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AI / ML Applications and Use Cases

Gestalt’s Insurance solutions, described below, have been developed in close association with various clients. These solutions have relied heavily on using NLP, Gen AI, advanced Anomaly detection and Transparent Machine learning capabilities of EazyML.

Contact Center Insight Management

CCIM derives insights from demographics, relationship and transcripts and uses them to increase contact center agents’ ability to successfully serve the customers by

  • Advising the agents with scripts on how to serve the customers based on their personas and reasons for calls.
  • Alerting them on customers propensity to churn or to upsell.
  • Suggesting customers preferences for servicing and also their sentiments based on recent digital interactions.


Inflation Forecast for Asset & Investment Management

For insurance companies, inflation plays a key role in determining the mix of investment vehicles, to help invest in various liquid assets. We have built models to forecast inflation – trend upwards, stay flat, or dip downwards. The existing data feeds sometimes are limited or misleading. EazyML’s LSTM  with the lag information about how long before parameters impact inflation, forecasts the trending – a useful way to see if the forecast for data feeds line up with the internal forecast. Best of all, EazyML’s Transparency – Explainable AI, in particular – makes the model transparent, vitally important for the investment professionals and analysts to understand the projected inflation behavior and gain confidence in it.


Automation in Approval & Onboarding

Are there multiple forms for insurance, the brokers? Do they require NLP to extract data elements – like, name and address that are expressed in different ways? Does this cause errors with data entry and mismatch, increasing time to process applications? EazyML’s NLP helps digitizes the insurance process: takes in data from several sources (fax via OCR, digital documents from email), extracts relevant content, normalizes the textual information, enters it into the downstream system to begin processing, helps underwriters with decision AI for approval – reducing the time to process from 3-8 days to a day.