Insurance
The global insurance industry is laden with growing risk, changing customer preferences, evolving regulatory requirements, and an influx of new entrants. A low-interest-rate environment and economic implications from the pandemic outbreak are challenging the revenue streams and profitability. The rise in natural catastrophes is further pushing insurers to revisit the existing risk assessment models.
This necessitates accurate pricing, effective underwriting, omnichannel distribution, and faster claims processing. Making strategic shifts such as annual renewal to ongoing product offerings, changing the role of the agent as product educators, manual to algorithmic underwriting, and claims organizations focusing on risk prevention and mitigation, have become the need of the hour.Â
Amidst these challenges, the growing adoption of analytics-driven decision making by business functions is a clear opportunity. In our view, the smart use of AI and analytics can fuel the adoption and integration of external data ecosystems, automation, and business insights, enabling insurers to improve their growth and profitability.
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How Analytics Can Help?
– Improve risk selection, pricing accuracy and loss reserving
– Improve revenue forecasting and compliance
– Augment and automate underwriting decisions
– Increase underwriting efficiency
Case Study
Underwriting Decision Engine that Avoids Evidence Costs for a Life Insurance Company
How Analytics Can Help?
– Improve risk selection, pricing accuracy and loss reserving
– Improve revenue forecasting and compliance
– Augment and automate underwriting decisions
– Increase underwriting efficiency
Case Study
Underwriting Decision Engine that Avoids Evidence Costs for a Life Insurance Company
How Analytics Can Help?
– Drive sales through effective customer segmentation
– Increase returns on marketing investment
– Improve agent and broker effectiveness
– Prevent sales fraud, malpractices
Case Study
Unsupervised Learning Algorithms Help Identify Policies Worth USD 9MM in Premium with Likely Sales Frauds
How Analytics Can Help?
– Reduce customer dissatisfaction
– Increase customer lifetime value
– Improve customer retention
Case Study
AI and Predictive Analytics help reduce customer complaints by ~20% for a Health Insurance Provider
Case Study
AI and Predictive Analytics identifies over USD 1Bn in Assets Under Management (AUM)
How Analytics Can Help?
– Improve efficiency & accuracy of payouts
– Enhance customer experience
– Improve adjuster performance
– Reduce quality gaps, improve compliance
How Analytics Can Help?
– Enhance employee effectiveness through workforce optimization
– Reduce cost of operations
– Increase process automation
