Fragmented source evidence
Policy, claims, premium and finance records may be extracted through different processes, making it difficult to prove what changed between source and actuarial input.
Insurance Data & Actuarial Controls
Actuarial Data Quality for Reliable Insurance Decisions
Build trusted, traceable data for pricing, reserving, valuation, experience studies, reinsurance, capital and regulatory reporting. DataConsultant helps insurers define critical data, expose quality risk, design controls, remediate root causes and sustain evidence from source systems to actuarial use.
✓Policy, premium, claims, reinsurance and finance data controls
✓Source-to-model lineage, reconciliations and transformation evidence
✓Business-owned rules, thresholds, exceptions and remediation
✓Monitoring, governance, operating model and implementation support
Scope is tailored to the insurer, actuarial purpose, data estate and applicable obligations. DataConsultant does not replace statutory actuarial judgment, legal advice, audit or regulatory approval.
Actuarial Data Quality Control Loop
Illustrative
CompletenessDefine threshold
ReconciliationEvidence status
LineageSource trace
ExceptionsOwned workflow
ReadinessDecision gate
Illustrative control states only; not client results, guarantees or regulatory conclusions.
Critical data element led
Source-to-model traceability
Risk-based controls
Actuarial, data & control ownership
Continuous monitoring & change control
Insurance operating context
Why Actuarial Data Quality Becomes a Business-Control Issue
Actuarial work depends on data assembled across policy administration, underwriting, claims, premium, reinsurance, finance, data platforms and end-user computing. Quality risk often appears when definitions, period cut-off, transformations, reconciliations, assumptions, exceptions or ownership diverge across that chain.
Recurring manual reconciliation
Repeated spreadsheet checks and late-period corrections can consume actuarial capacity without fixing the source, transformation or ownership cause.
Model input uncertainty
Models can be technically sound while inputs remain incomplete, stale, inconsistent or insufficiently traceable for the intended pricing, reserving or valuation decision.
Evidence and control expectations
Risk, finance, audit and regulatory stakeholders may need clear rules, ownership, reconciliation records, exception treatment, approvals and retained evidence.
Trigger conditions
When Insurers Commonly Need an Actuarial Data Quality Programme
The right trigger is not a generic “data problem”. It is a decision, reporting or control dependency where poor data could materially change interpretation, create rework or weaken assurance.
Pricing or product review
Experience, exposure, claims, premium and rating attributes need defined quality gates before use in analysis or rate decisions.
Reserving and valuation cycles
Period data needs controlled cut-off, aggregation, reconciliation, movement analysis and explainable exceptions.
Platform or data migration
Source-to-target mapping, transformations, historical conversion and post-load controls need actuarial fitness criteria.
Repeated quality incidents
Defects recur across reporting periods because root causes, ownership, thresholds or corrective controls are not operationalised.
Current state → target state
Move From Period-End Data Repair to Governed Actuarial Data Control
The target is not “perfect data”. It is a risk-aware capability that makes data fitness explicit, exceptions visible, responsibilities clear and remediation repeatable for the actuarial decisions that matter.
Typical current state
Reactive quality management around reporting and model cycles.
- Manual extracts and undocumented transformations
- Rules embedded in spreadsheets or individual knowledge
- Late reconciliation breaks and repeated adjustments
- Unclear critical data ownership and thresholds
- Exceptions closed without consistent root-cause evidence
- Limited traceability from source records to model inputs
Target controlled state
Continuous quality control aligned to actuarial purpose and risk.
- Defined critical data elements and business meaning
- Approved rules, tolerances and reconciliation logic
- Traceable source-to-model transformations and versions
- Owned exceptions with severity, action and closure evidence
- Preventive and detective controls positioned earlier in the flow
- Recurring monitoring, trend review and governed change
Assess Your Actuarial Data Quality Gaps
Identify critical data, recurring exceptions, reconciliation weaknesses and source-to-model control priorities.
Insurance process context
Where Actuarial Data Quality Sits Across the Insurance Value Chain
Data defects rarely originate inside the actuarial model alone. Controls need to connect the upstream process that creates data, the transformations that prepare it and the downstream decision that consumes it.
01Product & Pricing
02Distribution & Underwriting
03Policy Administration
04Premium & Collections
05Claims & Benefits
06Reinsurance
07Actuarial Valuation
08Finance & Regulatory Reporting
Control design principle: trace a material actuarial data element back to the process and system that creates or changes it, then position validation, reconciliation and ownership as close to the source of risk as practical.
Insurance data domains
Prioritise the Data That Drives Actuarial Decisions
Scope should be driven by intended use and materiality. Not every domain needs the same rule coverage, lineage depth, monitoring frequency or remediation priority.
Policy & Coverage
Customer / Party
Premium & Exposure
Claims & Benefits
Underwriting & Risk
Reinsurance & Treaty
Actuarial Assumptions
Model Inputs & Outputs
Reference & Master Data
Expenses & Commissions
Finance / Ledger
Regulatory Reporting
What DataConsultant does
Build a Controlled Path From Insurance Source Data to Actuarial Use
DataConsultant combines data quality, governance, metadata, architecture and operational control disciplines around the insurer’s actual actuarial decisions. The engagement can start as a targeted assessment and expand into implementation or managed quality operations.
Critical data scoping
Connect pricing, reserving, valuation, experience or reporting decisions to the specific elements, datasets and periods that require control.
Profiling & baseline
Measure observed quality patterns, missingness, invalid values, duplicates, relationships, distributions and historical anomalies.
Source-to-model lineage
Map extraction, joins, mappings, transformations, aggregation, adjustments and controlled datasets to downstream actuarial use.
Rules & reconciliations
Define testable business expectations, tolerances, control totals, cut-off logic and exception handling for material data.
Issue & remediation design
Classify impact and severity, assign ownership, trace root causes, define corrective actions and retain closure evidence.
Control framework
Position preventive, detective and corrective controls across source, integration, data platform, actuarial and reporting layers.
Scorecards & monitoring
Define measures, thresholds, alerting, trend views, decision gates and management reporting for ongoing quality oversight.
Ownership & governance
Clarify data owner, steward, actuarial SME, technology operator, risk reviewer and escalation responsibilities.
Implementation support
Translate approved rules and control designs into platform configuration, SQL, pipeline tests, workflows and dashboards.
Operational handover
Create runbooks, review cadence, rule change control, evidence requirements, training and managed-support boundaries.
Actuarial data quality framework
Define Quality as a Chain of Business Rules, Controls and Accountability
The control model links every critical data element to its intended actuarial use, a measurable expectation, an exception response and a named owner. This prevents quality from becoming an abstract score with no decision consequence.
Actuarial
Data Quality
Dimensions
Data Quality
Dimensions
CompletenessRequired records & fields
AccuracyAgreement with trusted evidence
ValidityFormats, domains & rules
TimelinessCut-off & availability
ConsistencyCross-system alignment
UniquenessDuplicate control
ReconciliationCounts, amounts & movement
TraceabilityLineage & version evidence
Data Element→Identify the policy, claim, premium, reinsurance, assumption or finance element that matters to the decision.
Business Rule→State the expected condition, population, period, transformation or reconciliation logic in business terms.
Quality Dimension→Classify the expectation so reporting and issue analysis use a consistent quality taxonomy.
Control & Threshold→Define where the check runs, what evidence it produces and how tolerance or materiality is assessed.
Exception & Impact→Route failures by severity and connect the defect to the affected actuarial process, output or reporting decision.
Owner & Remediation→Assign investigation, source correction, approved workaround, closure evidence and root-cause action.
Monitoring→Track recurring failures, drift, rule changes, overdue issues and control coverage across reporting periods.
Maturity assessment
Assess the Capability Needed for Sustainable Actuarial Data Control
An illustrative maturity model helps prioritise the next practical control improvements. It is not a client score and should be calibrated against the insurer’s risk, operating model and evidence.
| Capability | Reactive | Developing | Defined | Managed | Optimised |
|---|---|---|---|---|---|
| Critical data & ownership | Known informally | Priority lists exist | Approved inventory & owners | Linked to controls & decisions | Risk-based coverage adapts to change |
| Rules & reconciliations | Period-end manual checks | Repeatable templates | Governed rule catalogue | Automated where justified | Continuously tuned for signal & risk |
| Lineage & transformations | Individual knowledge | Key flows documented | Source-to-model lineage | Version & change evidence retained | Impact analysis integrated into change |
| Issue remediation | Manual correction | Issues logged | Severity, ownership & workflow | Root cause and recurrence tracked | Preventive control improvement embedded |
| Monitoring & reporting | Cycle-specific review | Basic scorecards | Defined measures & thresholds | Trend, alerts and governance cadence | Control coverage evolves with use and risk |
Define the Actuarial Data Controls Your Decisions Actually Need
Prioritise rules, reconciliations, lineage and ownership around pricing, reserving, valuation and reporting risk.
From insurance decision risk to control coverage
Map Actuarial Decisions to the Quality Failure Modes That Could Distort Them
The same quality dimension can have different consequences depending on the decision. Control coverage should therefore be designed from business impact backwards, not from a generic checklist.
Experience and exposure distortion
Missing classifications, inconsistent rating fields, duplicate exposures or mismatched claims history can affect analysis.
Control focus: completeness, validity, mapping, reconciliation.Period and movement uncertainty
Late claims, cut-off errors, inconsistent status, missing history or unexplained movement can weaken period analysis.
Control focus: cut-off, timeliness, movement, reconciliation.Model-input inconsistency
Policy attributes, benefits, assumptions or transformation logic can diverge across controlled datasets and versions.
Control focus: lineage, consistency, versioning, approval.Treaty and recovery mismatch
Coverage terms, ceded data, claims allocation or reference data can create reconciliation and eligibility exceptions.
Control focus: reference integrity, linkage, reconciliation.Untraceable output evidence
Adjustments, aggregations or manual interventions can be difficult to reproduce without controlled lineage and evidence.
Control focus: traceability, control totals, approval, audit trail.Target architecture & data flow
Place Quality Gates Across the Source-to-Actuarial Pipeline
The target pattern is technology-neutral. It can be implemented using existing policy, claims, finance, data-platform, governance and actuarial tooling when those investments are fit for purpose.
Policy / Claims / Premium / Reinsurance / Finance Sources
Controlled Extraction & Period Cut-off
Standardise, Map & Reference Data
Data Quality Rules & Profiling
Counts, Amounts & Reconciliations
Exception Queue & Root Cause
Controlled Actuarial Dataset
Model / Analysis / Reporting Use
Evidence, Scorecards & Retention
Cross-cutting controls: Metadata & Lineage | Access & Privacy | Versioning & Change Control | Approvals | Audit Evidence | Cost & Performance
Production monitoring & drift
Detect Deterioration Before the Next Actuarial Cycle
Monitoring should distinguish data-quality deterioration from legitimate business change and route only material signals to accountable owners.
Volume & completeness
Reconciliation breaks
Rule exceptions
Period-to-period drift
Transformation changes
Input distribution shifts
Issue ownership & ageing
Control evidence status
Human review & governance model
Keep Decision Rights With the People Accountable for the Risk
DataConsultant can design and operate the data-quality process, while actuarial judgment, policy interpretation and risk acceptance remain with authorised client roles.
Executive sponsorSets priority, funding, risk appetite and escalation path.
Actuarial owner / SMEConfirms intended use, business meaning, materiality and acceptable evidence.
Data owner / stewardApproves definitions, rules, ownership and remediation decisions.
Technology / data engineeringImplements controlled extraction, transformations, rules, logging and fixes.
Risk / compliance / privacyReviews applicable control, evidence, policy and privacy requirements.
DataConsultant deliveryAssesses, designs, implements, documents, monitors and transfers capability within scope.
Actuarial input readiness gates
Use Explicit Release Criteria for Controlled Datasets
A release gate provides a repeatable decision point before a dataset is accepted for a material actuarial purpose.
1Required source feeds received and period cut-off confirmedEvidence ready
2Critical rules and reconciliations executedResults reviewed
3Material exceptions classified and ownedDecision recorded
4Lineage, transformations and versions identifiableTraceable
5Actuarial owner accepts fitness for the intended useClient decision
AI / model considerations
Data Quality Is One Layer of Model and AI Control
If predictive, machine-learning or generative-AI methods are used in an insurance or actuarial workflow, the data-quality scope may also need provenance, representativeness, leakage, feature validity, training/grounding data controls, output review and monitoring. DataConsultant does not guarantee model accuracy or replace model-risk, actuarial or regulatory governance.
- 01Document source, purpose and allowed use of training, feature or grounding data.
- 02Define quality and representativeness criteria appropriate to the decision and risk.
- 03Track model/input versions, changes, exceptions and human-review evidence.
- 04Monitor relevant quality or drift signals after release and reassess when data changes.
Regulatory & professional reference points
Align the Control Design With Applicable Insurance, Actuarial and Privacy Context
References should guide discovery and control mapping, not be treated as a blanket compliance checklist. Applicability must be confirmed for the insurer, line of business, jurisdiction, data handled and work performed.
Actuarial, Finance and Investment Functions of Insurers
IRDAI’s Actuarial department publishes the 2024 regulations and the Master Circular dated 17 May 2024. These are relevant reference points when scoping data, evidence and control dependencies around actuarial and reporting functions.
Review IRDAI Actuarial references ↗Actuarial Practice Standards
IAI publishes Actuarial Practice Standards, including APS 34 General Actuarial Practice. Applicable professional standards should be interpreted by appropriately qualified actuarial professionals when defining evidence, review and practice requirements.
Review IAI practice standards ↗Digital Personal Data Protection Framework
Where policyholder or other personal data is in scope, privacy design should consider the DPDP Act and the Digital Personal Data Protection Rules, 2025, including their notified commencement timetable and authorised legal interpretation.
Review MeitY DPDP Rules references ↗Assurance boundary: DataConsultant can map data flows, controls, evidence and remediation to agreed requirements. It does not provide legal opinions, statutory actuarial opinions, certification, regulatory approval or statutory audit. Specialist review should be commissioned where those conclusions are required.
Delivery methodology
From Actuarial Decision Context to Operational Data Quality Controls
Delivery is evidence-led and staged so decisions about scope, materiality, ownership and implementation are made before automation or tooling expands.
01Understand
Actuarial uses, products, stakeholders, obligations and pain points.
02Trace
Map domains, sources, data flows, transformations and control points.
03Assess
Profile data, test existing checks and identify material gaps.
04Design
Define rules, reconciliations, thresholds, ownership and evidence.
05Implement
Configure or code controls, workflows, lineage and scorecards.
06Validate
Test expected, exception and boundary cases with accountable users.
07Operate
Monitor, remediate, govern change and continuously improve.
Phase 1Baseline
Critical data, current rules, lineage and issue evidence.
Phase 2Control Design
Prioritised rules, reconciliations, ownership and gates.
Phase 3Pilot & Prove
Implement selected data flows and tune thresholds.
Phase 4Scale
Extend control coverage to agreed products, entities or cycles.
Phase 5Operate & Improve
Monitoring, issue trends, rule change and root-cause prevention.
Build a Phased Actuarial Data Quality Roadmap
Sequence assessment, control design, pilot implementation, scale-up and operational handover around business risk.
Tangible outputs & client inputs
Produce Deliverables That Can Be Implemented, Governed and Reused
The deliverable pack is tailored to the decisions and operating teams that need to act on it. Missing evidence is recorded as a limitation rather than assumed.
Representative deliverables
Final outputs depend on scope, evidence and whether implementation or ongoing operations are included.
- Actuarial data quality current-state assessment
- Critical data element and intended-use inventory
- Insurance data-domain and ownership map
- Source-to-model lineage and transformation map
- Data quality rule and tolerance catalogue
- Reconciliation and control matrix
- Exception severity and issue workflow
- Root-cause and remediation backlog
- Quality scorecard and KPI specification
- Monitoring, alerting and evidence design
- Operating model, RACI and review cadence
- Implementation roadmap and acceptance gates
- Runbooks, templates and knowledge-transfer pack
- Risk, assumption, dependency and limitation register
What DataConsultant needs from the client
Access is agreed during mobilisation using the least-privilege approach appropriate to the work.
- 01Named sponsor plus actuarial, data, technology and control stakeholders.
- 02Defined actuarial use cases, reporting cycles and priority pain points.
- 03System inventory, data-flow diagrams, mappings, extracts and metadata where available.
- 04Representative data access or controlled profiling extracts approved for use.
- 05Existing reconciliation workbooks, quality checks, incidents, audit or risk findings.
- 06Policies, standards, retention, privacy, security and regulatory interpretations relevant to scope.
- 07Technical deployment support if controls are to be implemented in client platforms.
Implementation & ongoing operations
Carry the Design Through Build, Handover and Continuous Improvement
DataConsultant can stop at an assessment or continue into implementation and managed quality operations. Boundaries are explicit so source-system ownership, actuarial decisions and operational responsibilities remain clear.
Implementation support
Translate approved designs into working controls using client-approved tools and delivery processes.
- SQL or platform rules and reconciliations
- Pipeline and release checks
- Scorecards, alerts and workflow integration
- Testing and acceptance evidence
Operational transition
Prepare the internal team to run the capability with clear procedures, decision rights and evidence.
- Runbooks and RACI
- Control calendar and review cadence
- Training and knowledge transfer
- Rule and threshold change process
Managed quality operations
Provide recurring monitoring and coordination for an agreed control estate and service boundary.
- Scheduled control execution and reporting
- Issue triage and ageing oversight
- Root-cause trend analysis
- Continuous control improvement
Business outcomes
Target Better Evidence, Faster Issue Resolution and More Reliable Actuarial Inputs
Outcomes should be measured against the agreed use case and baseline. DataConsultant does not guarantee financial, regulatory or model outcomes.
Clearer input readiness
Decision gates make unresolved material exceptions visible before actuarial use rather than after output review.
Stronger traceability
Teams can follow critical elements through source, transformation, adjustment, model input and reporting layers.
Focused remediation
Severity, business impact and root-cause ownership direct effort to recurring defects that matter most.
More sustainable control
Rules, ownership, monitoring and evidence become a shared operating capability instead of period-end individual knowledge.
Commercial scope & engagement models
Choose the Delivery Model That Matches the Actuarial Data Problem
Commercial treatment is scope-led. DataConsultant does not publish a fixed fee or fixed duration for Actuarial Data Quality because the work can range from a focused diagnostic to multi-domain implementation and ongoing monitoring.
Focused assessment
Best for a defined actuarial use case, data domain or recurring quality problem that needs evidence and a prioritised remediation plan.
Framework & control design
Best when teams need a governed rule catalogue, reconciliation model, operating procedures and implementation-ready specifications.
Implementation programme
Best when approved rules, lineage, workflows, monitoring and data controls need to be built or integrated into existing platforms.
Managed quality support
Best when an agreed control estate needs recurring monitoring, issue coordination, reporting, rule maintenance and continuous improvement.
Buyer decision guidance
Is This the Right Engagement for Your Insurance Team?
A focused Actuarial Data Quality engagement is most useful when data fitness, lineage, reconciliation or control evidence is materially affecting actuarial work.
Good fit when
- Pricing, reserving or valuation teams repeatedly correct or reconcile the same data.
- Critical actuarial inputs lack documented rules, ownership or source-to-model lineage.
- A data platform, policy system or actuarial process change needs controlled acceptance criteria.
- Risk, audit or regulatory review has exposed evidence, quality or remediation gaps.
- The insurer wants to move from cycle-specific checks to sustainable monitoring.
A different service may be more appropriate when
- The requirement is a statutory actuarial opinion, actuarial methodology sign-off or formal audit.
- The problem is only one isolated technical defect with no wider data-control dependency.
- The primary need is software procurement rather than an operating control capability.
- Required data, accountable stakeholders or authorised access cannot be made available.
- The requested outcome is a guarantee of compliance, model accuracy or financial result.
Build an Actuarial Data Quality Capability That Survives Reporting Cycles and System Change
Connect assessment, rules, lineage, remediation, monitoring and accountable operations into one controlled programme.
Why DataConsultant
Combine Insurance Context With Enterprise Data, Governance and Implementation Capability
The engagement is designed as an enterprise capability problem—not a dashboard-only, staffing-only or software-resale exercise.
Decision-led scope
Start with the actuarial decision and business consequence, then define data and control coverage.
Cross-functional depth
Connect data quality with governance, lineage, architecture, engineering, risk and operational ownership.
Implementation-ready
Produce specifications, tests, workflows and roadmaps that can move into build rather than stop at high-level recommendations.
Sustainable operation
Design monitoring, change control, issue management and knowledge transfer so the capability can continue after project delivery.
Frequently asked questions
Actuarial Data Quality Questions for Insurance Buyers and Delivery Teams
Answers cover service scope, data domains, controls, implementation, governance, security, pricing and ongoing support.
What is actuarial data quality?
Actuarial data quality is the controlled assessment and management of data used for actuarial analysis and insurance decisions so that critical inputs are sufficiently complete, accurate, consistent, valid, timely, unique, reconcilable and traceable for their intended purpose. The exact dimensions, rules and tolerances should be agreed against the specific pricing, reserving, valuation, experience, capital or reporting use case.
What does DataConsultant’s Actuarial Data Quality service cover?
The service can cover current-state assessment, critical data element identification, source-to-model lineage, profiling, business-rule design, reconciliation controls, exception analysis, ownership, remediation planning, scorecards, monitoring, operating-model design and implementation support. Final scope depends on the insurer’s products, data flows, systems, regulatory context and the actuarial decisions in scope.
Which insurance data domains can be included?
Relevant domains can include policy, customer or party, coverage and benefits, premium, claims, underwriting, exposure and risk, reinsurance, expenses, actuarial assumptions and reference data, model inputs and outputs, finance or ledger data and regulatory reporting data. Only domains that materially affect the agreed actuarial use cases should be included.
Can you assess data used for pricing and reserving?
Yes. Pricing and reserving can be scoped around the source data, transformations, extracts, assumptions, reconciliations, controls and evidence used by the relevant actuarial process. DataConsultant focuses on data fitness, lineage, control and operational readiness; final actuarial methods, judgments and statutory sign-off remain with appropriately authorised actuarial roles.
How do you handle data reconciliations?
Reconciliation design starts with the business purpose and control boundary. Depending on the process, this can include source-to-extract counts and amounts, cross-system totals, movement analysis, period cut-off checks, control totals, exception tolerances and evidence of investigation and closure. Reconciliation logic is documented, owned and tested before operational use.
Can you improve source-to-model data lineage?
Yes. The engagement can map data from policy, claims, finance, reinsurance and other approved sources through extraction, transformation, aggregation and controlled actuarial datasets to model inputs and reporting outputs. The required lineage depth is determined by risk, change impact, assurance needs and available metadata.
How are regulatory requirements handled?
Relevant regulatory and professional references are identified during discovery and mapped to the data, control and evidence requirements in scope. Applicability depends on jurisdiction, insurer type, product, process and the work being performed. DataConsultant supports data and control readiness but does not provide legal advice, statutory actuarial opinions, regulatory approval or audit certification.
How do you protect sensitive insurance and policyholder data?
Delivery can be designed around least-privilege access, controlled environments, data minimisation, masking or pseudonymisation where appropriate, secure transfer, role-based access, approved retention and evidence controls. Privacy and security requirements should be confirmed with the client’s authorised legal, privacy and security teams for the applicable data and jurisdictions.
Can DataConsultant implement the quality controls as well as design them?
Yes. Implementation can be separately scoped to configure or code approved rules, reconciliations, pipeline checks, dashboards, alerts, issue workflows and lineage integrations using the client’s existing or selected tools. Deployment responsibilities, testing, release controls and acceptance criteria are agreed before build work begins.
Can you provide ongoing actuarial data quality monitoring?
Yes. Ongoing support can include scheduled control execution, scorecard production, threshold and alert review, issue coordination, recurring root-cause analysis, rule tuning, change control, evidence retention and service reporting. The operating boundary and accountability model are defined so that business and actuarial owners retain the decisions that belong to them.
What deliverables should we expect?
Typical outputs can include an assessment report, critical data element inventory, source-to-model lineage map, data quality rule catalogue, reconciliation and control matrix, exception and issue workflow, remediation backlog, scorecard specification, monitoring design, operating procedures, ownership model, implementation roadmap and knowledge-transfer pack.
How long does an actuarial data quality engagement take?
Timeline is confirmed after scoping. It depends on the number of actuarial use cases, product lines, legal entities, source systems, data domains, reporting periods, required history, stakeholder availability, access constraints, control depth, implementation requirements and review cycles.
How is Actuarial Data Quality pricing calculated?
DataConsultant uses custom scope and pricing for this service. Cost depends on the number of data domains and systems, lineage depth, data-access approach, profiling volume, rule and reconciliation complexity, control design, workshops, regulatory and assurance needs, deliverables, implementation depth and ongoing support. A quote is prepared after the required scope and assumptions are understood.
Discuss your requirement
Define the Actuarial Data Quality Controls Your Insurance Business Needs
Tell us which actuarial process, data domains, recurring issues or transformation initiative you need to address. We can help shape the assessment boundary and the next practical step.
- Focused assessment or broader actuarial data quality programme
- Pricing, reserving, valuation, reinsurance or reporting use cases
- Source-to-model lineage, rules, reconciliations and evidence
- Implementation and platform-neutral control design
- Operating model, monitoring and managed support options
- Custom scope, assumptions, deliverables and quote