Market Securities exists to protect WTW's placement integrity — assessing carrier financial health, sovereign risk, and regulatory compliance across 2,600+ carriers globally. Today, it delivers that protection through manually compiled reports. Tomorrow, it can deliver prescriptive intelligence that actively shapes placement decisions and builds WTW's proprietary risk perspective.
Market Securities operates across three pillars — data acquisition, risk analysis, and decision support. The team's expertise is deep. But 60–70% of analyst capacity is consumed by data gathering, leaving limited room for the higher-value judgment and advisory work the function was built for.
Financial statements, regulatory filings, rating agency data, and market intelligence gathered manually from disconnected sources. Periodic refresh cycles mean data is often days old by the time it reaches analysts.
Risk assessment depends on the data layer below it. When data arrives in batches, analysis is reactive — carriers reviewed on scheduled cycles rather than monitored continuously. High-risk investigations are thorough but resource-intensive.
Fact sheets, committee papers, sovereign reports, and watchlist updates are manually assembled and delivered on schedule. End users — placement teams, brokers, BD — receive documents. They interpret. They decide. Market Securities informs but does not advise.
* Efficiency estimates based on process analysis conducted during discovery. Validated directionally with Market Securities team.
The value of this transformation is not that Market Securities produces the same reports more quickly. It is that Market Securities evolves from an information provider into a risk advisory partner — delivering prescriptive intelligence that actively shapes how WTW places business.
A placement team preparing a carrier review requests information from Market Securities. An analyst spends 3–4 hours compiling a fact sheet from multiple sources. The broker receives it days later, reads a multi-page document, extracts what they need, and makes their own judgment call.
High-risk carriers are tracked on a static watchlist. Placement teams are informed through scheduled updates. If a carrier's risk profile deteriorates between cycles, the signal may arrive after the placement decision has already been made.
16–20 hours of analyst time goes into preparing committee materials each cycle. The people best qualified to provide strategic risk judgment are instead formatting documents and validating data they already gathered manually.
A placement team inputs client requirements. Market Securities' agentic process assesses the carrier universe against those requirements and returns a recommendation — carriers ranked by fit, with risk evidence, peer comparison, and flagged concerns. The broker confirms or overrides. The intelligence is already there before they ask.
When a carrier's risk profile changes materially — not minor fluctuations, but actionable shifts — the BD team receives a proactive trigger with recommended next steps. Minor changes are filtered. Only signals that require a decision surface. The team acts on intelligence, not on documents.
With data automation and copilot-assisted analysis handling the volume work, senior analysts shift to building proprietary risk models and predictive frameworks. Over time, this reduces WTW's dependency on expensive third-party data sources — and creates a risk perspective that is uniquely WTW's.
We started with Pillar 1 because every downstream capability — risk scoring, anomaly detection, prescriptive intelligence — depends on the quality and timeliness of the data beneath it. Three agent-driven solutions are in active development, each targeting a high-volume, high-repetition process where automation delivers immediate capacity recovery.
Early outputs have been validated with Market Securities team leads. The solutions are demonstrating the extraction accuracy, source coverage, and analyst review workflows needed for production deployment.
This is not a three-phase project plan. It is a compounding sequence. Phase 1 automates the data layer and frees analyst capacity. Phase 2 redirects that capacity into intelligence — starting with copilot-assisted analysis, progressively becoming more agentic. Phase 3 is where the strategic shift happens: freed analysts build proprietary risk models, reducing dependency on third-party data and creating a risk perspective that is uniquely WTW's.
Automated ingestion from global sources with real-time sync. A single unified data platform replaces disconnected systems, freeing analyst capacity from data acquisition for intelligence work.
AI-assisted risk scoring and peer comparison, progressively becoming more agentic. 2,600+ carriers under continuous surveillance with material risk changes surfaced proactively to analysts.
Senior analysts build proprietary risk models and predictive frameworks. Prescriptive carrier recommendations, proactive BD triggers, and reduced dependency on third-party data sources.
* All projected values are estimates based on process analysis. Actuals will be validated as each phase enters production.
Based on detailed process analysis of 24 Market Securities activities across 45 FTE, the following projections outline the efficiency gains each phase is designed to deliver. Production metrics will be tracked from day one through a structured value realization framework.
[Review estimates with John — adjust any figures based on operational experience before final presentation.]
Phase 1 automation will make data gathering faster. But without Phase 2 and Phase 3, analysts are still performing the same manual analysis on better data. The function remains a report factory — faster, but structurally unchanged.
Data automation recovers analyst hours from extraction. But those hours get absorbed by the same manual analysis and report preparation work. Without the intelligence layer, freed capacity fills with more of the same — not with higher-value work.
WTW's carrier universe is growing. Market Securities currently monitors 2,600+ carriers through manual and semi-manual processes. Without continuous AI-powered surveillance, coverage growth requires proportional headcount growth — an operating model that does not scale.
A team that produces static reports on request is perceived as operational overhead. A team that delivers prescriptive risk intelligence and builds proprietary risk models is perceived as a strategic asset. The difference is not capability — the expertise already exists. The difference is capacity. Meanwhile, WTW continues paying premium prices for risk data from external providers. The opportunity to build proprietary risk models — informed by WTW's unique placement data and carrier relationships — remains unrealized.
The solution is a network of specialized AI agents that gather, analyze, and anticipate — operating continuously against the same data analysts use, but at machine scale. The platform reads from and writes to WTW's authoritative systems. It holds no canonical state itself. Every output is traceable, auditable, and designed for human review.
The infrastructure being built in Phase 1 is not single-use. It is designed to scale within Market Securities itself — covering more of the carrier universe, automating more of the 24 mapped processes, and layering progressively deeper intelligence as each capability matures.
Phase 1 targets the highest-volume extraction processes. The same agent infrastructure scales to cover the full carrier universe — including regional carriers, specialty markets, and newly onboarded entities — without proportional resource growth.
Of the 24 L3 processes mapped in Market Securities, the initial three use cases address the data foundation layer. The remaining processes — risk scoring, deep-dive investigations, committee governance — follow the same agent architecture as automation confidence builds.
Each phase adds a layer of intelligence on top of the last. Automated data feeds enable continuous risk scoring. Continuous scoring enables anomaly detection. Anomaly detection enables predictive models built on WTW's own placement data and carrier relationships.
The architecture is modular by design. Every new capability inherits the data pipelines, governance patterns, and validation frameworks established by the ones before it — reducing marginal build cost and timeline with each addition.
The three data foundation use cases are in active development with early outputs validated. Production deployment will establish the measurable baseline — actual hours recovered, accuracy improvements, and cycle time reduction. This baseline becomes the foundation for the Phase 2 investment case.
With validated data automation in place, Phase 2 layers intelligent risk analysis — starting with copilot-assisted tools and progressively increasing agentic coverage. This is where the function begins to shift from reactive to predictive. Detailed scoping and investment requirements to follow upon Phase 1 production validation.
Define the path from three initial use cases to full coverage of all 24 mapped processes. Set the value realization framework for tracking production metrics, govern Phase 2 scoping decisions, and ensure the infrastructure built in Phase 1 — data pipelines, agent architecture, validation frameworks — is leveraged for every subsequent capability within the function.