Gains:
- Ability to summarize and interpret the three pillars of Solvency II, SCR/MCR (capital requirement) and technical provision concepts with the support of artificial intelligence
- Ability to configure model validation, backtesting, sensitivity analysis and independent review steps with artificial intelligence
- Ability to understand that artificial intelligence itself is a model that needs to be verified, and that the final responsibility for the capital account remains with the appointed actuary and management.
An insurance company makes promises that span decades with every policy it sells. In order to keep these promises, it must not only allocate sufficient provisions but also maintain sufficient capital against bad scenarios. Capital is a company's buffer that absorbs unexpected losses, a measure of its financial resilience. The framework regulating this resilience in Europe and largely in Türkiye is Solvency II. In this unit, we will cover the logic of Solvency II, capital requirement (SCR/MCR) and model validation, one of the most mature disciplines of actuarial; We'll see how to use AI in this field — and why AI itself is a model that needs to be validated.
Let us remind you from the beginning: the capital adequacy calculation determines whether a company will survive or not; The confidence of the regulator, credit rating agencies and policyholders depends on it. In this area, AI produces briefs, codes, and drafts; but ultimate responsibility for the capital figure lies with the appointed actuary and management, and each model — including AI — must undergo independent verification.
The three pillars of Solvency II
Solvency II is built on three "pillars". Column 1 — Quantitative requirements: defines how technical provisions and capital requirements will be calculated. There are two thresholds here. SCR (Solvency Capital Requirement): It is the capital that the company must keep in order to meet its obligations within a year at a 99.5 percent confidence level, even in a bad scenario that will occur approximately once in 200 years. MCR (Minimum Capital Requirement) is the absolute lower limit below which the license will be in danger. Pillar 2 — Governance and risk management: covers internal control, risk management system and own risk assessment (ORSA). Pillar 3 — Transparency and reporting: disclosure obligations to the public and the regulator.
SCR can be calculated in two ways. Standard formula: with ready-made risk modules and parameters defined by the regulator. Internal model: company's own developed, regulator-approved model (usually Monte Carlo based). Internal models are more accurate but require a much more burdensome verification and validation process. The regulatory authority in Türkiye is SEDDK (Insurance and Private Pension Regulation and Supervision Agency) and the legislation is gradually converging to Solvency II principles.
The following table summarizes the key concepts:
concept
Meaning
critical point
Technical response
Present value of liabilities
Best guess + risk margin
SCR
1-year loss capital with 99.5% confidence
Main qualification criterion
MCR
Absolute minimum capital
Six = license risk
ORSA
Company's own risk assessment
Pillar 2, management responsibility
Solvency ratio
Equity / SCR
Above 100% = sufficient
Technical response: best estimate and risk margin
In Solvency II, the technical provision consists of two parts. Best estimate: the expected value of all future cash flows, probability-weighted and discounted—an unbiased estimate that is neither conservative nor optimistic. Risk margin: the additional buffer that another company will demand if it inherits the uncertainty; added to your best guess. This distinction ties directly into the allowance calculation in unit 3: to the best estimate you find with the chain ladder or BF is added a risk margin derived from the uncertainty you measure with Mack/bootstrap. AI helps explain these components and generate the accounting code.
Tip: Don't read a number like "Solvency rate 160 percent" on its own. Ask with what assumptions (interest, mortality, catastrophic scenario) this rate is calculated and how much stress it is based on; A ratio that looks good may be the product of poor assumptions.
Model verification: Every model including AI is verified
One of the most mature disciplines of actuarial is model validation. No model—not a GLM, not a Monte Carlo, not an AI tool—can be trusted without validation. Verification consists of several components. Backtesting: comparing the model's past predictions with actual results; If the model constantly underestimates or overestimates (bias), there is a problem. Sensitivity analysis: changing input assumptions and seeing how much the output changes; An overly sensitive model is fragile. Benchmarking: comparing the result with an alternative method or industry data. Independent review: an audit by a qualified actuary other than the person who built the model. Solvency II explicitly calls for this independence.
There is a critical point here: AI itself is a model and must be validated. The code, calculation, or suggestion that a language model produces must undergo backtesting, sentiment, and independent review, just like a GLM. Moreover, how the AI produces an answer is often not transparent (black box); This requires handling it more meticulously in terms of verification. You can use AI to structure the verification process, but you cannot delegate the verification itself to AI — that would be a conflict of interest.
Caution: Making the AI say "validate and validate my model" is like making a student read his own exam. Verification is done by an independent and competent person, through documentation. AI can only help in generating checklists and test code.
How to use AI in Solvency and verification
1) Concept summary and translation into business language:
Your role: actuarial training assistant. Explain Solvency II to an assistant general manager in 6 items: SCR, MCR, technical response (best estimate + risk margin), solvency ratio, standard formula vs internal model, ORSA. Write each item in a single sentence, in plain language. Do not make up numbers; just explain the concepts.
2) Verification checklist:
I will validate a reservation model under Solvency II. Give me a model validation checklist: - Backtesting steps - Assumptions to change for sensitivity analysis - Benchmarking options - Questions to ask for independent review Let this be a framework; I will make the decision and evaluation.
3) Backtest code:
Write a backtesting code in Python (with comments): Input: predicted response for each period and subsequently realized value. 1) Forecast - calculate the actual bias for each period. 2) Report the average deviation and its direction (is it consistently low/high). 3) Suggest a simple statistical sign test. I will interpret the result; I will provide the data.
4) Solvency rate scenario:
My equity is 320M, my SCR is 200M. Calculate my solvency ratio. Then, if the SCR increases to 250M under stress, calculate the new ratio. In both cases, comment on whether the ratio is sufficient (100% threshold). Just use the numbers I gave.
Weak prompt / Strong prompt
Weak prompt:
Calculate my capital requirement and confirm my model is correct.
Two errors: no data and no method; Also, making the AI say "confirm" invalidates the verification.
Powerful prompt:
Your role: actuarial verification assistant.Task A: Solvency ratio calculation — equity 320M, SCR 200M; find the ratio and interpret it based on the 100% threshold.Task B: Provide a checklist and backtest code draft to independently validate this booking model.Note: You will not validate the model; An independent actuary will do the verification. You just produce the framework, code and questions to ask. Don't make up numbers.
three mini cases
Case 1 — Good rate with weak assumption. One company's solvency rate appeared to be 180 percent and management was relaxed. The independent review showed that the rate was calculated assuming a very optimistic interest rate and low catastrophic scenario; With realistic assumptions, the rate dropped to 115 percent. Lesson: the odds are only as good as the assumptions underlying them. The AI quickly recalculated the impact of the assumption change; An independent actuary made the assessment.
Case 2 — Systematic deviation. A backtest of a model found that it underpredicted the provision by an average of 8 percent for three consecutive years. Individual years seemed "acceptable", but consistently deviating in the same direction was a sign of bias. Corrected model evolution factor selection. AI produced the backtest code and deviation graph; The actuary interpreted the pattern.
Case 3 — The trap of outsourcing verification to AI. An assistant told the AI to "confirm this internal model is it compliant with Solvency II"; The AI produced a fluent “is available” text. But the AI had not actually inspected the model's data or assumptions; This was a facade confirmation, not actual confirmation. When the independent actuary stepped in, two serious assumption errors were found. Lesson: verification is a human responsibility; AI cannot take it over.
Common mistakes
- Reading the solvency ratio without assumptions. A good ratio may be the product of poor assumptions; Question the assumptions below.
- Looking at individual years and missing systematic bias. Consistently small deviation in the same direction is a sign of serious bias.
- Having the verification done and approved by AI. This is a conflict of interest and appearance endorsement; Verification is done by independent human.
- Confusing best guess with risk margin. The two are separate components; The risk margin is an uncertainty buffer and is not included in the forecast.
- Using AI output as if it were real, not as an unvalidated model. AI is also a model; must undergo backtesting, sensitivity and independent control.
In summary
Solvency II regulates the financial resilience of insurance companies; Its three pillars are quantitative requirements, governance and transparency. SCR is the capital to cover a year's loss with 99.5 percent confidence, MCR is the absolute minimum; The technical provision consists of the best estimate and the risk margin. Model validation — backtesting, sentiment, benchmarking, independent review — is the discipline that every model, including AI, must go through before it can be trusted. AI produces concept briefs, code, and checklists; But the responsibility for the capital figure lies with the appointed actuary and the management, the verification belongs to the independent human, and having the AI say "approve" is not a substitute for actual verification.
Application task
Set up a solvency ratio scenario with anonymous figures (equity and SCR). Ask the AI to (a) calculate the rate and recalculate it under a stress, (b) produce a validation checklist and backtest code draft for a reservation model. Verify the ratio manually. Then consciously ask the AI to “validate my model” and critically evaluate its response: what did it actually control, what did it not control?
checklist
- [ ] Did I read the solvency ratio questioning the underlying assumptions?
- [ ] Have I looked for systematic bias as well as individual years in the backtest?
- [ ] Did I leave verification to an independent and competent human, or did I not delegate it to AI?
- [ ] Have I allocated the best estimate and risk margin correctly?
- [ ] Have I treated the AI output as a model that needs to be validated?
- [ ] Have I maintained that the final responsibility for the capital figure rests with the appointed actuary?