Transform manual, time-intensive loss reserving workflows into an automated, governance-first agentic recipe that delivers actuarially sound cohort segmentation with full transparency and regulatory compliance.
Loss reserving depends on identifying groups of claims that develop similarly over time. Yet the process of creating, testing, and validating these cohorts remains overwhelmingly manual. creating a structural bottleneck that limits precision, speed, and adaptability.
Actuaries manually review dozens or hundreds of granular triangles, relying on visual pattern recognition and expert judgment. a process that takes months per reserving cycle.
Brute-force triangle generation produces signal and noise together. Distinguishing meaningful patterns from random variation requires intensive expert review at scale.
Due to operational burden, only a fraction of potential cohort hypotheses are tested. Promising segmentation strategies remain unexplored simply due to time constraints.
Cohorts are refreshed infrequently. not because they lack value, but because the process doesn't scale. This limits adaptability to emerging risks like climate volatility or inflation.
Documentation focuses only on final selections, with limited transparency into rejected alternatives. This reduces auditability and makes it difficult to explain cohort logic to regulators.
Cohort credibility is assessed through judgment and experience, not systematic testing. There's no repeatable framework for comparing alternative cohort designs.
The traditional approach relies heavily on actuarial expertise and manual iteration. While this produces sound results, it doesn't scale with increasing data volume, granularity, or the need for rapid adaptation to changing risk dynamics.
Existing tools produce triangles at highly granular levels. by peril, business unit, geography, loss size, attorney involvement, and more. This creates dozens or hundreds of triangles for a single line of business.
High volume, no prioritizationActuaries visually review these triangles to identify similar development patterns. They look for tight clustering of age-to-age factors, convergence trends, and stability across accident years.
Time-consuming, varies by analystBased on pattern recognition, actuaries iteratively group triangles into candidate cohorts. This is exploratory. combining and recombining until something "looks right" based on experience and judgment.
Not standardized, limited hypotheses testedCohort credibility is evaluated using volume thresholds, factor stability checks, and reasonableness of ultimate loss estimates. These assessments are not automated and lack systematic testing frameworks.
Subjective, no comparative scoringDocumentation typically covers only the final cohort structure. Rejected alternatives and the rationale behind choices are often undocumented, limiting explainability for auditors and regulators.
Reduced auditability and repeatabilityDue to the operational burden, cohorts are refreshed infrequently. often only when development patterns appear to have shifted significantly. The process cannot keep pace with increasing data volume or volatility.
Lagging indicator, not proactiveThe Core Constraint: Traditional methods are limited by process, not by analytical value. The inability to systematically explore and validate cohort alternatives creates a structural bottleneck that prevents actuarial teams from achieving the precision and adaptability modern reserving demands.
Rather than retrofitting AI onto existing processes, ElevateNow is built from first principles: deterministic calculations, agentic orchestration, and regulatory compliance by design.
LLMs never generate numbers. All actuarial calculations. ATA factors, R² regressions, reconciliation checks. are executed by auditable Python code. The AI agent orchestrates workflow and synthesizes narratives, but cannot hallucinate reserve amounts.
Zero Math Hallucination RiskDeterministic tools handle all data transformations: triangle construction, cohort slicing, statistical testing, and reconciliation. AI agents handle hypothesis generation, narrative synthesis, and assessment recommendations.
Clear Separation of ConcernsEvery calculation is traceable to Python source code. Audit trails are automatically generated. Schema validation catches errors before AI processing. This architecture directly addresses ASOP 56 (Modeling) transparency requirements.
Regulatory-Ready ArchitectureRather than rigid pipelines, ElevateNow uses a dynamic recipe matrix. The AI agent decides when to call tools based on analysis context, data characteristics, and human checkpoint approvals. enabling flexible, adaptive workflows.
Flexible, Context-Aware RoutingThe agent analyzes actual portfolio metrics. accident year distributions, dimension values, portfolio shares. to propose 4-5 testable hypotheses. Predictions cite specific data from deterministic tool outputs, not generic actuarial knowledge.
Evidence-Based RecommendationsEvery cohort design undergoes three automated tests: homogeneity (within-cohort R²), heterogeneity (one-level-up pattern comparison), and retrospective (actual vs expected). Composite scores enable objective ranking of alternative designs.
Repeatable Validation FrameworkWhy This Matters: Traditional AI approaches mix deterministic and stochastic logic, creating ambiguity about where calculations come from. ElevateNow's hard boundary design eliminates this ambiguity: if it's a number, it came from Python code. If it's an interpretation, it came from the AI agent. This separation is what makes the agentic recipe auditable, explainable, and safe for production reserving.
An end-to-end automated system that transforms months of manual cohort analysis into a one-hour, governance-first workflow. without compromising actuarial standards or regulatory compliance.
From CSV upload to final cohort selection, ElevateNow handles data ingestion, hypothesis generation, statistical testing, retrospective validation, and documentation. all while maintaining human oversight at strategic checkpoints.
Upload loss triangle CSV. System validates data quality, constructs cumulative master triangle, enforces MECE reconciliation, and generates data profile with portfolio metrics.
Agent analyzes portfolio characteristics and proposes 4-5 cohort hypotheses. each with data support, credibility checks, expected performance scores, and actuarial rationale.
System slices master triangle into cohort sub-triangles per approved hypothesis. Validates credibility thresholds, enforces MECE completeness, and verifies reconciliation (cohort sum = master at every cell).
Three rigorous tests applied to each cohort: Homogeneity (within-cohort R² regression), Heterogeneity (one-level-up pattern comparison), and Retrospective (actual vs expected emergence). Composite score ranks all hypotheses objectively.
Agent synthesizes test results into clear assessment: composite score >75 = recommended, 60-75 = acceptable with refinements, <60 = try alternative hypothesis. Comparative rankings across all tested designs support informed decision-making.
Generate deliverables: cohort triangles CSV (ready for reserving models), master triangle CSV (for benchmarking), and full documentation package (audit trail, statistical test results, methodology narrative). All outputs are production-ready.
Transform 3-6 month manual cohort cycles into 1-hour automated workflows. Test 5+ hypotheses per analysis instead of 1-2. Enable quarterly refresh cycles instead of annual. dramatically increasing responsiveness to emerging risks.
Systematic testing across multiple hypotheses identifies the objectively best cohort design. not just the first acceptable one. Heterogeneity scores >80 indicate materially different development patterns that improve reserve precision.
Test multi-dimensional hypotheses that would be operationally infeasible manually. Explore interactions between dimensions (e.g., Peril × CAT × Business Unit) to discover non-intuitive but actuarially meaningful segmentations.
Automated audit trails document every decision. Full transparency into rejected alternatives and statistical rationale. ASOP 56 compliant by design. every calculation traceable to source code, not LLM generation.
Eliminate analyst-to-analyst variation. The same data and hypothesis always produce the same test results (deterministic calculations). Composite scoring provides objective, comparable metrics across all designs.
Start with Property, expand to CMP Liability, Workers' Comp, and beyond. The agentic recipe scales with data volume (thousands of triangles) and complexity (additional dimensions, unstructured data) without linear increases in manual effort.
Join forward-thinking actuarial teams leveraging AI to achieve faster, more accurate, and more defensible loss reserve estimates. without compromising regulatory standards.