Overview

Saves knowledge worker time

50%

Reduction in time spent by knowledge workers who dedicate 20% of their time to text analysis and search tasks.¹

Delivers rapid ROI

383%

Potential return on IBM Watson Discovery IT investment over 13 months.¹

Generates revenue and profit

USD 6.1M

In possible benefits generated over three years by taking advantage of efficiency gains when using IBM Watson Discovery.¹

What's your potential ROI?

IBM Watson® NLP solutions can help improve productivity and lower costs of previous tools.

Use cases

Underwriting

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Increase efficiency with automation

Using AI technology to analyze many documents at once can accelerate manual reviews, helping reduce error and increase productivity. Employees can delegate time-consuming, manual tasks to machines and get data-informed insurance policy recommendations in near real-time. Not only can this approach reduce the duration of the underwriting lifecycle, better customer experiences.

Claims management processing

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Provide expertise-based action

AI solutions can streamline claims management processes by extracting relevant data and offering insights to insurance company employees. With intelligently-routed claims, employees get policy and eligible claim recommendations that help determine the next best action and reduce errors.

Risk and fraud management

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Spot patterns and detect fraud

Use AI to help improve fraud detection by noting potential markers of fraudulent claims. The technology can analyze various documents for patterns across historical data points that indicate fraud for employee review before claims are paid out.

Customer service

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Empower agents with faster answers

With AI technology, customer service employees can spend more time solving customer problems and less time looking for the information needed to inform their next action. Agents can access more effective answers to common questions in near-real time for faster problem resolution and improved customer satisfaction.

Client stories

Everest Reinsurance

Changing the claims process

Everest Reinsurance used deep learning to automate the internal insurance claims process. It has contributed over USD 5 million in premium growth and USD 3.5 million in efficiency gains across multiple areas of underwriting and claims.

Meiji Yasuda Life Insurance Co.

Using AI to analyze language

Since deployment of the AI tools to analyze unstructured data, the insurer has seen a 90% reduction in work involving text data reading and analysis, and has eliminated nearly 1,500 hours of work annually.

Featured offering

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