Financial Services Firm Enhances Contract Inquiry Efficiency and Insights with AWS
About the Customer
Known for its strong commitment to financial strength, integrity, and customer service, our client is a diversified financial services company that provides a wide range of products and services, including life insurance, retirement plans, investment services, and wealth management solutions.Â
Focusing on helping individuals and businesses achieve financial security, the client serves millions of customers across the United States through a network of financial professionals, advisors, and strategic partnerships.Â
The Customer's Challenge
The client was experiencing bottlenecks for customer contract questions, especially as the company grew, and needed to implement automation to process these inquiries more efficiently. Prior to implementing an AI solution, the company faced significant delays in responding to customer contract questions. The process involved multiple manual steps, including:
- Receiving and interpreting customer inquiries.
- Exchanging several emails for clarification.
- Manually identifying and reviewing the relevant contract sections.
- Responding to the customer, often with incomplete or unclear information.
This workflow resulted in an average response time of three days and was prone to errors and omissions, leading to customer dissatisfaction. Additionally, the volume of inquiries was increasing by 40% annually, further straining the existing processes.
phData's Solution
To overcome these challenges, the company partnered with phData to design and implement a modern data foundation on AWS that could power faster, more accurate contract inquiries.
The solution centered on creating a scalable data lake and layering in advanced search and workflow automation.
1.
Modern Data Lake Foundation
As part of a broader migration from Hadoop to AWS, contract and operational data was consolidated into a centralized data lake. Key components included:
- Amazon S3 + AWS Glue + Amazon Redshift Spectrum: Data was landed in Amazon S3, with AWS Glue crawlers automatically cataloging it for query. Amazon Redshift Spectrum then enabled engineers to analyze raw data directly from the data lake.
- Amazon Redshift: Refined and modeled data was ingested into Amazon Redshift to support deeper analytics. Analysts and data scientists could query data via the Amazon Redshift console, Amazon SageMaker notebooks, or non-AWS integrations.
- Analytics Integration: Amazon Redshift was already integrated with Tableau Server for dashboarding and RStudio for advanced statistical analysis, enabling a wide range of stakeholders to access insights.
2.
Search and Retrieval
To accelerate contract-specific inquiries, the firm implemented Amazon Kendra, allowing associates to quickly locate the most relevant sections of customer contracts with natural language search.
3.
Automated Workflows
With AWS Step Functions, the process of retrieving, analyzing, and presenting contract information was automated, streamlining responses to client inquiries.
Outcomes
The implementation of the AWS-powered solution led to:
- A 70% reduction in response time, with same-day responses becoming the norm.
- Significant improvements in accuracy and completeness of the information provided to customers.
- A reduction in the need for multiple email exchanges, streamlining the communication process.
By leveraging AWS data and analytics services, the financial services firm was able to enhance operational efficiency, improve customer satisfaction, and scale its operations to handle increasing inquiry volumes effectively.
reduction in response time
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