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Intact Financial Corporation is the leading provider of property and casualty insurance in Canada, a leading provider of global specialty insurance, and a leader in commercial lines in the UK and Ireland. Intact faced a challenge in managing its vast network of customer support call centers and required a workable solution within 6 months and long-term solution within 1 year. With up to 20,000 calls per day, the manual auditing process was inefficient and struggled to keep up with increasing call traffic and rising customer service expectations. Quality control agents had to manually pick calls to audit, which was not a scalable solution. To address this, Intact turned to AI and speech-to-text technology to unlock insights from calls and improve customer service. The company developed an automated solution called Call Quality (CQ) using AI services from Amazon Web Services (AWS). The implementation of CQ allowed Intact to handle 1,500% more calls (15 times more calls per auditor), reduce agent handling time by 10%, and generate valuable insights about agent behavior, leading to improved customer service.
Amazon Transcribe is a fully managed automatic speech recognition (ASR) service that helps developers add speech-to-text capabilities to applications. It uses deep learning to convert audio to text quickly and accurately. In this post, we demonstrate how the CQ solution used Amazon Transcribe and other AWS services to improve critical KPIs with AI-powered contact center call auditing and analytics.
This allowed Intact to transcribe customer calls accurately, train custom language models, simplify the call auditing process, and extract valuable customer insights more efficiently.
Solution overview
Intact aimed to develop a cost-effective and efficient call analytics platform for their contact centers by using speech-to-text and machine learning technologies. The goal was to refine customer service scripts, provide coaching opportunities for agents, and improve call handling processes. By doing so, Intact hoped to improve agent efficiency, identify business opportunities, and analyze customer satisfaction, potential product issues, and training gaps. The following figure shows the architecture of the solution, which is described in the following sections.
Intact selected Amazon Transcribe as their speech-to-text AI solution for its accuracy in handling both English and Canadian French. This was a key factor in Intact’s decision, because the company sought a versatile platform capable of adapting to their diverse business needs. Amazon Transcribe offers deep learning capabilities, which can handle a wide range of speech and acoustic characteristics, in addition to its scalability to process anywhere from a few hundred to over tens of thousands of calls daily, also played a pivotal role. Additionally, Intact was impressed that Amazon Transcribe could adapt to various post-call analytics use cases across their organization.
Call processing and model serving
Intact has on-premises contact centers and cloud contact centers, so they built a call acquisition process to ingest calls from both sources. The architecture incorporates a fully automated workflow, powered by Amazon EventBridge, which triggers an AWS Step Functions workflow when an audio file is uploaded to a designated Amazon Simple Storage Service (Amazon S3) bucket. This serverless processing pipeline is built around Amazon Transcribe, which processes the call recordings and converts them from speech to text. Notifications of processed transcriptions are sent to an Amazon Simple Queue Service (Amazon SQS) queue, which aids in decoupling the architecture and resuming the Step Functions state machine workflow. AWS Lambda is used in this architecture as a transcription processor to store the processed transcriptions into an Amazon OpenSearch Service table.
The call processing workflow uses custom machine learning (ML) models built by Intact that run on Amazon Fargate and Amazon Elastic Compute Cloud (Amazon EC2). The transcriptions in OpenSearch are then further enriched with these custom ML models to perform components identification and provide valuable insights such as named entity recognition, speaker role identification, sentiment analysis, and personally identifiable information (PII) redaction. Regular improvements on existing and new models added valuable insights to be extracted such as reason for call, script adherence, call outcome, and sentiment analysis across various business departments from claims to personal lines. Amazon DynamoDB is used in this architecture to control the limits of the queues. The call transcriptions are then compressed from WAV to an MP3 format to optimize storage costs on Amazon S3.
Machine learning operations (MLOps)
Intact also built an automated MLOps pipeline that use Step Functions, Lambda, and Amazon S3. This pipeline provides self-serving capabilities for data scientists to track ML experiments and push new models to an S3 bucket. It offers flexibility for data scientists to conduct shadow deployments and capacity planning, enabling them to seamlessly switch between models for both production and experimentation purposes. Additionally, the application offers backend dashboards tailored to MLOps functionalities, ensuring smooth monitoring and optimization of machine learning models.
Frontend and API
The CQ application offers a robust search interface specially crafted for call quality agents, equipping them with powerful auditing capabilities for call analysis. The application’s backend is powered by Amazon OpenSearch Service for the search functionality. The application also uses Amazon Cognito to provide single sign-on for secure access. Lastly, Lambda functions are used for orchestration to fetch dynamic content from OpenSearch.
The application offers trend dashboards customized to deliver actionable business insights, aiding in identifying key areas where agents allocate their time. Using data from sources like Amazon S3 and Snowflake, Intact builds comprehensive business intelligence dashboards showcasing key performance metrics such as periods of silence and call handle time. This capability enables call quality agents to delve deeper into call components, facilitating targeted agent coaching opportunities.
Call Quality Trend Dashboard
The following figure is an example of the Call Quality Trend Dashboard, showing the information available to agents. This includes the ability to filter on multiple criteria including Dates and Languages, Average Handle Time per Components and Unit Managers, and Speech time vs. Silence Time.
Results
The implementation of the new system has led to a significant increase in efficiency and productivity. There has been a 1,500% increase in auditing speed and a 1,500% increase in the number of calls reviewed. Additionally, by building the MLOps on AWS alongside the CQ solution, the team has reduced the delivery of new ML models for providing analytics from days to mere hours, making auditors 65% more efficient. This has also resulted in a 10% reduction in agents’ time per call and a 10% reduction of average hold time as they receive targeted coaching to improve their customer conversations. This efficiency has allowed for more effective use of auditors’ time in devising coaching strategies, improving scripts, and agent training.
Additionally, the solution has provided intangible benefits such as extremely high availability with no major downtime since 2020 and high-cost predictability. The solution’s modular design has also led to robust deployments, which significantly reduced the time for new releases to less than an hour. This has also contributed to a near-zero failure rate during deployment.
Conclusion
In conclusion, Intact Financial Corporation’s implementation of the CQ, powered by AWS AI services has revolutionized their customer service approach. This case study serves as a testament to the transformative power of AI and speech-to-text technology in enhancing customer service efficiency and effectiveness. The solution’s design and capabilities position Intact well to use generative AI for future transcription projects. As next steps, Intact plans to further use this technology by processing calls using Amazon Transcribe streaming for real-time transcription and deploying a virtual agent to provide human agents with relevant information and recommended responses.
The journey of Intact Financial Corporation is one example of how embracing AI can lead to significant improvements in service delivery and customer satisfaction. For customers looking to quickly get started on their call analytics journey, explore Amazon Transcribe Call Analytics for live call analytics and agent assist and post call analytics.
About the Authors
Étienne Brouillard is an AWS AI Principal Architect at Intact Financial Corporation, Canada’s largest provider of property and casualty insurance.
Ami Dani is a Senior Technical Program Manager at AWS focusing on AI/ML services. During her career, she has focused on delivering transformative software development projects for the federal government and large companies in industries as diverse as advertising, entertainment, and finance. Ami has experience driving business growth, implementing innovative training programs and successfully managing complex, high-impact projects.
Prabir Sekhri is a Senior Solutions Architect at AWS in the enterprise financial services sector. During his career, he has focused on digital transformation projects within large companies in industries as diverse as finance, multimedia, telecommunications as well as the energy and gas sectors. His background includes DevOps, security, and designing and architecting enterprise storage solutions. Besides technology, Prabir has always been passionate about playing music. He leads a jazz ensemble in Montreal as a pianist, composer and arranger.
“}]] In this post, we demonstrate how Intact’s Call Quality solution used Amazon Transcribe and other AWS services to improve critical KPIs with AI-powered contact center call auditing and analytics. Read More Amazon Transcribe, Artificial Intelligence, Customer Solutions