For the policyholder, a claim is the moment of truth: the day they find out whether the policy they paid for over the years is worth anything. For the insurer, it usually starts as an email with seven attachments that someone has to open, read, rename and match against the policy before the case ever reaches an adjuster.
That first mile — from first notice of loss to a filed claim — is where most time is lost, and where AI already delivers measurable results. In this guide you will see which stages of the claims process AI can automate, which decisions must stay in human hands, which controls it requires, and how to start without betting the whole budget.
Where the time goes in a claim
Colombia’s Commercial Code gives insurers one month to pay once the policyholder or beneficiary proves the occurrence and amount of the loss (art. 1080). But that clock does not capture everything that happens before: the days the file sits incomplete, the unread email, the chain of messages asking for missing documents. That time does not show up in the KPI, but the policyholder lives through all of it.
At most insurers, the flow looks like this:
- Notice of loss. It arrives by email, web portal, broker or call center, in any format.
- Supporting documents. Police report, ID, medical records, invoices, photos, appraisals. They rarely arrive together.
- Policy check. Validity, coverages, deductibles, exclusions.
- Triage and assignment. By line of business, severity and complexity.
- Analysis, adjustment and settlement. The adjuster’s judgment work.
Stages 1 to 4 are largely reading and sorting work. And that is the problem: people with technical expertise spend hours renaming PDFs and hunting for policy numbers in the body of an email.
What AI can do today (and what it cannot)
AI does not replace the claims adjuster. It takes the reading off their plate so they can spend their judgment where it is needed. This is a realistic split by stage:
| Stage | What AI automates | What stays human |
|---|---|---|
| Notice of loss | Read the inbox, recognize a claim, extract policy, policyholder, date and description of the event | Handle sensitive cases that need direct contact |
| Documents | Classify each attachment (police report, invoice, medical record, photo) and check the file is complete for that claim type | Assess ambiguous or low-quality documents |
| Policy | Check validity and coverages against the core system or the issued policy | Interpret disputed clauses |
| Missing items | Automatically request what is missing from the policyholder, with a precise list | Manage exceptions and complaints |
| Triage | Prioritize by severity, line and estimated amount; route to the right team | Reassign complex cases |
| Fraud | Flag inconsistencies (dates, amounts, duplicated documents) for review | Decide whether there is fraud and deny the claim |
| Settlement | Prepare the case summary and the data for the calculation | Approve, deny and settle |
The rule we apply: AI prepares and recommends; any decision that affects the policyholder is made by a person. Denying a claim or refusing coverage is not a task to delegate to a model, for legal reasons and for reputational ones.
How it works under the hood
A typical AI claims automation flow has five pieces, and none of them requires changing the insurer’s core system:
- Connection to the intake channel. The claims inbox (Outlook, Gmail or IMAP) or the portal. The solution reads what already arrives; the policyholder changes nothing.
- Classification and document intelligence. A model identifies each document type and extracts the key fields, even from scanned PDFs or phone photos. See how it works in document intelligence.
- Completeness rules by claim type. An auto claim does not require the same documents as a life or health claim. Your technical team defines the rules, not the model.
- Policy validation. Checking validity and coverages in the policy administration system or in the policy itself.
- Prioritized filing. The case reaches the adjuster classified, validated and prioritized, with a trace of what the AI did and on what evidence.
What the adjuster gets is not a black box: it is an assembled file, with every data point linked to the document it came from.
Results: what we are measuring in a pilot
We are running an AI claims management pilot with a Colombian insurer. The figures are preliminary — the pilot is still running — but they show the order of magnitude:
| Metric | Before | With AI (preliminary) |
|---|---|---|
| Intake and triage | 2–3 days | Under 30 minutes |
| Complete files at first filing | ~40% | ~85% |
| Time to assemble the file | — | −60% |
The metric that matters most is not speed: it is completeness at first filing. Every incomplete file triggers a chain of emails, days of waiting and a policyholder who feels the insurer is putting obstacles in the way. Requesting every missing item at once, with a precise list, cuts that chain from the very first message.
Non-negotiable controls
A claim carries some of the most sensitive data an insurer handles. Before automating, these controls must be part of the design:
- Sensitive data. A medical record is sensitive data under Colombia’s Law 1581 of 2012. Processing it with AI requires authorization, data minimization and strict access controls. We explain it in detail in AI and personal data protection in Colombia (Law 1581).
- Human oversight on adverse decisions. No denial, coverage refusal or fraud suspicion goes out without an adjuster’s review.
- Traceability. Every classification and every extracted data point must be traceable to the document and the rule that produced it. If an auditor or the Financial Superintendence asks, the answer has to exist.
- Confidence thresholds. When the model is unsure — an illegible document, a policy that cannot be found — the case goes to manual review instead of guessing.
- Continuous measurement. Classification accuracy, returned cases and time per stage, measured every week, not just at project close.
Where to start
You do not need to automate every line of business at once. The lowest-risk path:
- Pick a high-volume line with predictable documents. Auto or health are usually good candidates: many cases, similar documents.
- Start with the first mile. Reading the notice, classifying documents and requesting missing items. That is where the biggest savings and the lowest risk are, because no payment decision is made.
- Measure before scaling. A 2–3 week, fixed-cost proof of concept on your real notices tells you what share of cases can be classified without intervention and how much time it frees up.
- Integrate in stages. Only with a measured pilot do you move on to integrating with the claims core and to other lines.
To see how this fits into an insurer’s operation, visit our page for the insurance industry.
Shall we talk about your claims process?
Tell us how your notices of loss arrive today and how long it takes for a file to be complete. With that, we can tell you whether your case is a good candidate for a proof of concept. Write to us or talk to us directly on WhatsApp.