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Insurance Claims Agent

Automatically Routing 30,000 Claims That
Don't Require Human Review

A large insurance organization processing ~120,000 claims annually had identified that roughly 25% of those claims required
no human intervention. The challenge was identifying which ones — at submission — without manual review. AltaML built and
deployed an ML model that makes that call automatically, saving $200,000 and more than 8,000 staff hours a year.

office
CLIENT

A large Canadian insurance organization
deep-learning
PROJECT
Automated claims triage, predicting whether a claim requires human review or can be routed automatically
machine-learning
WHAT WE DID

Machine learning model development, cloud deployment, integration with existing claims processing system
stopwatch-funding
IMPACT

8,000+ staff hours freed per year and $200K in annual cost savings 

THE CHALLENGE

Processing insurance claims at scale is a volume problem. With 120,000 claims coming in annually, the organization's processing agents were spending significant time on claims that, by the organization's own assessment, required no human involvement at all. Roughly 25,000 to 30,000 claims per year fell into this category: cases where the claim details met clear criteria for automatic handling, but where identifying them still required staff time to review.

The problem compounded when claims were unnecessarily escalated to higher-skilled resources. A claim that should have been auto-processed instead consumed time from an adjudicator or senior case manager who could have been focused on genuinely complex cases.

The goal: to identify no-intervention claims at the moment of submission, before a reviewer ever touched them, and route them automatically.

ForgeAsk Research Assistant

THE SOLUTION

AltaML built a machine learning model that integrates directly into the organization's existing claims system and makes a routing decision at submission time.

When a new claim form is submitted, it passes through an initial set of business rules. Claims that clear those rules are evaluated by the model, which produces a probability score indicating whether human intervention is required. Claims below the intervention threshold are routed automatically to a no-human-intervention pool. Claims above it are assigned for human review.

The model was trained on 40+ variables drawn from the organization's existing data. It was deployed as a real-time web service inside the organization's Azure environment — integrated directly into the existing claims workflow rather than running as a separate system.

Post-deployment, the model has been supported through a structured maintenance program, including quarterly retraining as new data accumulates, and ongoing performance tracking to catch drift before it affected routing accuracy.

200k in annual cost
savings
8,000+ staff hours saved per year
25% of annual volume handled

WHY IT MATTERS

At 120,000 claims per year, even a modest improvement in routing accuracy has a significant operational impact. The 8,000+ hours recovered annually represents real capacity handed back to claims agents. By identifying the claims where a human adds no value, with high confidence, human attention can be concentrated where it does.

Insurance NHI Dashboard-1

THIS IS WHAT AI LOOKS LIKE WHEN THE STRAIGHTFORWARD WORK
STOPS TAKING UP EXPERT TIME.

See more of how we build for the insurance industry, or reach out to our team — we'd love to hear about what you're trying to solve.
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