Insurer Data Leakage: What Closed Claims Forget

The evidence

Across three anonymised client portfolios, 9,573 marine cargo claims carry ₹80.39 crore of estimated loss. The portfolios cover different windows, from August 2012 to February 2026, and are not a representative sample of the sector. These are estimated losses: they are not audited claims paid, not insurer net losses and not leakage savings.

The question the data raises is about knowledge more than money: what did the insurer learn from each of these claims, and where did the lesson go?

The attribution problem

When the carrier is recorded as ‘Others’, ‘Unknown’ or an unresolved label, the experience attached to that carrier disappears. 33.3% of estimated loss across the three portfolios sits under such labels: 47.2% in the first portfolio, 57.4% in the second and 29% in the third. This is impaired attribution. It is not a demonstrated payment error.

Client-level carrier pseudonyms are not a common identity registry. Matching-looking codes from separate clients are not proof of the same carrier, so no cross-account carrier conclusions are drawn.

Root cause: a label is not a mechanism

In the first portfolio, ‘improper handling during transit’ accounts for 914 of 945 claims and 85.9% of estimated loss. In the third, ‘damage during loading / unloading’ and ‘jerks / jolts’ together cover 75.5% of claims. Each phrase hides several different events: lashing failure, acceleration, forklift impact, packing failure, road condition, unloading or transhipment.

Loss prevention needs a chain: pre-dispatch condition, then transport and handling events, then damaged condition, then adjudicated proximate cause, then verified settlement and recovery. Claim counts alone do not measure transport risk without insured-shipment denominators: a carrier with 100 claims may have handled far more loads than one with 10.

The missing canonical fields

Populated is not the same as useful. Clause, Commerce and People/Moral hazard show zero coverage in all three source reports. Some fields appear fully complete even where generic labels limit their value for underwriting.

Percentages are the producing reports’ nominal completeness diagnostics, not an independently audited measure of semantic accuracy.

Sensitivity, not a savings claim

Suppose better risk intelligence prevented even a small fraction of ultimate loss cost. The table applies hypothetical percentages to the observed estimated-loss baseline of ₹80.39 crore.

This is an analytical scenario, not observed leakage and not a projected return. No controlled intervention, finalised-claim data or total insured-movement denominators were supplied.

Method and commercial thesis

The proposal is to cooperate on verification and compete on insurance, transport and finance. The workflow has four stages:

1. Pre-dispatch: validated party and cargo identity, packing and evidence.
2. Movement: route, events and exceptions, with telemetry where lawful.
3. Post-loss: cause, assessed amount, paid amount, reserve and recovery.
4. Feedback: permissioned, minimised and aggregated intelligence returned to underwriting and prevention.

The boundaries matter. The source is three aggregate report files. There are no event-level claim records, no trade-transaction denominators and no verified entity linkage across datasets. Raw party names and competitively sensitive bidding information must not be shared, and data rights and secure token mapping should be formalised before any cross-client carrier classification.

The economic thesis in one line: insurers pay to learn from each cargo failure, and without a governed feedback loop the next shipment starts without the lesson.

Information is not underwriting advice, and estimated loss is not money lost.