A multi-line insurance company had knowledge management and training issues with their claims’ operations. The claims adjuster needed a reliable place for reference at every step of the claims process. They envisioned a solution that enabled easy and efficient access to their knowledge base.

The company followed a traditional model of claims adjustments. It involved preparing the claims’ team through a training process that lasted months. The processes were dependent on the experience and skills of the claims’ adjusters and their supervisors. And, when losing a skilled adjuster or on the event of catastrophe claims, it was a massive challenge for the claims team to avail credible capacity to process the claims.

The organization hired Nuvento to define a self-learning system to answer the adjusters’ queries regarding the claims processing steps. The system was designed to be trained using historical data to provide a proactive lookup to the claims adjuster. Nuvento implemented a self-learning system that trains its model with the historical data on an ongoing basis. The adjusters interacted with the system

through a chatbot interface. The system was integrated with a claims system database to capture all the data accumulated so far for a claim and would provide a prediction or lookup of the future steps required. The adjusters could post a question through a chat interface referencing the application, and the system will answer that according to the trained model. If the system can’t answer with a reasonable confidence level, it will bring in one of the available experts. This reduced their training need and knowledge management problems to a great extent.

Project Highlights

Efficient claims processing

Significant reduction in dependency on individuals

Significant hours of waiting time saved

Made scalability simple

Reduced continued training efforts by 40%

Reduced wait times on the claims process steps significantly

Technology Stack

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