Windward Financial Services global Company newsroom

LSEG (London Stock Exchange Group)

Risk Management

LSEG needed to help shipping market participants identify and mitigate sanctions compliance risk in global maritime trade as deceptive practices such as dark activity and ship-to-ship transfers made manual assessment more complex.

42% of all high-risk tankers being Russian-affiliated
track approximately 117,200 vessels currently at sea globally

Reported impact

Business outcome

LSEG can offer trading and chartering users a real-time workflow for assessing and mitigating maritime sanctions compliance risk using AI-driven vessel risk profiling combined with its shipping analytics.

  • Compliance
  • Risk Reduction

Solution

LSEG deployed Windward’s AI-based solution into its LSEG Workspace environment to strengthen maritime sanctions screening for shipping-market users. The implementation augments LSEG shipping analytics with Windward vessel risk profiling in real time, so trading and chartering teams can evaluate suspicious behaviour inside their normal workflow instead of using a separate tool. According to the release, the AI monitors about 117,200 vessels at sea globally, including oil tankers, dry bulk carriers, and container vessels. It applies advanced behavioural risk assessment models to vessel activity and screens for indicators such as going dark by altering or disabling AIS tracking, visiting ports in sanctioned countries, and loitering in sanctioned areas. The resulting risk profiles are surfaced through LSEG Workspace; the source does not disclose a distinct storage layer or back-end data persistence design.

Data flow

Windward’s AI-based solution monitors approximately 117,200 vessels at sea, evaluates behavioural indicators such as AIS signal manipulation, sanctioned-port visits, and loitering in sanctioned areas, and produces vessel risk profiles. LSEG blends those profiles with its shipping analytics capabilities and presents the combined output in real time through the LSEG Workspace platform for trading and chartering users.

Solution architecture

3 components · 3 layers
  1. Compute
    • Windward’s AI-based solution Tracks vessels globally and uses AI-based behavioural risk assessment models to identify suspicious maritime activity and assign risk profiles.
  2. Serving
    • LSEG Workspace platform Delivers the combined analytics and risk insights to trading and chartering users in real time as part of their daily workflow.
  3. Uncategorized
    • LSEG shipping analytics capabilities Provides the existing shipping analytics that are augmented with Windward vessel risk profiling.

Architecture clues

  • Covers oil tankers, dry bulk carriers, and container vessels
  • Real-time deployment on the LSEG Workspace platform
  • Screens for dark activity, sanctioned-port visits, and loitering in sanctioned areas
  • Used by trading and chartering users as part of daily workflow

Evidence from the source

"42% of all high-risk tankers being Russian-affiliated."
"It will track approximately 117,200 vessels currently at sea globally including oil tankers, dry bulk carriers and container vessels."
"This could include ‘going dark’ – turning off or altering an AIS tracking device, visiting the port of a sanctioned country, and/or loitering in a sanctioned area."
"Windward’s maritime AI-powered technology will augment existing shipping analytic capabilities available on the LSEG Workspace platform in real-time."
"using AI and advanced behavioural risk assessments models, the technology screens against criteria which could be an indicator of illicit activity to determine a risk profile."
"we can now equip our trading and chartering users with the best solution to assess and mitigate the sanctions compliance risks in the maritime space as part of their daily workflow."
Transparency Evidence & methodology 86/100 reference value · 94% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value86/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity16/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact15/25
    1 metric describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence94%
  • Source authority25/25
    The deployment is described by the adopting organization or a government source.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    6 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. LSEG (London Stock Exchange Group)

    LSEG (London Stock Exchange Group) customer story

    First-party
    https://www.lseg.com/en/media-centre/press-releases/2023/windward-artificial-intelligence-deployment
    Source type
    Customer-published story
    Published
    Jul 5, 2023
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.