Actuarial software we provide

Actuarial software that moves your team from running models to explaining results

Quarter-end still means copying data between spreadsheets, projection engines, and reporting templates. We build actuarial software around your existing models, with AI that checks data, flags assumption drift, and explains movements before anyone asks.

Streamline your actuarial process with us

Why the projection engine is rarely the bottleneck

Most insurers already own a capable projection engine. The time goes elsewhere. Actuaries clean policy extracts, update assumption tables by hand, rerun models after late data, and rebuild change analyses in spreadsheets. Each step adds risk and slows the close.

Svitla builds actuarial software that handles everything around the engine. It manages data, assumptions, runs, and results in one governed environment. Your models keep doing the math, and your actuaries get time back to understand what the numbers mean.

Who we serve

Pricing actuaries

You need fast rating models, clean experience data, and a way to test price changes before they reach the market.

Reserving actuaries

You run triangles and projections every quarter. You need consistent data, method comparison, and a clear audit trail behind each selection.

Valuation and financial reporting teams

You produce IFRS 17 or LDTI results under tight deadlines. You need controlled runs, reconciliations, and movement analysis that finance can trust.

Challenges we address

Actuarial tooling is often a patchwork. In an Oliver Wyman survey of over 40 life insurers published by the SOA, most participants used more than one software package just to model assets. Every handoff between tools adds manual work.

Policy data arrives late and messy, and actuaries fix it in spreadsheets

With custom software: Automated pipelines validate, reconcile, and format data before it reaches the model. AI flags outliers and missing fields.

Assumptions live in scattered files with unclear versions

With custom software: One assumption repository tracks versions, owners, approvals, and the run that used each table.

Runs are started by hand, one after another, on limited hardware

With custom software: An orchestration layer schedules runs, scales cloud compute, and reruns only what changed.

Analysis of change takes days, and the explanation is built after the deadline

With custom software: Movement analysis runs automatically, and AI drafts commentary on the largest drivers for actuaries to review.

Our process at Svitla

STEP 1

We map your actuarial cycle

We follow data from admin systems to final results, and note every handoff, spreadsheet, and manual check.

STEP 2

We agree on what stays and what changes

Your projection engine and approved methods stay. We decide together which steps to automate first.

STEP 3

We design for governance

We define access, approvals, version control, and audit trails that meet your model risk and audit requirements.

STEP 4

We build and run in parallel

The new process runs beside your current close. Results are reconciled to the decimal before switchover.

STEP 5

We hand over and extend

Your team takes over daily use. We add new products, methods, and reports as your needs grow.

Schedule a call

Actuarial data management

Pulls policy, claims, and asset data from source systems.

Validates and reconciles records against finance totals.

Flags anomalies and missing fields with AI.

Converts data into each model's required format.

Assumption management

Stores mortality, lapse, expense, and economic tables in one place.

Tracks versions, owners, and approvals.

Links each table to the runs that used it.

Alerts actuaries when experience drifts from assumptions.

Run orchestration and cloud compute

Schedules runs and dependencies across engines.

Scales compute for stochastic and nested runs.

Reruns only the parts affected by a change.

Logs inputs, settings, and outputs for every run.

Experience studies with machine learning

Analyzes mortality, lapse, and claims experience by segment.

Uses ML to surface new risk drivers for actuaries to review.

Compares actual with expected over time.

Produces credibility-weighted results.

Results, movement analysis, and reporting

Runs analysis of change step by step.

Drafts commentary on the main drivers with AI.

Feeds IFRS 17, LDTI, and Solvency II reporting.

Reconciles results with general ledger entries.

Model governance

Keeps an inventory of models, owners, and validations.

Controls access and changes by role.

Records every approval and override.

Exports evidence packs for audits.

Is custom actuarial software the best option for your needs?

Answer a few simple questions to find out whether you should choose a custom solution or a pre-built actuarial platform.

1 / 8

Does your team spend more time preparing data than analyzing results?

Do you keep assumption tables in spreadsheets or shared folders?

Do you use more than one projection engine or modeling tool?

Have you tried a vendor's add-on that didn't fit your existing models?

Does your quarter-end close regularly run late?

Would AI-based experience studies or movement commentary help your team?

Do you plan to add new products, entities, or reporting bases soon?

Important integrations for actuarial software

Projection and valuation engines

We connect to engines such as Prophet, AXIS, or MG-ALFA, as well as Python and R models. Inputs and outputs flow automatically, and the engines keep doing the core calculations.

Policy administration and claims systems

In-force, new business, and claims data come straight from source systems, with reconciliation to control totals.

Investment and asset systems

Asset holdings and cash flows feed ALM and capital models without manual extracts.

Finance and accounting systems

Results flow to subledgers and the general ledger for IFRS 17, LDTI, or local GAAP reporting.

Economic scenario generators and market data

Scenario sets and market curves load on schedule and are versioned like any other assumption.

Faster close

Automated data preparation.

Parallel and incremental runs.

Movement analysis ready on day one.

Fewer errors

Controlled assumptions and versions.

Automatic reconciliations.

Fewer late restatements.

Better decisions

More scenarios tested per cycle.

Earlier warning on assumption drift.

Clearer insight for pricing and capital.

Easier audits

Full audit trail for every run.

Model inventory and validation records.

Shorter audit cycles.

Process and control discovery

We document the full cycle, from source data to signed-off results, including every control and sign-off. This shows where automation saves the most time without weakening controls.
Data model and pipelines

We design one actuarial data model for policies, claims, assets, and assumptions. Pipelines validate and reconcile data on every load.
Assumption and run management

We build the assumption repository, approval workflow, and run orchestration. We test engine connectors against current outputs.
Analytics and AI

We add experience studies, drift alerts, and commentary on movement. Every AI output is reviewed by actuaries and documented for model governance.
Parallel run and reconciliation

The new process runs beside the old one for at least one full reporting cycle. Differences are traced and resolved before cutover.
Rollout and extension

We train each team, retire manual steps, and add products, entities, or reporting bases in planned releases.

How much does actuarial software cost?

A focused tool for a small insurer, MGA, or consulting firm typically costs $150,000 to $300,000. Examples include an experience study platform or an assumption-and-run manager for one line. An enterprise actuarial platform with data pipelines, governance, cloud compute, and reporting feeds typically costs $350,000 to $800,000. The final cost depends on lines of business, engines, and reporting bases.

Want to understand the cost of your actuarial software?

Calculate the cost

Check our client success cases

Energy Saving Solution

Project Type Web Development

Industry Energy

Learn how we overhauled a secure client portal for a solar energy company, modernizing its technology stack, implementing a fresh UI/UX, migrating to Microsoft Azure, and introducing a new billing system, thereby enhancing user satisfaction and streamlining operational efficiency.

Cloud Services for SCM

Project Type Cloud Solutions

Industry Financial Services

Explore our approach to providing comprehensive cloud support for a US technology company’s supply chain management platform. Through meticulous AWS infrastructure management and Kubernetes deployments, we enhanced operational efficiency, security, and scalability.

InvoiceASAP

Project Type Mobile Development

Industry Financial Services

Discover how we enhanced InvoiceASAP, a mobile invoicing network, by developing new features, increasing app accuracy, and improving reporting and visualization. With a dedicated development and QA team, we expanded capabilities, improved stability, and enhanced user experience.

Consulting

We review your actuarial cycle, tools, and controls, then define what to automate and in what order.

Engineering

We build data, assumption, run, and results tools around your engines and methods.

Modernization

We audit your spreadsheet tools and legacy models and plan the migration. The new environment adds version control and automated runs the old setup lacked. We cut over after a parallel run and validate every result.

Data pipeline engineering

We build and maintain the pipelines that feed policy, claims, asset, and market data into your models.

FAQ

Do we have to replace Prophet, AXIS, or our other engines?

No. Most clients keep their engines for the core calculations. We build the data, assumption, run, and reporting layers around them, so actuaries stop doing manual handoffs.

How do you protect approved methods?

Methods stay in your models or in code your actuaries approve. Every change goes through review, and we compare results with the previous version before release.

Can AI outputs be used in reported results?

AI supports actuaries but doesn’t replace their judgment. It flags anomalies, suggests risk drivers, and drafts commentary. Actuaries review and sign off on everything that reaches reports.

How does this help with IFRS 17 or LDTI?

The software prepares inputs, controls runs, and produces movement analysis and reconciliations. It feeds your existing subledger or accounting engine with consistent, traceable data.

What happens when source data arrives late?

Pipelines process whatever is ready and flag what’s missing. When late data arrives, we repeat only the affected runs. You see exactly which results changed.

How do you handle model risk management?

The software keeps a model inventory, validation records, and change logs. Access and approvals follow your model risk policy, and you can export evidence for auditors.

Can we start with one line of business?

Yes. Many clients start with one line or one process, such as experience studies. The data model is built to add more lines and functions later.

Stop rebuilding your analysis of change in spreadsheets

Tell us about your actuarial cycle and tools. We’ll show you what custom actuarial software could change first.