Catastrophic Claim Cost Control AI Agent for Claims Economics in Insurance

What is Catastrophic Claim Cost Control AI Agent in Claims Economics Insurance?

A Catastrophic Claim Cost Control AI Agent is a specialized decision-intelligence system that helps insurers predict, triage, and optimize the cost and speed of catastrophic (CAT) claims across property, auto, and specialty lines. It combines geospatial analytics, large language models (LLMs), computer vision, and operations research to reduce indemnity leakage and loss adjustment expense (LAE) while accelerating fair settlements. In Claims Economics, this AI Agent orchestrates the end-to-end CAT lifecycle—before, during, and after an event—to deliver better outcomes for both insurers and policyholders.

1. Core definition and scope

The Catastrophic Claim Cost Control AI Agent is an autonomous-yet-supervised software agent designed to anticipate surge demand, prioritize high-severity losses, and guide adjusters and vendors toward the most efficient actions. It operates across hurricanes, wildfires, floods, earthquakes, hailstorms, winter storms, and man-made catastrophes, adjusting to line-of-business nuances such as homeowners, commercial property, auto physical damage, business interruption, and specialty risks. Its scope includes forecasting claim volume and severity, optimizing staffing and vendor dispatch, calibrating reserves, and enforcing leakage controls during the most volatile, correlated loss scenarios insurers face.

2. Key capabilities at a glance

The AI Agent delivers a cohesive stack of capabilities that align with Claims Economics objectives: real-time event detection, exposure mapping, FNOL (first notice of loss) automation, severity scoring, coverage and subrogation reasoning, reserve setting, reinsurance optimization, vendor allocation, fraud detection, and payments orchestration. It integrates structured and unstructured data, reasons with LLMs over policy language and adjuster notes, and uses computer vision to interpret aerial imagery, street-level photos, and drone footage. It also employs optimization algorithms to minimize cycle time and unit cost while maintaining compliance with jurisdictional requirements and fair claims practices.

3. Data foundation for catastrophic claims

The AI Agent is data-hungry by design, ingesting and aligning heterogeneous data in near real time. It fuses geocoded policy and exposure records, hazard forecasts, historical and live weather feeds, satellite and aerial imagery, property attributes, building footprints, topography, soil saturation, flood depth grids, remote-sensing fire perimeters, and third-party loss indices. It also leverages adjuster notes, call transcripts, photos from insureds, telematics and IoT signals (where permissible), vendor capacity feeds, and regulatory bulletins. Clean data pipelines, entity resolution, and feature stores ensure that each decision leverages the freshest, most trustworthy signal available under disaster conditions.

4. Architecture overview and components

The Catastrophic Claim Cost Control AI Agent implements a modular architecture that supports scale, resiliency, and governance. It separates concerns between data ingestion, modeling, decisioning, orchestration, and controls to ensure reliable outcomes in high-pressure surge scenarios.

Data ingestion and normalization

The agent continuously ingests policy, claims, vendor, and external hazard data through streaming and batch connectors. It normalizes addresses, geocodes locations, deduplicates entities, and applies quality checks to prevent drift and contamination during a catastrophe.

Modeling and analytics layer

Predictive and prescriptive models estimate claim frequency, severity, fraud propensity, subrogation likelihood, and cycle-time drivers. Multimodal models interpret imagery and documents, while probabilistic models generate scenario ranges useful for reserve setting and capital planning.

Decisioning and optimization

A rules-plus-ML engine enforces coverage guardrails, triage thresholds, and fair handling protocols, while optimization solvers allocate vendors and adjusters to minimize total cost and backlog. Human-in-the-loop steps ensure oversight in ambiguous cases.

Orchestration and workflow automation

Event-driven workflows route tasks, surface recommendations inside adjuster desktops, and trigger communications and payments. The agent coordinates with RPA bots and claims platforms to reduce manual swivel-chair work and maintain audit trails.

Governance, risk, and compliance

Model monitoring, lineage tracking, explainability reports, and access controls align the agent with model risk management frameworks. Bias testing and fairness constraints are applied to ensure equitable treatment across populations and jurisdictions.

Why is Catastrophic Claim Cost Control AI Agent important in Claims Economics Insurance?

This AI Agent is important because catastrophic losses are the sharp end of Claims Economics—capital-intensive, highly correlated, and operationally complex. It reduces aggregate loss costs and LAE while preserving customer trust, which directly impacts combined ratio and solvency. By making faster, more accurate, and fairer decisions under surge, the agent strengthens the insurer’s ability to price, reserve, and allocate capital effectively.

1. The economics of catastrophic claims

CAT claims drive the tail of the loss distribution and inject volatility into insurers’ income statements and capital requirements. Correlated exposures mean many policyholders are impacted simultaneously, multiplying operational load and supply chain constraints. Small percentage improvements in severity and cycle time translate into material improvements in combined ratio due to the scale of catastrophe event losses.

2. Traditional constraints and leakage drivers

Conventional CAT response relies on manual triage, ad hoc vendor coordination, and retrospective analytics, which struggle when claim counts spike. Leakage arises from scope creep, duplicated inspections, inconsistent coverage interpretation, overpayments, underutilized subrogation, and delayed settlements that invite litigation. Without automation and optimization, adjuster bottlenecks and vendor scarcity drive higher unit costs and longer cycle times.

3. Regulatory and customer expectations

Regulators expect prompt, fair, and consistent claim handling, especially after disasters. Customers expect clear communication, rapid help, and empathy when they are most vulnerable. The AI Agent helps insurers meet prompt-pay statutes and fair claims practices by standardizing decisions, documenting rationale, and accelerating resolution without sacrificing accuracy.

4. Strategic value to pricing and capital

Better claims intelligence feeds back into risk selection, pricing, reinsurance purchase, and capital planning. More accurate reserves and event loss estimates improve ERM (enterprise risk management) and solvency positioning. Over time, the agent’s insights help refine CAT models and inform underwriting appetites, improving portfolio resilience.

How does Catastrophic Claim Cost Control AI Agent work in Claims Economics Insurance?

It works by anticipating events, triaging claims, orchestrating resources, calibrating reserves, and closing losses faster with lower leakage—all underpinned by explainable analytics and human oversight. The agent operates continuously across pre-event planning, in-event response, and post-event learning to create a virtuous cycle of improvement.

1. Pre-event preparedness and exposure mapping

Before hurricane season or peak wildfire months, the agent identifies concentrations of exposure, stress-tests likely scenarios, and pre-negotiates vendor capacity. It simulates claim volume by ZIP/postcode and peril, aligns inventory of adjusters and contractors, and ensures surge communications are ready. Exposure maps guide stockpiling of materials, staging of mobile units, and selection of temporary housing providers.

2. Event detection and surge activation

The agent monitors authoritative feeds—meteorological alerts, fire perimeters, flood depth models—and triggers surge protocols when thresholds are met. It forecasts rolling claim counts and severity by day, enabling dynamic staffing and vendor allocations. Geospatial overlays assess accessibility and safety for field teams, recommending drone or satellite assessments where ground access is restricted.

3. FNOL intake and intelligent triage

At FNOL, the agent captures structured details via digital forms, voice, and chat, and instantly scores claims for severity, coverage complexity, and vulnerability. LLMs parse narratives and policy clauses, while CV models analyze uploaded photos to estimate damage extent. Claims are routed to touchless, light-touch, or complex handling paths, minimizing handoffs and focusing expert attention where it matters most.

Triage scoring features

Severity scores blend peril intensity, building attributes, prior repairs, occupancy, photo-derived damage markers, and local contractor availability. Coverage complexity factors include endorsements, sub-limits, deductibles, and potential exclusions. Risk flags surface possible total losses, ALE (additional living expense) needs, or BI (business interruption) urgency.

4. Reserve setting and reinsurance optimization

The agent proposes initial case reserves using severity distributions conditioned on peril, structure, and locality, explaining the drivers to adjusters. It aggregates early reserves to inform event-level loss picks for reinsurance attachment, reinstatement, and IBNR (incurred but not reported) estimation. Finance teams receive probabilistic ranges, improving capital and liquidity planning during the event.

5. Field operations and vendor dispatch optimization

Optimization engines match claims to inspectors, adjusters, and contractors based on location, skills, certifications, and availability. Travel time, safety factors, and material supply constraints are modeled to minimize cycle time and unit cost. The agent recommends when to deploy drones, mobile units, and virtual inspections, and it detects surge price anomalies to enforce fair, pre-agreed rates.

6. Fraud, subrogation, and salvage detection

Anomaly detection and graph analysis flag patterns such as duplicate FNOLs, inflated scopes, or vendor collusion. For subrogation, the agent identifies recoverable parties—e.g., utilities in wildfire events, manufacturers in product-related fires, or municipalities in drainage failures—and assembles evidence packages. It also highlights salvage opportunities for contents and vehicles to offset indemnity.

7. Payments, communications, and leakage controls

The agent recommends partial advances for urgent needs (e.g., ALE), enforces coverage and documentation rules for each payment, and ensures auditable trails. It orchestrates proactive, empathetic communications—status updates, required documents, next steps—to reduce inbound calls and complaints. Leakage is reduced by preventing double-payments, validating scopes against imagery, and cross-checking vendor invoices with contracts.

8. Learning loop and model lifecycle management

Post-event, the agent reconciles predicted versus actual outcomes, tunes models, and updates playbooks. It performs data drift checks, recalibrates thresholds, and retires features that underperform. Lessons learned feed underwriting, reinsurance purchasing, and catastrophe modeling, continuously improving Claims Economics.

What benefits does Catastrophic Claim Cost Control AI Agent deliver to insurers and customers?

It delivers lower indemnity and LAE, faster cycle times, better reserve accuracy, and more consistent, fair claim outcomes. Customers experience clearer communication and quicker, more transparent settlements, while insurers gain capital efficiency and operational resilience.

1. Indemnity reduction without compromising fairness

By improving scoping accuracy, catching fraud, and surfacing subrogation, the agent reduces overpayments and unwarranted leakage. Computer vision checks reconcile line items with visible damage, while policy reasoning ensures coverage is applied correctly. This keeps indemnity aligned with actual loss while preserving trust and fairness.

2. LAE reduction through automation and right-touch handling

Automated FNOL intake, triage, document parsing, and payment controls reduce manual effort and rework. Right-touch handling—touchless for simple losses and expert focus on complex claims—prevents costly handoffs and escalations. Streamlined operations result in lower unit costs during surge periods.

3. Faster cycle times and improved customer experience

Optimized dispatch and virtual inspections compress days and weeks into hours where appropriate. Proactive updates reduce uncertainty and inbound call volume, while immediate advances support vulnerable policyholders. Faster, clearer journeys tend to lift satisfaction and retention after catastrophic events.

4. Reserve accuracy and capital optimization

Early, explainable reserve recommendations improve case estimates and event-level loss picks. Better reserve accuracy supports more stable financial reporting, capital planning, and reinsurance strategies. Reduced uncertainty helps executive teams navigate market communications and regulatory scrutiny during crisis periods.

5. Compliance, auditability, and explainability

Every AI recommendation is logged with rationale, features, and applicable policy language, creating a defensible audit trail. Explainability tools communicate to adjusters and auditors why a decision was suggested, supporting model risk management and fair claims handling practices. This lowers regulatory risk while making decisions more transparent to customers.

6. Workforce augmentation and safety

The agent augments adjusters with copilots that draft communications, interpret policies, and summarize claim files. It reduces exposure to hazardous sites by recommending drones and remote inspections when safer. Teams can scale their impact under surge without compromising employee well-being.

How does Catastrophic Claim Cost Control AI Agent integrate with existing insurance processes?

It integrates through APIs, event streams, and no-code connectors into policy admin, claims management, billing, document repositories, and vendor networks. The agent fits into existing workflows, augmenting rather than replacing core systems, and supports human-in-the-loop checkpoints at every critical decision.

1. Core platform integration points

The agent reads policy data from PAS, writes tasks and notes into the claims system, and posts payments through billing/disbursements. It exchanges documents with content management systems and pushes status updates into customer communication platforms. Bi-directional APIs keep systems synchronized without disrupting adjuster desktops.

2. External data and geospatial services

Integration with mapping APIs, hazard feeds, imagery providers, and property data enriches each claim’s context. The agent geocodes with rooftop accuracy, resolves parcel boundaries, and overlays hazards to validate causation and coverage triggers. Streamlined contracts and caching strategies ensure continuity during network stress.

3. Workflow, BPM, and RPA alignment

The agent publishes and consumes events to coordinate with BPM tools and RPA bots, ensuring repeatable processes across jurisdictions. Decision points are configurable to match carrier playbooks, with rules layered over ML to handle regulatory exceptions. This preserves institutional knowledge while modernizing execution.

4. Security, IAM, and privacy controls

SAML/OIDC-based single sign-on, role-based access controls, and encryption at rest/in transit protect sensitive data. Data minimization and purpose limitation principles are enforced, with retention policies aligned to regulatory requirements. Optional privacy-enhancing technologies and regional hosting address cross-border constraints.

5. Reinsurance, finance, and reporting

The agent aggregates event metrics for bordereaux, ceded claims, and recoverables, and surfaces attachment probabilities as the event unfolds. Finance teams receive near real-time loss development views, while dashboards track cycle times, leakage metrics, and vendor performance. Standardized exports support regulatory and rating-agency reporting.

6. Change management and human-in-the-loop

Integration includes training modules, simulation sandboxes, and staged rollouts by peril and territory. Adjusters can accept, modify, or override recommendations with captured rationale, improving models while retaining accountability. Clear escalation paths ensure complex and sensitive claims get expert oversight.

What business outcomes can insurers expect from Catastrophic Claim Cost Control AI Agent?

Insurers can expect lower combined ratios through decreased indemnity and LAE, faster settlement cycles, improved reserve accuracy, and enhanced customer retention. They also gain operational resilience and better capital deployment during catastrophic events.

1. Cost outcomes that move the combined ratio

By shrinking leakage and optimizing vendor spend, the agent reduces indemnity and LAE on a per-claim and aggregate basis. Even modest percentage improvements at catastrophe scale can notably improve the combined ratio. Savings are durable because they come from structural process improvements.

2. Speed and throughput improvements

Touchless and light-touch pathways, virtual inspections, and optimized dispatch increase throughput during surge. Shorter cycle times reduce severity creep, lower rental and ALE durations, and minimize litigation risk. Faster resolution enhances brand reputation at the moment of greatest scrutiny.

3. Capital and reserve stability

More accurate case and event reserves stabilize earnings and help meet solvency requirements. Dynamic insights into reinsurance layer usage and reinstatement timing improve capital efficiency. This supports disciplined growth and pricing under evolving climate and legal trends.

4. Revenue and retention lift

Better claim experiences drive higher Net Promoter Scores and lower churn in catastrophe-prone regions. Transparent, explainable decisions and proactive communications reinforce customer trust. Cross-sell and renewal opportunities improve when insureds feel supported during the worst moments.

5. Operational resilience at scale

Event-driven orchestration, standardized playbooks, and robust data pipelines reduce chaos under surge. The agent’s monitoring and self-healing capabilities maintain service quality amid power outages, network disruptions, and staffing constraints. This resilience becomes a competitive differentiator as climate volatility increases.

What are common use cases of Catastrophic Claim Cost Control AI Agent in Claims Economics?

Common use cases include hurricane wind and surge, wildfire, inland flood, hail, earthquake, man-made catastrophes, social inflation litigation management, and subrogation against third parties. Each scenario benefits from tailored models, triage logic, and vendor playbooks integrated by the agent.

1. Hurricane wind and storm surge property claims

The agent fuses wind swaths, surge maps, and building attributes to estimate likely roof and structural damage. It prioritizes total-loss inspections, coordinates tarping and temporary power, and accelerates ALE advances. Reserve recommendations reflect combined wind and water exposure while respecting coverage separations.

2. Wildfire structure and smoke damage

Satellite fire perimeters, burn severity indices, and parcel-level defensible space metrics inform triage. The agent distinguishes structural burns from smoke/ash contamination, guiding appropriate remediation and preventing over-scoping. Subrogation signals against utilities or contractors are flagged when patterns warrant investigation.

3. Inland flooding and basement losses

Depth grids and terrain models help estimate inundation levels per address, guiding inspection modality and contents salvage decisions. Coverage reasoning identifies limitations for groundwater versus overland flood and coordinates with NFIP or private flood coverage where applicable. Vendor routing ensures dehumidification and mitigation start quickly to limit secondary mold losses.

4. Hail-driven auto and roof claims

Hail swaths and impact intensity correlate with dent density and shingle damage probabilities. The agent recommends photo AI for auto estimates and roof-elevation imagery for property, enabling touchless settlements when confidence is high. It monitors for hail-chasing contractor anomalies to curb inflated scopes.

5. Earthquake and business interruption

Shake maps and building codes guide structural triage and red-tag prioritization. For business interruption, the agent analyzes POS, payroll, and supply-chain signals to validate downtime and calculate loss periods. It highlights contingent BI scenarios and potential subrogation tied to faulty construction or equipment.

6. Man-made catastrophes and infrastructure failures

Pipeline bursts, industrial fires, or grid outages require rapid causation analysis and cross-policy coordination. The agent synthesizes incident reports, sensor feeds, and regulatory notices to inform coverage and subrogation. It orchestrates multi-claim vendor assignments while maintaining evidence chains.

Early signals of attorney involvement or adverse venue dynamics trigger specialized handling. The agent drafts litigation plans, suggests negotiation windows, and quantifies tail-risk scenarios. Proactive communication reduces disputes and shortens the time to fair settlement.

8. Subrogation against utilities, OEMs, and builders

Causation patterns and product defect signals surface recoverable losses and relevant counterparties. The agent assembles documentation—photos, purchase records, expert notes—and tracks statute limitations. Effective subrogation offsets indemnity and improves overall Claims Economics.

How does Catastrophic Claim Cost Control AI Agent transform decision-making in insurance?

It transforms decision-making by shifting from reactive, anecdotal judgment to proactive, explainable, portfolio-aware intelligence. Decisions become faster, more consistent, and more aligned with enterprise risk appetite and regulatory requirements.

1. From intuition to governed decision intelligence

The agent encodes best practices and regulatory guardrails into configurable policies, augmented by ML predictions. Adjusters and leaders see why a recommendation is made and how it aligns with coverage and fairness. This reduces variance and institutionalizes expertise across the workforce.

2. Portfolio-level steering in real time

Executives gain live visibility into claim counts, severities, and vendor capacity, enabling resource shifts by region and peril. The agent quantifies trade-offs—cost versus speed versus customer impact—so leaders can act deliberately during fluid events. These portfolio levers were previously unavailable in real time.

3. Market-signal and supply-chain awareness

Vendor pricing, material availability, and access constraints are modeled as dynamic inputs to decision-making. The agent negotiates within pre-set limits and flags anomalies, preventing runaway surge costs. This market awareness anchors decisions in current conditions rather than static rate cards.

4. Scenario planning and underwriting feedback loops

Scenario engines test “what-if” choices (e.g., more virtual inspections, different vendor mixes) and measure outcomes. Insights flow back to underwriting and reinsurance purchase, aligning claims realities with pricing and capital strategies. Decision-making becomes a continuous, data-driven loop, not a one-off reaction.

What are the limitations or considerations of Catastrophic Claim Cost Control AI Agent?

Limitations include data quality constraints, model bias risks, vendor dependencies, edge cases, and regulatory boundaries. Effective deployment requires rigorous governance, human oversight, and contingency planning for disaster conditions.

1. Data quality, availability, and bias

CAT events can disrupt data feeds; imagery may be delayed or cloud-obscured, and FNOL narratives can be sparse. Bias can creep in if training data underrepresents certain geographies, building types, or customer segments. Continuous monitoring, fallback logic, and fairness tests are essential.

2. Model risk management and explainability

Complex models can be brittle when exposed to novel event dynamics. Insurers must maintain documentation, challenger models, backtesting, and explainability to satisfy MRM frameworks. Human-in-the-loop checkpoints reduce the risk of over-automation.

3. Ethical use and fairness safeguards

The agent must avoid proxies that could unfairly impact protected classes, especially when allocating resources or setting reserves. Policy reasoning should be consistent and transparent, with recourse processes in place. Ethics reviews and bias mitigation strategies are non-negotiable.

4. Vendor dependency and lock-in

Overreliance on a single imagery or contractor network can create single points of failure. Multi-vendor strategies, open standards, and portability safeguards reduce lock-in and improve resilience. Contracting should include surge SLAs and data access provisions.

5. Edge cases and black swans

Unprecedented events or compound hazards can invalidate learned patterns. The agent needs conservative fallback modes and rapid human escalation pathways. Simulation and stress testing help prepare for these low-frequency, high-severity scenarios.

6. Regulatory and cross-border data constraints

Privacy, localization, and claims-handling regulations vary by jurisdiction. The agent must respect data residency, purpose limitations, and specific claims rules. Configurability by state or country is critical for compliant scale.

7. Disaster conditions: power and connectivity

Field operations may be hampered by outages and infrastructure damage. Offline-capable mobile tools, satellite communications, and cached models help sustain operations. Redundant data centers and failover designs maintain core decisioning.

What is the future of Catastrophic Claim Cost Control AI Agent in Claims Economics Insurance?

The future brings more multimodal intelligence, edge AI, parametric triggers, and collaborative data ecosystems, all governed by stronger risk and ethics frameworks. Insurers will blend autonomous claims for simple cases with expert-guided AI for complex losses, accelerating decisions without losing empathy and fairness.

1. Multimodal models across imagery, text, and time series

Next-generation agents will natively fuse aerial imagery, drone video, sensor streams, and claim narratives in unified models. This drives sharper severity estimates and earlier detection of total losses. It also reduces the need for repeat inspections and expedites settlement.

2. Edge AI and IoT under disaster constraints

On-device models in mobile apps and drones will perform triage even when connectivity is poor, syncing when networks recover. Property and vehicle sensors—where consented—will provide objective damage signals and shorten FNOL. Edge inference improves safety and speed in hazardous zones.

3. Parametric and smart-contract payouts

Parametric triggers combined with traditional indemnity claims will deliver fast partial payouts while full assessments are underway. Smart contracts can automate settlement when thresholds are met and documentation is complete. The agent will coordinate these hybrid paths to maximize customer relief and cost control.

4. Federated learning and industry data collaboratives

Federated approaches allow carriers to learn from broader patterns without centralizing sensitive data. Shared hazard and loss signals improve model generalization during rare events. This raises the floor on industry-wide CAT response quality.

5. Climate-informed underwriting feedback

As climate volatility reshapes hazard baselines, claim intelligence will inform localized pricing, mitigation incentives, and risk engineering. The agent will quantify mitigation ROI—defensible space, fortified roofs, floodproofing—to guide customer programs. Underwriting becomes more adaptive and prevention-oriented.

6. Generative AI copilots for adjusters and counsel

LLM copilots will draft coverage analyses, scope reviews, settlement proposals, and litigation briefs with embedded citations. They will summarize multi-source evidence into concise, explainable recommendations approved by humans. This elevates the role of adjusters from data wranglers to decision stewards.

7. Autonomous claims for simple losses, expert focus for complex

With confidence thresholds and guardrails, the agent will autonomously settle simple, well-documented claims quickly and accurately. Complex claims will receive earlier expert involvement, supported by rich analytics and collaboration tools. This bifurcation maximizes efficiency without sacrificing outcome quality.

FAQs

1. What types of catastrophes can the Catastrophic Claim Cost Control AI Agent handle?

It supports natural catastrophes like hurricanes, wildfires, floods, earthquakes, hail, and winter storms, as well as man-made events such as industrial accidents and infrastructure failures.

2. How does the AI Agent reduce indemnity leakage during CAT events?

It improves scoping accuracy with imagery analysis, enforces coverage rules, flags anomalies and fraud, and surfaces subrogation and salvage opportunities, all with auditable rationale.

3. Can the AI Agent integrate with my existing claims platform and vendor networks?

Yes. It connects via APIs and event streams to claims systems, policy admin, billing, document repositories, and third-party vendor networks without disrupting adjuster workflows.

4. How does the agent ensure fair and compliant claim decisions?

It embeds configurable guardrails aligned to jurisdictional rules, provides explainable recommendations, logs decision rationale, and supports human overrides with captured context.

5. What data does the AI Agent require to be effective?

It uses geocoded policy and claim data, hazard feeds, imagery, property attributes, adjuster notes, vendor capacity, and regulatory updates, with strict security and privacy controls.

6. Will adjusters be replaced by the AI Agent?

No. The agent augments adjusters by automating low-value tasks and providing decision support; humans remain accountable for complex, ambiguous, or sensitive decisions.

7. How quickly can insurers expect benefits after deployment?

Benefits typically emerge in phased rollouts—first via improved triage and communications, then vendor optimization and reserve accuracy—as models calibrate across events and regions.

8. What governance is required to manage model risk in CAT claims?

Insurers should implement model inventories, monitoring, backtesting, explainability, bias checks, challenger models, and human-in-the-loop controls aligned to their MRM framework.