Document AI for Claims
OCR and NLP extract data from claims, forms and evidence in seconds, but confidence and exceptions must route to a human, not straight to a payout.
Underwriting, claims and fraud are being rebuilt around AI. We help carriers and brokers move faster while keeping decisions explainable and compliant.
AI can read a claim, price a risk and flag fraud in seconds, but an insurance decision has to be fair, explainable and defensible to a regulator and a policyholder. The struggle isn't capability; it's adopting AI without unfair outcomes, leaked claimant data, or answers no one can justify. Periscope closes that gap.
What's Happening Now
The core of insurance, assessing risk and settling claims, is exactly where AI is landing first.
OCR and NLP extract data from claims, forms and evidence in seconds, but confidence and exceptions must route to a human, not straight to a payout.
Models price risk on richer data, putting fairness, transparency and rating-rule compliance under direct scrutiny.
Unsupervised models catch fraud rings earlier, as the same techniques power increasingly sophisticated fraudulent claims.
The AI Challenge
An insurance decision affects someone's coverage and money. That's why ungoverned AI here isn't just a tech risk, it's a regulatory and reputational one.
A model that quietly discriminates on price or claims exposes you to fair-treatment findings, fines and class action, often invisible until it's audited.
Adjusters pasting claim details into consumer AI tools leak sensitive personal and medical data outside your boundary with no audit trail.
A chatbot that invents a coverage term or a claims model that can't show its reasoning becomes a bad-faith and compliance problem instantly.
We test models for fairness, keep claimant data inside your boundary, and route every decision through explainable, human-reviewed workflows, so AI speeds settlement without unfair outcomes or compliance exposure.
How We Help
We build AI into underwriting, claims and fraud so it's fast for your teams and transparent for everyone who reviews it.
OCR and NLP that turn claims and forms into structured data with confidence scores, and an exception queue so people handle the edge cases.
Models tested for disparate impact and documented for rating and fair-treatment review before they ever touch a policyholder.
AI proposes; adjusters and underwriters decide. Approval gates keep an accountable person on every coverage and payout call.
Tokenization, redaction, encryption and logging so sensitive claimant data stays protected and every AI decision is reconstructable.
Anomaly detection on transactions, claims and documents, catching leakage earlier while staying explainable to investigators.
An API-first, containerized core so AI can actually reach your policy and claims systems, without a risky rip-and-replace.
The outcome: faster underwriting and claims, fair, explainable and audit-ready.
How We've Helped
An AI-agent auditing system for an insurer, built on a secure, compliant cloud, with people kept in control.
Case study · Insurer
We combined OCR, NLP and unsupervised anomaly detection on a secure, compliant cloud to audit transactions and claims, catching fraud earlier and shortening every audit cycle, with a human reviewing the exceptions.
Read the case studyReady to Adopt AI Safely?
Book a consultation for a fixed-scope assessment of where AI helps first and how we keep it fair, explainable and compliant.
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