Customized Taxonomy

Technology Category
  • Application Infrastructure & Middleware - Data Exchange & Integration
Applicable Industries
  • Finance & Insurance
Applicable Functions
  • Human Resources
Use Cases
  • Search & Rescue
Services
  • Data Science Services
The Challenge
BNL, a leading Italian bank, was facing a challenge with its internal support and enablement resources. The employees had to manually search for answers to their questions across numerous information sources. This process was time-consuming and inefficient. The bank wanted to establish a more efficient and reliable support process by automating their search and categorization systems.
About The Customer
BNL is a leading Italian bank with more than 14,000 employees. They are headquartered in Rome and are a subsidiary of the BNP Paribas Group. The bank is known for its comprehensive range of financial products and services, including retail banking, corporate banking, investment banking, asset management, and insurance. As a part of the BNP Paribas Group, BNL has a strong international presence and is committed to providing innovative solutions to meet the evolving needs of its customers.
The Solution
BNL partnered with Expert.ai to automate their search and categorization systems. Expert.ai provided BNL with custom taxonomy and semantic search capabilities which enabled them to understand and automatically process incoming employee requests and searches. As a result, employees were no longer forced to manually search for answers or consult the help desk for their requests. Instead, they could interact directly with the support system using natural language search terms and receive results carefully organized by a custom taxonomy. Now, when BNL employees search the internal portal for information on legislative documents, internal procedures, tutorials, data sheets, FAQs and more, the system returns the most relevant results.
Operational Impact
  • Improved search accuracy via enriched content metadata
  • Significant time savings improved employee satisfaction
  • Compiled more than 8,000 FAQs and classified more than 20,000 documents using a custom taxonomy
Quantitative Benefit
  • More than 8,000 FAQs compiled
  • More than 20,000 documents classified

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