22%

Increase in customer satisfaction

29%

Reduction in fraud

37%

Decrease in claim processing time ​

Technologies Used

TensorFlow Hub 300
TensorFlow
Pytorch
PyTorch
Kafka
Kafka
Talend
Talend
Client Profile

The client, a prominent insurance provider in Asia, specializes in healthcare, travel, and accident coverage and is dedicated to maintaining the industry's highest claim ratio. They aim to retain a leading position within the sector through data integration by emerging sources and achieving a comprehensive perspective on patient health, provider performance, and healthcare trends

Business Context

The client aspired to achieve seamless AI data integration across diverse sources and formats as a leading insurance provider. They aimed to enhance operational efficiency, reduce compliance risks, and optimize claims processing by eliminating manual processes and ensuring data consistency. Additionally, they wanted to pursue a solution to leverage emerging data sources in the dynamic healthcare insurance sector for more accurate insights, informed decision-making, and superior customer experiences

Challenges

Challenges

Time-consuming manual data integration led to error-prone operations, delays, and potential compliance risks​


Existing solutions hindered accurate insights, informed decision-making, and seamless customer experience​​


Emerging data sources like wearable devices and electronic health records added complexity to data integration​


Solution

Automated data extraction and consolidation from multiple sources with Kafka, improving efficiency and reducing errors​


Standardized and harmonized data using Talend, ensuring consistency and compatibility across the organization​ ​


Leveraged Generative AI use cases, TensorFlow, and PyTorch to align and integrate data elements, minimizing manual effort​​

solution

Challenges

Time-consuming manual data integration led to error-prone operations, delays, and potential compliance risks​​


Existing solutions hindered accurate insights, informed decision-making, and seamless customer experience​​


Emerging data sources like wearable devices and electronic health records added complexity to data integration​​


Solution

Automated data extraction and consolidation from multiple sources with Kafka, improving efficiency and reducing errors​


Standardized and harmonized data using Talend, ensuring consistency and compatibility across the organization​ ​


Leveraged Generative AI use cases, TensorFlow, and PyTorch to align and integrate data elements, minimizing manual effort​​

Business Impact

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Improved data quality

Ensured accurate risk assessment, fraud detection, and reliable decision-making through advanced data cleansing algorithms

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Comprehensive analytics

Cultivated comprehensive analytics, predictive modeling, and personalized care recommendations through AI data integration, as part of the generative AI solutions

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Effective care management

Empowered healthcare professionals with actionable insights to provide adequate care management and personalized experiences

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Enhanced decision-making

Facilitated informed decision-making with timely and accurate insights for risk assessment, care management, and provider performance evaluation

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Competitive advantage

Enabled superior delivery, overcoming data complexities that helped secure a leadership position in the market

Why Kanerika?

Kanerika is a premier provider of data-driven software solutions and services that facilitate digital transformation. Specializing in Data Integration, Analytics, AI/ML, and Cloud Management, Kanerika prides itself on its expertise in employing cutting-edge technologies and agile methodologies to ensure exceptional outcomes. With a proven track record across various industries, Kanerika maintains rigorous quality standards, backed by ISO 27701 certification, SOC II, and GDPR compliance. As a distinguished partner of Microsoft, AWS, and Informatica, Kanerika’s commitment to innovation and strong partnerships positions it at the forefront of empowering businesses for their growth.

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