How Can AI Support Sanctions Screening in Delegated Authority?

Delegated authority can place underwriting, claims handling and payment activity with organisations operating in different countries and using different systems.

That creates a practical sanctions-control challenge. Information about an insured, beneficiary, owner, vessel or location may be incomplete, recorded in another alphabet or changed between the original risk and a later claim.

Screening must therefore operate at the relevant points in the relationship and transaction lifecycle. It also needs a reliable route for investigating alerts without allowing a rapid automated process to make a legal judgement from uncertain data.

Why delegated authority makes sanctions screening harder

Lloyd's financial crime guidance recognises that managing agents, coverholders and delegated claims administrators need appropriate systems and controls for the financial crime risks that affect their business.

The data required for screening may be distributed across a coverholder's policy system, a claims administrator's file, bordereaux received by the managing agent and information held by brokers or other parties. Names can be abbreviated or transliterated in several ways. Ownership and control may not be visible from a simple policyholder name.

The relevant facts can also change. A party may be added to a list, ownership may change, or a claim payment may introduce a beneficiary who was not present at underwriting. Sanctions restrictions and licensing positions vary by regime and over time, so current specialist guidance is essential.

The established screening and escalation framework

A conventional control framework starts with a financial crime risk assessment. It defines which parties and transactions are screened, at which points, against which official or approved data, and who can review and resolve an alert.

Screening tools apply exact and fuzzy matching rules to names and identifiers. Potential matches move to trained analysts, who compare dates of birth, addresses, nationalities, ownership, vessel details and other evidence. Uncertain or material cases are escalated, and a policy, endorsement, claim or payment may be paused while advice is obtained.

This provides an explainable control structure. Its operational burden comes from poor input data and large alert volumes. Analysts can spend substantial time assembling facts before they can decide whether an apparent match is genuine.

Where AI can improve screening operations

AI can help prepare data and alerts for specialist review. It may:

• Standardise names, addresses and identifiers while retaining the original values.
• Recognise likely spelling and transliteration variants.
• Link records that appear to concern the same person, organisation or vessel.
• Extract relevant ownership and relationship information from approved documents.
• Summarise why an alert was generated and identify missing evidence.

These capabilities can make an analyst's investigation faster and more consistent. They are particularly useful when relevant details sit in narrative claims information or several bordereaux fields.

AI should operate around, rather than replace, the authoritative screening control. It must not substitute its own knowledge for a current sanctions list or infer that an uncertain party is safe to transact with.

Boundaries that protect legal and operational control

List freshness and provenance must be monitored. The organisation should know when its sanctions data was updated, which regimes it covers and whether a failure has interrupted screening.

Alert prioritisation needs conservative safeguards. High-risk or uncertain cases should not be hidden because a model assigns a low score. Teams should test spelling variants, transliterations, incomplete records and ownership scenarios, with particular attention to missed matches.

The record should retain original data, normalisation steps, lists and rules used, the reason for the alert, supporting evidence, reviewer actions and any advice or licence relied upon. Overrides need appropriate approval and periodic review.

Most importantly, a qualified person must determine what the current restrictions mean for the specific parties and transaction. AI can improve the evidence available to that person, while legal and regulatory judgement remains firmly within the controlled compliance process.

Example

A hypothetical marine coverholder submits a claims bordereau before a payment. The vessel name has changed since underwriting, the assured's name appears in two transliterations and the proposed beneficiary is recorded only in accompanying correspondence.

An AI-supported process links the spelling variants and related records, then prepares an alert summary with the original fields and source documents. It does not clear the alert.

The coverholder pauses the payment and escalates the case. A managing agent sanctions specialist reviews current official information and the applicable guidance, obtains further evidence and decides the required action.

FAQs

• Can AI clear a sanctions alert automatically?

  Automated closure may be appropriate only where a firm's approved policy defines a clear, tested rule. Ambiguous identity, ownership or control questions require qualified review and documented evidence.

• When should delegated authority data be screened?

  Screening points should follow the firm's risk assessment and applicable requirements. They may include onboarding, underwriting, material changes, claims and payments, with rescreening where circumstances or lists change.

• What data quality problems weaken sanctions screening?

  Missing names, aliases, dates of birth, addresses, locations, registration numbers, vessel identifiers and ownership information can all make matching and investigation less reliable.