Auto Claims, The Wild Ride
Auto Claims, The Wild Ride
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Company and Sector: Guidewire, Inc (Property and Casualty Insurance Tech)
Objective: Map the current journey of an auto claim to identify pain points and innovate using AI
Role: Design lead
Project duration: 2 months
Overview
Guidewire is a leading software provider in the property and casualty insurance space. We mapped the current state of the auto claim journey to identify pain points and plan targeted improvements to integrate AI.
A journey mapping workshop revealed the complexity of the auto claim journey, highlighting the many personas involved across companies, and many auxiliary entities. This led to a new app to alert claim supervisors about high cash reserves.
Snippets from the Journey Mapping Workshop
Snippets from the Journey Mapping Workshop
Resulting map of the current state of an auto claim for accident with injury
Resulting map of the current state of an auto claim for accident with injury
High level overview with the personas involved
Iterative steps involved in a claim for accident with injury
Iterative steps involved in a claim for accident with injury
Iterative group of steps involved
Iterative group of steps involved
Validation of medical data went through several iterations.
Nature of injuries determined the amount of iterations and the personas involved.
Minor injuries would result in smaller $$ payouts, but major injuries would involve corroboration with the police, witnesses to the accident, and medical reports from the doctors.
This would often change as time passed and if the severity of the injury increased over time and caused lasting damage to the third party.
Capturing this in a manner that was visually easy to understand was challenging.
Several places were identified where Predictive Analytics would constantly alert the Claims Supervisor if the $$ amount reserved for a claim suddenly changed based on the severity of the injury.
This led to a new 'Alerts App' to alert the Supervisor if the $$ reserve suddenly needed to be increased.
Current state map for auto claim for accident no injury
Current state map for auto claim for accident no injury
Future State Journey Map
Future State Journey Map
The next step was to create a future state vision which had planned AI integrations shown in place.
Learning
Learning
Non-linear and iterative nature
Non-linear and iterative nature
Several steps, or groups of steps would have one or many iterations and move across carriers in a non-linear way resulting in long times and changes in exposures.
Sprawling complex journey map
Sprawling complex journey map
The journey map itself looked difficult to consume since it reflected the complexity and diversity of personas involved in the process.
Success
Success
Despite the complexity, this exercise proved very valuable, revealing numerous opportunities to leverage business intelligence, predictive analytics, and AI to enhance efficiency.
Creation of a new app - Supervisor Alerts
Creation of a new app - Supervisor Alerts
A new Data and Analytics app called Supervisor Alerts was created.
This AI-driven tool flagged claims that may require substantial cash reserves.
It alerted supervisors when any changes happened to the exposure amount reserved for claims.
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