84% prediction accuracy rate of top 3 claim categories
84% prediction accuracy rate of top 3 claim categories
84% prediction accuracy rate of top 3 claim categories
Automaise applied Machine Learning to analyse claims submission history and predict mediator inputs during the claim reporting process
Client
Large property and casualty insurance group
Solutions
Industry
Insurance
Timeline
4 weeks
About the project:
This client is a large property and casualty insurance group where claim reporting processes, managed through mediators, were error-prone and required extensive manual input. This affected handling speed and efficacy, causing unnecessary delays for policyholders and increasing overhead costs.
What we did:
Clients' Main Achievements using AI in Customer Service
84% prediction accuracy rate for the top 3 claim categories
45% improvement in claim management efficiency
Reduced Average Handling Time (AHT)
Results delivered in 4 weeks
The challenge
The client faced several operational challenges:
Mediator-driven claim reporting flows were prone to error and required heavy human input
Delays in handling affected policyholder experience and increased operational costs
The solution
The client implemented Automaise's AI-driven automation solutions:
Automaise applied Machine Learning to analyse claims submission history and predict mediator inputs during the claim reporting process
The solution was trained on a dataset of claim submissions and contextual data
User experience improved and error rates decreased as a result
Impact:
84%
prediction accuracy for top 3 categories
45%
improvement in claim management efficiency
Reduced
AHT
Client
Large property and casualty insurance group
Solutions
Industry
Insurance
Timeline
4 weeks
About the project:
This client is a large property and casualty insurance group where claim reporting processes, managed through mediators, were error-prone and required extensive manual input. This affected handling speed and efficacy, causing unnecessary delays for policyholders and increasing overhead costs.
What we did:
Clients' Main Achievements using AI in Customer Service
84% prediction accuracy rate for the top 3 claim categories
45% improvement in claim management efficiency
Reduced Average Handling Time (AHT)
Results delivered in 4 weeks
The challenge
The client faced several operational challenges:
Mediator-driven claim reporting flows were prone to error and required heavy human input
Delays in handling affected policyholder experience and increased operational costs
The solution
The client implemented Automaise's AI-driven automation solutions:
Automaise applied Machine Learning to analyse claims submission history and predict mediator inputs during the claim reporting process
The solution was trained on a dataset of claim submissions and contextual data
User experience improved and error rates decreased as a result
Impact:
84%
prediction accuracy for top 3 categories
45%
improvement in claim management efficiency
Reduced
AHT
Client
Large property and casualty insurance group
Solutions
Industry
Insurance
Timeline
4 weeks
About the project:
This client is a large property and casualty insurance group where claim reporting processes, managed through mediators, were error-prone and required extensive manual input. This affected handling speed and efficacy, causing unnecessary delays for policyholders and increasing overhead costs.
What we did:
Clients' Main Achievements using AI in Customer Service
84% prediction accuracy rate for the top 3 claim categories
45% improvement in claim management efficiency
Reduced Average Handling Time (AHT)
Results delivered in 4 weeks
The challenge
The client faced several operational challenges:
Mediator-driven claim reporting flows were prone to error and required heavy human input
Delays in handling affected policyholder experience and increased operational costs
The solution
The client implemented Automaise's AI-driven automation solutions:
Automaise applied Machine Learning to analyse claims submission history and predict mediator inputs during the claim reporting process
The solution was trained on a dataset of claim submissions and contextual data
User experience improved and error rates decreased as a result
Impact:
84%
prediction accuracy for top 3 categories
45%
improvement in claim management efficiency
Reduced
AHT
