Data Ingestion

Extracting data from pdf's for quote comparison, excel sheets, tables in emails, integrating with existing systems etc. for collation of data is critical to improve data quality for insurance analytics.


Data Analytics

Analytics is used for customer segmentation, identification of opportunities for cross selling as well as upselling. In addition new product development and suggestions for new products is pivotal part of insurance analytics.


Data Representation

Improved representation of data helps management bring in additional visibility. Reporting for regulatory purposes as well as to monitor regulatory capital adequacy ratios for insurance firms.


Vector capability to ingest data , use capabilities in machine learning, natural language processing helps generate advanced analytics which will impact revenue, generate leads, handle additional volumes in terms of sales and manage costs to increase revenue per employee.


Considerable benefits gathered through customer segmentation, behavioural risk quantification, propensity/cross sell are leveraged upon to raise additional revenue. This has have targeted marketing based on customer segmentation and behaviour helps increase conversion rates and align products along with customer requirements.


Analytics for Expense value analysis, Liabilities valuation, workforce planning, fraud detection has helped reduce cost considerably and impact bottom line in a meaningful way.

Customer Experience

With a more targeted offering, relevance of offering to customer helps improve customer retention as well aid improve customer experience on a whole.