How a simple statistical model can help welfare bodies understand the effect of claims on their funds
Welfare schemes are built around a simple principle: members contribute regularly into a common fund, and the fund provides financial assistance when members experience defined life events such as bereavement, childbirth, marriage, medical emergencies or retirement.
The challenge, however, is not collecting contributions.
The real challenge is answering a much more important question:
«Will the fund continue to have enough money to meet members’ welfare needs as the number and cost of claims change over time?»
This question becomes particularly important for sectorial welfare bodies with hundreds or thousands of members.
A fund may appear financially healthy today because contributions exceed current claims. But what happens if membership changes? What if claims increase? What if the average benefit rises from GH₵800 to GH₵1,000? What happens when more members approach retirement?
These are questions that should be answered before the fund experiences financial pressure.
That is the reason for developing the Welfare Scheme Feasibility and Sustainability Model.
You can try it yourself: the model is published as a free, browser-based tool at tophermdev.github.io/welfare-feasibility-calculator (source on GitHub). Enter your membership, contribution rate and benefit assumptions, and it works out the funding ratio and feasibility status as you type. The rest of this post explains what it is doing and why.
The problem with looking only at the current fund balance
A common way of assessing a welfare fund is to look at the current balance:
«We have GH₵X million in the account, therefore the fund is healthy.»
The fund balance is important, but it does not tell the whole story.
A welfare fund is a dynamic system.
Money comes in through:
- monthly member contributions;
- investment income;
- other approved sources.
Money goes out through:
- bereavement claims;
- birth and maternity claims;
- medical or emergency assistance;
- marriage benefits;
- other welfare claims;
- administrative expenses;
- reserves;
- retirement benefits.
The financial position of the fund is therefore determined by the relationship between contributions, claim frequency, claim severity and the timing of those payments.
In actuarial and insurance modelling, this distinction between the frequency of claims and the severity or size of claims is a standard way of understanding claim costs.
The same concept can be applied to a sectorial welfare fund.
A simple example: GH₵18 versus GH₵800
Consider a welfare scheme in which every member contributes:
GH₵18 per month
That gives:
GH₵216 per member per year.
Now suppose the average welfare payment is:
GH₵800 per claim.
One GH₵800 claim therefore consumes approximately:
3.7 member-years of contributions.
That observation is useful, but it is not enough.
We also need to know:
«How many claims are expected during the year?»
This is where the model becomes useful.
From “How much do we pay?” to “How often will we pay?”
Suppose a welfare body has 1,000 members.
Annual contribution income is:
1,000 × GH₵18 × 12 = GH₵216,000
If the average welfare payment is GH₵800 and the fund experiences 100 claims during the year:
100 × GH₵800 = GH₵80,000
The fund has therefore spent GH₵80,000 on claims against GH₵216,000 in contributions, before administration, reserves and other expenses.
But what if claims increase to 200?
200 × GH₵800 = GH₵160,000
The margin has now become considerably smaller.
This illustrates the central purpose of the model:
«The number of claims matters just as much as the amount paid per claim.»
Modelling each welfare event separately
One of the important design decisions in the model is not to treat all welfare claims as one single percentage.
Instead, the model separates the major welfare events.
For example:
| Benefit | Expected incidence | Average benefit |
|---|---|---|
| Birth/maternity | 4% | GH₵800 |
| Bereavement/death | 0.5% | GH₵800 |
| Medical/emergency | 8% | GH₵800 |
| Marriage | 2% | GH₵800 |
| Other welfare | 3% | GH₵800 |
These numbers are illustrative assumptions, not prescribed Ghanaian rates.
The objective is for each welfare body eventually to replace these assumptions with its own historical data.
For example, if a sectorial body has 2,000 members and recorded 70 birth claims in a year:
70 / 2,000 = 3.5%
Its observed annual birth incidence would be approximately 3.5%.
This is much more useful than simply guessing a percentage.
Building the model from historical welfare data
The long-term objective is for sectorial bodies to maintain a simple claims database.
For every year, the body should ideally record:
- number of members;
- member exposure;
- number of claims by category;
- amount paid for each category;
- average claim amount;
- largest claim;
- number of new members;
- number of members leaving;
- number approaching retirement;
- fund balance;
- contribution income;
- investment income;
- administrative expenditure.
With three to five years of historical information, the body can begin developing its own empirical assumptions.
For example:
| Year | Members | Birth claims | Death claims | Medical claims | Total claims |
|---|---|---|---|---|---|
| 2023 | 1,500 | 54 | 8 | 102 | 164 |
| 2024 | 1,620 | 58 | 7 | 119 | 184 |
| 2025 | 1,710 | 63 | 9 | 128 | 200 |
The model can then calculate claim rates rather than relying entirely on assumptions.
This also makes the system increasingly accurate as more data becomes available.
The funding ratio
One of the most useful indicators produced by the model is the funding ratio.
It compares available annual contribution income with expected annual welfare expenditure.
Funding Ratio = Annual Contribution Income ÷ Expected Annual Welfare Outgo
For example:
Annual contributions:
GH₵216,000
Expected total welfare outgo:
GH₵180,000
The funding ratio is:
216,000 / 180,000 = 1.20
This means the scheme has GH₵1.20 available for every GH₵1.00 of expected welfare outgo.
Conversely, a funding ratio below 1.00 indicates that expected annual expenditure exceeds contribution income.
The purpose of this indicator is not to provide a universal actuarial pass/fail threshold. Rather, it gives the welfare committee a simple way to see the relationship between income and expected obligations.
Why reserves matter
A welfare fund should not operate on the assumption that claims will always equal the historical average.
Real life does not work that way.
One year might have unusually few bereavements.
Another year could experience several deaths, medical emergencies or other expensive events.
This is why the model allows a welfare body to allocate part of its contribution income to reserves.
For example:
- 10% administration;
- 10% reserve allocation.
The model then asks:
«After paying expected claims, administration and reserve allocations, is the fund still generating a positive annual position?»
This provides a more realistic picture than simply comparing contributions with claims.
The importance of stress testing
Perhaps one of the most useful features of the model is the ability to examine different scenarios.
The model uses three broad scenarios:
Expected — the welfare body’s best estimate of normal experience.
Conservative — claim incidence is increased to test what happens if claims are higher than expected.
Stress — claim incidence is increased further and average benefit amounts are also increased.
This allows a welfare committee to ask questions such as:
«What happens if claims increase by 25%?»
«What happens if the average payment rises from GH₵800 to GH₵1,000?»
«What happens if membership declines?»
«What happens if several benefit categories experience higher-than-normal claims in the same year?»
These questions are much more valuable for planning than simply looking at last year’s balance.
Retirement must be treated differently
There is another important consideration.
A welfare body may provide ordinary welfare benefits today while also having a retirement obligation.
These should not automatically be mixed together.
For example, a fund could have:
Annual contributions: GH₵216,000
Annual welfare claims: GH₵100,000
On the surface, this looks healthy.
But suppose 30 members are reaching retirement and the proposed retirement benefit is GH₵20,000 each.
That creates:
30 × GH₵20,000 = GH₵600,000
of retirement payments.
Suddenly, the financial picture is very different.
This is why the model displays retirement obligations separately.
For Ghana’s social security system, age 60 is the compulsory pensionable age, and actuarial projections explicitly incorporate demographic assumptions such as mortality and retirement at age 60.
The same principle is relevant when designing a sectorial welfare model: future obligations should not be hidden by today’s cash flow.
From a calculator to a decision-support system
The first version of the tool is intentionally simple. It is live now at tophermdev.github.io/welfare-feasibility-calculator — a single page that runs entirely in your browser, with nothing to install and no data leaving your machine.
A welfare body enters:
Membership
- number of members;
- contribution per month;
- starting fund balance;
- expected membership growth.
Welfare benefits
- birth incidence;
- bereavement incidence;
- medical/emergency incidence;
- marriage incidence;
- other welfare incidence;
- average benefit for each category.
Fund management
- administration costs;
- reserve allocation;
- investment income;
- other obligations.
Retirement
- members approaching retirement;
- retirement benefit.
The system then calculates:
- annual contribution income;
- expected number of claims;
- expected claim expenditure;
- administration cost;
- reserve allocation;
- total welfare outgo;
- annual surplus or deficit;
- funding ratio;
- retirement obligation;
- projected fund balance.
The result is then expressed in a simple form:
FEASIBLE — FEASIBLE, LOW MARGIN — or NOT FEASIBLE
The declaration is not intended to replace an actuarial valuation. It is intended to give welfare committees an accessible early-warning and planning mechanism.
The bigger opportunity: learn from the fund’s own data
The real value of this approach will emerge when sectorial bodies begin feeding their actual historical claims into the model.
Imagine a central system where each welfare body can maintain:
Member population → Claims history → Claim frequency → Average severity → Fund balance → Projection
Over time, the model can answer increasingly useful questions:
- What is our normal claim rate?
- Which welfare category consumes the most money?
- Has our claim frequency been increasing?
- Is our GH₵800 benefit still affordable?
- What happens if we increase the benefit to GH₵1,000?
- How many additional members would we need to support that increase?
- What contribution would be required?
- How much should be held as a reserve?
- What happens if membership falls by 10%?
- What happens if claims increase by 20%?
These are the questions that turn financial records into management intelligence.
The ultimate model
The current model can eventually develop into a more sophisticated system built around four components:
1. Demographic model — understand the composition of the membership: age, sex where relevant, membership growth, retirement exposure, mortality, and member turnover.
2. Claim-frequency model — estimate how often each type of welfare event occurs.
3. Claim-severity model — estimate how much each event costs.
Frequency and severity are established building blocks in actuarial claims modelling.
4. Fund projection model — combine contributions, investment income and opening reserves against claims, administration, retirement benefits and other obligations over multiple years.
This moves the system from a simple annual calculator toward a multi-year collective risk model.
Why this matters to sectorial welfare bodies
Welfare schemes exist to provide security to their members.
But that security depends on the financial sustainability of the fund.
The objective of this model is therefore not to discourage legitimate welfare claims.
Quite the opposite.
It is to help welfare bodies understand:
- How many claims can the fund reasonably support?
- What happens when claim frequency changes?
- What benefit level can the fund sustain?
- What contribution level is required?
- How much should be held in reserve?
- What happens when the membership profile changes?
And perhaps most importantly:
«Can today’s welfare promise still be honoured tomorrow?»
A welfare fund should not have to wait until it is struggling to discover that its contribution structure and benefit obligations are misaligned.
With even a relatively simple statistical model, welfare committees can move from reactive financial management to evidence-based planning.
From assumptions to evidence
The most important next step is therefore not making the calculator more complicated.
It is collecting better data.
Every welfare claim is information.
Every contribution is information.
Every member who joins, leaves or reaches retirement age is information.
When that information is systematically captured, the welfare body can begin to understand its own risk profile and make decisions based on actual experience rather than intuition.
The GH₵18 contribution and GH₵800 average benefit are therefore not the conclusion of the model.
They are the starting point.
The real objective is to develop a system that allows every sectorial welfare body to answer, with evidence:
«Given our membership, our contribution rate and our historical pattern of claims, what is likely to happen to our fund?»
That is the purpose of the Welfare Scheme Feasibility and Sustainability Model.