AegisNow
Insurance, reinsurance, and actuarial operating context: connect policy, claims, financial, and modeling workflows within the agreed implementation scope.
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InsuranceAI actuarial
AI actuarial: accelerate reserving and claims development review
Actuarial teams spend valuable review time reconciling claims data, rebuilding development datasets, and explaining movements between reserving cycles. Different extracts and assumptions can make comparisons difficult to reproduce.
Solution blueprint: implementation is scoped with your team. The workflow below describes an engagement approach; it is not evidence of a completed customer deployment or a guaranteed return.
FROM PROBLEM TO PROCESS
How the workflow can improve.
Prepare reconciled paid and incurred claims datasets with documented adjustments.
Run approved reserving methods against versioned assumptions and compare scenarios.
Use AI to draft movement explanations grounded in the calculated results, then route the analysis to an actuary for challenge and sign-off.
How Genellipse brings the platforms together.
Genellipse maps the business rules and source systems, configures the agreed capabilities, and validates the workflow with the people who will operate it. Existing systems and supported interfaces determine the integration approach.
GeneData
Data foundation: connect approved sources, prepare governed datasets, track lineage and quality, and support repeatable analysis and reporting.
Cortex AI
Agent workflow: assemble evidence, assist with drafting and coordination, and apply configured permissions, evaluation, approval, and monitoring controls.
VALUE TO MEASURE
Where ROI can come from.
Recover time in data preparation and reporting while improving reproducibility. Measure preparation and review effort separately; a change in reserve estimates is not itself a cost saving or proof of improved accuracy.
Track these measures against your baseline.
- Hours to prepare the reserve review
- Unexplained reconciliation differences
- Reproducibility and back-testing results
Agree definitions, data sources, and an observation period before the MVP. Compare similar work, include exception handling and quality review, and subtract implementation and ongoing operating costs.
Start with a focused, reviewable MVP.
- Suggested starting scope
- One line of business and a historical reserving period, reproduced against the existing approved process.
- Data and integration needs
- Claims transactions, exposure data, development periods, recoveries, prior runs, and approved reserving assumptions.
- Review and control requirements
- Actuaries choose methods, assess uncertainty, validate results, and approve conclusions. AI-generated explanations never replace actuarial judgement.
What Genellipse can deliver with your team.
A current-state workflow map, data and interface assessment, configured MVP, evaluation evidence, and a phased delivery recommendation. Agree the output, acceptance criteria, operating owner, and improvement measures before extending the scope.
Follow the process in more detail.
The opportunity is to reduce preparation and reconciliation effort while making actuarial judgement easier to inspect. A controlled reserving workflow connects source snapshots, transformation rules, model versions, assumptions, and reviewer decisions.
Explore the workflow walkthroughBUILD YOUR BUSINESS CASE
Explore an illustrative ROI scenario.
Replace these example assumptions with your own baseline and validated MVP results. This calculator estimates the value of processing-time improvements; it does not predict a customer outcome.
ILLUSTRATIVE FIRST YEAR
- Capacity hours released annually
- 2,000
- Annual realized capacity value
- CA$60,000
- First-year total cost
- CA$44,000
- First-year net value
- CA$16,000
- First-year value ROI
- 36.4%
- Simple payback
- 6.7 months
Released capacity becomes cash savings only when spending is actually avoided. Otherwise, measure the value of agreed redeployment or additional output. The realization percentage must reflect that distinction.
Calculation method and assumptions
Annual hours = monthly volume × minutes saved per item × 12 ÷ 60. Annual realized value = annual hours × hourly cost × realization percentage. First-year cost = implementation cost + 12 × monthly operating cost.
First-year value ROI = (annual realized value − first-year cost) ÷ first-year cost. Payback = implementation cost ÷ monthly value after recurring costs, when that monthly value is positive.
The example assumes steady volume and immediate full operation. Include integration, licensing, AI usage, infrastructure, support, quality review, training, and change management in your costs. Model ramp-up separately before an investment decision. Currency selection changes the unit label; it does not convert amounts. Do not add the same benefit again as headcount savings, revenue, or avoided rework.
Bring us the workflow you want to improve.
Tell us about your source systems, current bottleneck, and the outcome you want to measure. Your selected use case will be added to an editable enquiry so we can start with the right context.
Discuss this use case