A model that reads every claim, and pulls aside the few that need a look
Sompo Insurance Singapore screens all incoming claims with a machine learning model, which identifies the ones that need further investigation so the rest are not held behind them.
The problem
Claims processing takes time, and the time is paid for twice: by the customer waiting and by the manpower spent getting through the queue. Checking every claim properly is expensive, checking a sample means some that needed a closer look did not get one, and either way the straightforward claims wait behind the difficult ones.
Made for the teamsthat keep every claim moving
Claims teams
Every claim screened rather than a sample, so the ones needing investigation are chosen rather than noticed.
Operations
Manpower is the cost in claims processing. Less of it goes on claims that were always going to be straightforward.
Policyholders
For the person who filed it, the claim is the relationship. Waiting is most of what they experience.
The model screens every incoming claim, identifies the ones that need investigating, and lets the rest clear without waiting behind them.
Screen all of them
Every incoming claim goes through the model, not a sample of them. Coverage is the point: nothing is skipped because the queue was long.
Pull aside the few
The model identifies which claims need further investigation, and which do not.
Spend the attention there
Assessors work the flagged set, where a closer look changes the outcome, rather than working down an undifferentiated queue.
Let the rest through
The straightforward claims are no longer held behind the difficult ones, which is where the turnaround comes from.
The results
The results, as the source reports them. It publishes no figures for them, so none are quoted here.
Shorter turnaround
Claims come back to the customer sooner.
No claim left waiting
Screening everything is what makes that a check rather than a hope.
Less manpower per claim
The source reports good progress on cost savings, without publishing a figure.
What we can buildfor you
The same pattern, built around your book. Each idea comes from a service we already run.
Claims triage
Screens every incoming claim and marks the ones needing investigation, so assessors start from a sorted queue rather than a list.
Document intake
Reads policy documents, forms and loss reports, pulls the fields out and routes them. A person handles what does not fit.
Policy questions
Answers questions about cover, exclusions and renewals from your own wordings, with a person taking over for anything contentious.
Renewal and lapse signals
Reads the book you already hold and flags which policies are likely to lapse, with the reasons attached.
Your version of thisstarts with one queue
Tell us the queue your people work down by hand, and we will tell you plainly whether AI is the right tool.