TREN

Hotel security · Structured operations with high record volumes

AI System for Matching Lost Property Reports with Hotel Records

Finding similar items in several rooms does not mean the first candidate is the rightful owner. The system ranks possible matches against guest stay records; the security officer verifies identity and decides whether to release the item.

Representative hotel security panel with a work queue, checks and an audit trail: lost property match queue — **Lost property match queue** Representative interface — contains no real data. A candidate match is not proof of ownership; no item is released without security approval.

The problem

When guest reports, stay records and found-property records are held in different sources, staff must manually align the fields before preparing a list of possible matches.

The security officer must compare every suggestion in the possible-match list with the source record; incomplete or conflicting evidence must not replace the final decision.

If similar items found in several rooms are not referred for separate review, property could be released to the wrong person; the record must not progress without a human decision.

What we set out to improve

Time taken for the draft possible-match list and its supporting evidence to reach the security officer for review
How records referred for human review because similar items were found in several rooms are resolved
Number of suggestions corrected after identifying the risk of releasing property to the wrong person

How the system works

01 Retrieve the guest report, stay record and found-property record from authorised sources
02 Verify field and document versions
03 Prepare the possible-match list with supporting evidence
04 Check for exceptions: similar items found in several rooms
05 Have the security officer accept, correct or reject the suggestion
06 Decision gate: matching and release of the property
07 Record the decision and source separately

Methods we used

A source-identified data schema for guest reports, stay records and found-property records
A structure that separates deterministic rules from AI suggestions
A screen showing the possible-match list alongside its supporting evidence
A human review queue for similar items found in several rooms
A record identifier that prevents duplicate processing

Where people stay involved

The security officer compares the possible-match list with the source record. The workflow stops if an exception arises: similar items have been found in several rooms. The security officer decides whether the property matches the report and whether it should be released.

Data and security

Guest reports, stay records and found-property records should be processed using only the fields required. Role-based access should be applied, and unnecessary identity, health, financial and internal organisational data should not be sent to the model. Source retrieval, the AI suggestion and the security officer's decision should be recorded separately.

Who this suits

A good fit

Teams that regularly prepare possible-match lists
Organisations that manually combine guest reports, stay records and found-property information
Operations that keep the final decision with a person

Not a good fit

Organisations whose source data is not current
Operations where responsibility for the security officer has not been defined
Operations where AI suggestions would be acted on without review

Frequently asked questions

What data is used?

Guest reports, stay records and found-property records are retrieved only from authorised sources. A field whose source and version cannot be established is not treated as confirmed information.

When does the process stop?

If similar items are found in several rooms, the record is referred to the security officer for review. If supporting evidence is missing, no change is made to the target record.

Does AI make the final decision?

No. The security officer has authority over matching and releasing the property. Acceptance, correction and rejection are recorded as human decisions.

Project led by:DijitalPi

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