Typical UW journey for small commercial property UW Journey
1
Underwriter logs in to the Underwriting Portal
2
Sees a list of quotes which have been declined, accepted and referred for Underwriter’s review
The ones that have been declined just have a one liner explanation as the reason of decline - no further details provided
Same scenario holds true for the auto accepted ones - plus, there is no documentary of how the risk impacts the overall portfolio performance of the BOB
For the referred quotes, no priority order exists as well as specifics to be checked for each quote
4
Sees a list of quotes which have been declined, accepted and referred for Underwriter’s review
3 Underwriter picks up a sample of declined & accepted quotes and does manual reviews to validate the decisions-notices a few gaps in some of the quotes Underwriter reviews all the submitted documents regarding property location, building type, age, construction materials, other occupancies details, fire safety certificates, etc. 5
7
6
Determine if they meet the insurers risk appetite & underwriting guidelines.
Keys in pertinent information on the pricing tool to get premium & coverage limit/ deductible guidance
8
9
In case of quote acceptance, drafts policy papers & sends directly to insured or relevant stakeholders in the organisation to take it forward.
Logs into the portfolio management system to check how the risks impact the overall BOB performance
10
Documents the underwriting decision made for each policy & maintain a record of the policy details.
Pain Points
Documents are currently unstructured - Incomplete data Unstructured quote requests - from agents/ brokers via emails Extraction using OCRs -*** Manual ways
Multiple Data sources - needs for data harmonisation
Misrepresentation of data - Fraud detection Not able to understand fraud behaviour
Which UW to assign the quote to?
Fraud detection - Detecting the right KYC is a challenge - streamlining
Risk assessment - needs assistance Not integrated - not consolidation - easy choose & pick for tailored products
Decisions are not data driven rather geography - What is driving this decision?
Ingestion period - incompleteness of data - agent / brokers - manual to email
Non accuracy
Manual assessment of regulatory documents
Pricing models are not accurate - need to be explainable
Separate model for coverage, premium offering
Portfolio optimisation
Feedback loop to Gen Al Initial policy draft takes time
Binding is not real time
Lack of empathy during rejection
Fig 5. Process without GenAI help and STP workflows in place
© 2023 Fractal Analytics Inc. All rights reserved
07
Made with FlippingBook - PDF hosting