This is our story of enabling a leading pet insurer to accelerate claims processing and enhance customer satisfaction using an advanced data annotation solution driven by SKENSE.
Efficient claims handling is a ubiquitous challenge in the insurance industry. Policyholders often fail to provide accurate or complete information when filing claims, leading to delays and complications in claims processing. Moreover, the surge in fraudulent claims has added another layer of difficulty. Automated data extraction assumes paramount importance, considering the pivotal role of claims processing in shaping customer experience and enterprise brand image.
Two-fold: Effective revenue management for claims settlement and the efficient deployment of human resources to manage escalating claims. To address this, the client sought an automated data extraction solution to mine critical information from documents and expedite claims processing. However, achieving this goal necessitated the availability of accurately labeled data encompassing vital details from a diverse array of invoice templates.
Triange Consult, the consulting arm of WNS Triange – our AI, Analytics, Data and Research practice – devised an efficient data annotation solution essential for training Machine Learning (ML) models.
To develop the model for each document class, we designed a classifier to segregate pages into invoice or medical history categories for further processing and data extraction. Harnessing the power of SKENSE – our proprietary platform built on AI and ML – we meticulously extracted, contextualized and annotated claims-related data. This facilitated the extraction of pivotal details like the policyholder, veterinarian, insured pet, clinical history, date, description, amount and more.
To further augment the data annotation solution, WNS Triange engineered an automated pipeline. This led to data classification, task allocation and comprehensive log generation, ensuring seamless oversight and task management.
To unlock multiple benefits, including:
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29 January 2024
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