The COVID-19 pandemic has disrupted normal life, businesses, and economies all over the world. If recent events like furlough schemes by governments the world over, business bailouts, and Wimbledon payouts are any indications, the insurance industry is likely to face formidable challenges in the future.
But there is opportunity in this crisis. The time is perfect for insurers to transform their businesses and associated underwriting processes by accelerating the adoption of cloud and automation technologies. This will not only help the insurers stay afloat today but also prepare them for the tougher times ahead as the global economy strides toward a possible recession.
Powered by artificial intelligence and machine learning, TCS’ Underwriting Transformation Solution enables the following:
Comprehensive information gathering: Automated extraction of data from surveys, contract notes, loss-control reports, and statement of values (SoV); identification of key data quality issues like detecting anomalies, duplicates, and entity recognition
Portfolio analysis of underwriting risks: Financial simulations to estimate losses, automated side-by-side comparison of multiple outputs and blended view of the losses
Supply-side disruptions: Non-catastrophic exposures modelling, climate risks, and analysis of supply-side disruptions, as well as scenario simulation of ensuing financial losses
Other features of the solution include a reference architecture, common data model, and simpler interfaces to support underwriters.
With TCS’ Underwriting Transformation Solution, insurers can benefit from:
- Improved underwriting efficiency and time to market through AI-ML based risk factor extractions, data cleansing, and insight-driven risk selection
- Enhanced and efficient risk analysis at account, portfolio, and re-insurance levels
- Reduced manual processing, providing more time for underwriting analysis; multiple ways to analyze losses – average annual losses, aggregate losses, occurrence losses, and excess losses
- Aggregated view of risks through common data model for inputs and outputs to quickly execute multiple models, compare the outputs and blend them