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Insurance

Claims prediction, underwriting automation, and fraud analytics

Automate claims triage, improve underwriting accuracy, and detect fraudulent claims — with explainable models that regulators and actuaries can audit and trust.

The Challenges

Claims Volume

Hundreds of thousands of claims require automated triage to prioritize high-value or suspicious cases for human review.

Underwriting Accuracy

Pricing policies too aggressively loses customers; pricing too conservatively increases loss ratios.

Regulatory Transparency

Insurance regulators require model explainability to verify that pricing and claims decisions are fair and unbiased.

Historical Data Quality

Legacy claims systems contain inconsistent coding, missing fields, and duplicated records that degrade model accuracy.

How CorePlexML Helps

AutoML

Claims Prediction Models

AutoML builds classification models for claims severity and fraud likelihood with automatic class balancing.

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ML Studio

Actuarial What-If Analysis

ML Studio enables actuaries to test underwriting scenarios and see predicted loss ratios before changing pricing.

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MLOps

Production Monitoring

MLOps tracks prediction drift and claims accuracy over time with automated alerts when model performance degrades.

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Dataset Builder

Data Quality Pipeline

Dataset Builder cleans and standardizes legacy claims data through AI-guided steps, handling codes and missing values.

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SDK Example

example.py
from coreplexml import CorePlexMLClient

client = CorePlexMLClient(
    base_url="https://api.coreplexml.io",
    api_key="sk_your_api_key"
)

# Train claims severity model
experiment = client.experiments.create(
    project_id="proj_claims",
    dataset_version_id="dsv_claims_2025",
    target_column="claim_severity",
    max_models=30,
    max_runtime_secs=600
)

# What-If analysis for underwriting
session = client.studio.create_session(
    project_id="proj_claims",
    deployment_id="dep_underwriting_v3"
)
scenario = client.studio.create_scenario(
    session_id=session["id"],
    features={"age": 35, "coverage_amount": 500000}
)

Expected Impact

96%
Claims Triage Accuracy
4.2x
Fraud Detection Lift
58% faster
Underwriting Cycle Time
97%
Data Quality Score

Ready to get started?

Try CorePlexML free — no credit card required. Train your first model in under 10 minutes.

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