Introduction
In the fast-paced world of insurance, accuracy and efficiency are paramount. Our recent collaboration with a leading insurance provider in India uncovered a critical challenge they faced – the time-consuming and error-prone extraction of policy holder data from insurer policy files. This bottleneck not only hindered their operations but also required significant resources for data verification. To remedy this, we embarked on a journey to transform their processes, resulting in a remarkable turnaround.
The Challenge
Our client, dealing with thousands of insurance policy files daily, relied on a third-party service that took a staggering 8 minutes per policy for data extraction. The accuracy was subpar, demanding additional resources for data quality verification. The existing solution was not scalable, and a change was needed.
The Solutions
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Micro Service-Based Architecture for Scalability
- Understanding scalability as the top priority, we implemented a micro service-based architecture. Breaking down the policy extraction process into independent services, we facilitated seamless communication through RabbitMQ. This approach allowed us to scale up services according to the load of policy extraction, addressing the bottleneck.
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Efficient Data Storage
- Our expertise in database design played a crucial role. Prior to development, we meticulously analyzed data flows, requirements, and ER diagrams, ensuring that our data storage met the application’s needs. This strategic approach laid the foundation for an efficient and streamlined data storage system.
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Application of Machine Learning (ML)
- Recognizing the necessity of data extraction accuracy, we leveraged machine learning techniques, including classification, OCR, NER, and post-processing ML services. The result? An increase in data extraction accuracy from 70% to an outstanding 95%.
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Agile Methodology for Seamless Project Execution
- From day one, we embraced the Agile methodology for execution. Conducting scrum planning meetings, daily scrums, code reviews, and sprint-wise testing and deployment, we ensured a structured and iterative development process. This methodology played a vital role in completing the project seamlessly within the specified timeframe.
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Technology Stack
- To deliver this solution, we employed a robust technology stack including Python DJango, DRF, PostgreSQL, RabbitMQ, Google OCR, Docker, K8S, and AWS.
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The Impact
- Our innovative approach addressed the scalability issue through a micro service architecture, improving policy extraction time from 8 minutes to a mere 30 seconds – a remarkable 16x reduction. Moreover, we increased data extraction accuracy from 70% to 95% through the implementation of advanced machine learning models.
Conclusion
Our collaboration not only overcame the challenges faced by our client but also set a new standard for efficiency and accuracy in insurance data extraction. This success story exemplifies the power of strategic planning, adopting cutting-edge technology, and a commitment to excellence in solving complex business challenges.
In the last five years, we at CoReCo Technologies, have worked with 60+ various size businesses from across the globe, from various industries and have been part of 110+ such success stories. We applied the latest technologies for adding value to our customers’ businesses through our commitment to excellence.
For more details about such case studies, visit us at www.corecotechnologies.com and if you would like to convert this virtual conversation into a real collaboration, please write to [email protected].
Vikas Jamdar
Principal Software Engineer
CoReCo Technologies Private Limited
