City Treasurer AI Solution

How Cities and Municipalities Can Get More Out of Their Budgets Through AI-Driven Portfolio Optimization

Executive Summary: The problem with municipal budgets isn’t just a lack of funds—it’s also about optimal allocation

What if a city could renovate more schools, modernize more infrastructure, and achieve greater overall social benefit with the same investment budget—simply by better combining its planned projects?

Municipal budget planning is one of the most complex decision-making tasks in public administration.

City treasurers, finance directors, mayors, and city council members must balance numerous investment projects against limited financial and human resources.

Schools, daycare centers, roads, bridges, sports facilities, administrative buildings, digitalization, energy supply, climate adaptation, and social infrastructure all compete for the same budget funds.

Each of these measures may, in and of itself, be necessary, economically sound, or politically desirable.

But this raises a key question:

Is the sum of all the individual measures that have been approved also the best possible combination for the city as a whole?

The answer cannot be reliably determined through traditional project prioritization alone.

Even with just 50 binary project decisions, there are theoretically 2^50 = 1,125,899,906,842,624 possible project combinations.

That corresponds to roughly 1.126 quadrillion theoretically possible investment portfolios.

A city council can discuss 50 projects individually. However, it cannot manually compare more than one quadrillion project combinations with one another.

This is precisely where AI-supported mathematical portfolio optimization comes into play.

StratePlan combines methods from operations research with AI-based optimization approaches to systematically examine large combinatorial decision spaces.

In conjunction with a budget simulation, different financial conditions and project combinations can be compared.

The crucial question is not merely: Which projects can we afford?

But also: Which combination of our planned projects achieves the highest defined overall economic and social benefit within the available budget?

Further information on mathematical municipal budget optimization.

1. Why Municipal Budget Planning Is a Mathematical Portfolio Problem

Traditional municipal budget planning follows an established administrative and decision-making process.

Departments submit investment requests. The finance department reviews financial constraints. The administration prepares a draft budget. Committees deliberate. The council decides.

This procedure is necessary to ensure democratic legitimacy, legal requirements, and financial accountability.

However, it does not automatically answer the mathematical question of what the best combination of all investment projects is.

A city does not decide solely on individual projects.

It makes decisions regarding an entire portfolio of competing initiatives.

Each project requires different resources:

• investment capital
• Personnel and administrative capacity
• Planning and construction capacity
• Time frames for implementation
• Grants and co-financing
• Debt and Financing Flexibility
• Future operating and maintenance costs

The selection of one project can influence which other projects remain financially viable or feasible.

For example, a new sports hall may make economic and social sense.

However, if its funding displaces several urgent school modernization projects, the entire portfolio of projects must be considered.

The economic and social value of a municipal investment program does not arise solely from the evaluation of individual measures, but from their combination.

2. The Invisible Decision Space: 50 Municipal Projects Mean More Than a Quadrillion Combinations

Let’s consider an example of a medium-sized city with 50 planned investment measures.

In a simplified binary model, each project can either be selected or not selected.

With N projects, there are theoretically 2^N possible combinations.

• 10 projects: 2^10 = 1,024 combinations
• 15 projects: 2^15 = 32,768 combinations
• 20 projects: 2^20 = 1,048,576 combinations
• 30 projects: 2^30 = 1,073,741,824 combinations
• 40 projects: 2^40 = 1,099,511,627,776 combinations
• 50 projects: 2^50 = 1,125,899,906,842,624 combinations

With 50 projects, the theoretical decision space thus comprises approximately 1.126 quadrillion combinations.

Not every one of these combinations is actually permissible. Mandatory legal obligations, budget constraints, project dependencies, and available resources limit the decision space.

Nevertheless, the sheer scale illustrates why traditional budget deliberations can only consider a small subset of all possible investment portfolios.

The approved budget is a selected combination. Whether it is optimal within the defined objectives and constraints cannot be determined without a corresponding mathematical analysis.

3. Budget Simulation and Simultaneous Optimization: The Crucial Difference

Municipal financial planning and mathematical portfolio optimization serve different purposes.

A budget simulation examines how revenues, expenditures, investments, borrowing, liquidity, and follow-up costs develop under certain assumptions.

Portfolio optimization, on the other hand, examines which combination of projects achieves the highest contribution toward the objectives within defined constraints.

Combining both approaches creates a more comprehensive basis for decision-making.

Budget simulation answers:

• How will the municipal budget evolve over several years?
• What investment funds are available?
• How will changes in tax revenue affect the budget?
• How much borrowing is required?
• How will interest and principal payments evolve?
• What follow-on costs result from new investments?

Mathematical portfolio optimization answers:

• Which projects should be selected within the available budget?
• Which combination yields the highest defined total benefit?
• Which measures will displace other projects?
• How do mandatory projects affect the outcome?
• What alternatives arise under different budget constraints?
• Which combination of projects is particularly effective given the specified constraints?

Combining both approaches makes it possible not only to simulate the financial development of a budget but also to mathematically optimize different investment portfolios at the same time.

This requires that the relevant financial and project data be consolidated into a consistent model.

4. Real-world example: 50 projects, 220 million euros in investment needs, and a budget of only 81 million euros

A city has an investment budget of 81 million euros.

At the same time, there are 50 planned investment projects with a total funding requirement of 220 million euros.

The investment requirement thus exceeds the available budget by 139 million euros.

The city cannot finance all projects at the same time.

The traditional approach is to prioritize projects based on urgency, political significance, and available budget funds.

However, a ranking does not automatically answer the question of what the best combination is.

Mathematical portfolio optimization can determine which selection, within the budget limit of 81 million euros, achieves the highest defined total benefit.

In addition, mandatory projects, resource constraints, and strategic weightings can be taken into account.

What questions could the city treasurer immediately investigate?

• Which combination of the 50 projects generates the highest modeled total benefit with a budget of 81 million euros?
• Which projects would be selected if the budget were reduced to 70 million euros?
• What additional measures would be possible with a budget of 90 million euros?
• How does the selection change if certain school and infrastructure projects must be implemented?
• How does the portfolio change if greater weight is given to educational or climate impacts?
• What effects do different investment combinations have on subsequent years?

The results can be compared with the original budget plan.

This reveals which economic or social improvements are possible within the underlying model.

The city does not necessarily have to spend more money to achieve a higher modeled overall impact. Under certain circumstances, a different combination of existing investment projects may suffice.

5. What does “optimal” mean in a municipal budget?

Unlike a purely economic investment decision, a municipal budget has multiple objectives.

The city should remain financially viable, fulfill its legal obligations, and at the same time generate the greatest possible social benefit.

Relevant objectives may include:

• Budget stability
• Investment and financing flexibility
• Debt level and interest burden
• Education and childcare capacity
• Quality of transportation and infrastructure
• Housing supply
• CO2 Reduction
• Social Impact
• Location attractiveness
• Long-term operating and maintenance costs

To enable a mathematical comparison of different project combinations, the target metrics must be defined consistently.

One option is to evaluate projects using a standardized impact model.

Political leaders can then determine the weightings.

An example model could use the following weightings:

• 40% budget stability
• 20% Climate impact
• 20% Social impact
• 20% Economic development

These weightings are merely an illustrative example. The actual priorities must be determined by the respective municipality.

It is also important that the underlying impact metrics be standardized to a comparable scale before weighting.

The mathematical optimum is always the best solution within the defined goals, evaluation models, and constraints—not an objective truth that exists independently of political value judgments.

6. Mandatory Responsibilities and Legal Requirements: What Is Not Up for Negotiation

Municipal budget optimization must not treat legal obligations as if they were freely selectable investment alternatives.

Mandatory tasks, binding safety measures, and other legally required expenditures must be taken into account in the modeling.

One possible approach is to define mandatory projects in advance.

The funds required for these are factored into the financial model.

The remaining budget is then available for optimizing the discretionary projects.

Alternatively, mandatory projects can be incorporated into the optimization model as binding constraints.

This ensures that the mathematical search considers only permissible budget scenarios.

Additional constraints may include:

• Local government regulations
• Budgetary requirements of the respective state
• Contractual obligations already entered into
• Funding restrictions
• Available credit and financing flexibility
• Personnel and technical implementation capacities
• Project dependencies

A mathematically optimized budget plan must be legally permissible, financially viable, and organizationally feasible.

7. Optimizing Grants: Why Combining Projects Can Be Crucial

Grant funding plays an important role in municipal investments.

The federal government, the states, and the European Union provide funding programs for various initiatives.

The economic viability of a project can change significantly as a result of funding.

At the same time, funding is often subject to certain conditions.

For example:

• Eligible Types of Investments
• Municipal co-financing
• Deadlines and approval periods
• Restrictions on use
• Rules on Combining and Cumulative Funding
• Minimum technical or environmental requirements

Mathematical portfolio optimization can take such conditions into account, provided they are modeled as data and constraints.

This allows for the comparison of different project combinations while taking into account their funding impacts.

For example, a project with a lower individual impact can become particularly attractive if a high funding rate means it requires only a limited municipal contribution.

Conversely, a project that appears cost-effective may result in high long-term municipal follow-up costs.

The decisive factor is therefore not merely the amount of funding, but the overall economic and social contribution of the funded project combination.

8. Multi-Year Budget Planning: Why the Best Project Must Also Be Implemented at the Right Time

Municipal investment decisions often have effects spanning multiple fiscal years.

For example, building a new school may incur planning costs in the first year, construction costs in subsequent years, and ongoing operating costs after completion.

A realistic budget simulation must take these temporal relationships into account.

Relevant figures may include:

• Investment expenditures per fiscal year
• Inflows of grant funds
• Loan borrowings
• Interest and principal payments
• Personnel costs
• Operating and maintenance costs
• Project Durations
• Dependencies between activities

If a multi-period optimization model is used, project planning can also be analyzed with respect to time constraints.

This raises an important additional question:

Which projects should not only be selected, but also implemented in which fiscal years?

However, temporal optimization requires appropriate data and a model that explicitly accounts for project start dates, durations, and annual financial constraints.

9. Opportunity Costs: What Is the Cost of a Policy Priority?

Local government must make decisions that are not based solely on economic indicators.

Democratic legitimacy, societal needs, and political objectives are indispensable components of budget planning.

Mathematical optimization, however, can help make the impacts of different decisions more transparent.

Suppose a city has a calculated investment portfolio.

The city council then decides to commit to an additional project.

This changes the budget available for other initiatives.

The portfolio is optimized again.

A comparison of the two scenarios can reveal:

• Which other projects are dropped
• How the overall impact changes
• What additional follow-up costs arise
• What effects this will have on financing
• How defined impact metrics change

The difference between the modeled results can be interpreted as the economic or social opportunity cost of the additional requirement.

This assumes that the target values and evaluation criteria remain consistent.

Policymakers continue to decide on goals and priorities. The mathematics makes the consequences of different alternatives transparent.

10. Ex ante rather than ex post: Improving budget decisions before implementation

Municipal financial oversight examines, among other things, whether public funds are used lawfully, economically, and for their intended purpose.

A significant portion of this audit takes place after or during implementation.

However, the selection of investment projects is made beforehand.

This is precisely where ex ante optimization comes into play.

It examines possible investment combinations before financial commitments are made or projects are launched.

This allows different budget scenarios to be compared with one another at an early stage.

For example:

• The administration’s draft budget
• Mathematically optimized combination of projects
• Politically adjusted project combination
• Investment portfolio with a reduced budget
• Investment portfolio with additional funding

The results enable a more informed discussion of conflicting economic and social objectives.

Ex-ante optimization does not replace budgetary control. It supplements it by providing a mathematical basis for decision-making prior to capital commitment.

11. StratePlan: AI-powered mathematical portfolio optimization for municipal investments

StratePlan was developed to mathematically optimize complex investment and project portfolios.

The technology combines methods from operations research with AI-based optimization approaches.

The focus is not on the isolated evaluation of individual projects, but on the analysis of their possible combinations.

For a basic economic optimization model, only three pieces of information per project are initially required:

1. Project ID: Unique identifier for the project
2. Expenses: Planned expenditures or investments
3. Income: Expected revenue or economic returns

In addition, the available investment budget is defined.

The data can be imported, for example, from an existing Excel project list.

With “Maximize Net Contribution,” the focus is on maximizing the expected net value contribution within the defined constraints.

The portfolio ROI can also be reported as an economic metric.

For municipal impact goals that cannot be directly expressed in euros, additional evaluation parameters and a suitable impact model are required.

Advanced optimization models can take the following aspects into account, among others:

• Strategic weightings
• Mandatory projects
• Project dependencies
• Resource and capacity constraints
• Multi-year budget targets
• Time Constraints
• Risk and sustainability criteria

To effectively address these requirements, they must be technically and functionally modeled within the respective model.

StratePlan thus enables the transition from pure project prioritization to a mathematically supported selection of investment portfolios.

12. Live on City Council: Which budget issues can be analyzed mathematically?

The combination of budget simulation and portfolio optimization becomes particularly relevant when comparing different political and financial scenarios.

Scenario A: The Existing Investment Budget

The city treasurer asks:

“Which combination of our 50 projects achieves the highest defined total benefit within the available investment budget?”

The projects are modeled collectively and optimized while taking the specified constraints into account.

Scenario B: Reduced Budgetary Resources

The mayor asks:

“Which measures should we select if we have to reduce our investment budget by 15%?”

The budget constraint is adjusted, and the portfolio is recalculated.

Scenario C: Prioritizing Investments in Schools and Education

A city council member asks:

“How does our investment portfolio change if we give greater weight to educational initiatives?”

The corresponding impact parameters are adjusted, and a new combination of projects is calculated.

Scenario D: Mandatorily Include Required Measures

The finance commissioner asks:

“What are the implications if certain infrastructure measures must be implemented?”

The relevant projects are taken into account as binding constraints.

Scenario E: Additional Funding

A committee member asks:

“What additional measures could we implement if further funding is approved?”

The financing parameters are adjusted, and the portfolio is reoptimized under the new conditions.

Change budget assumptions. Recalculate project combinations. Compare financial and societal impacts. Make more informed decisions.

13. Organizational Prerequisites: How a Municipality Can Implement Mathematical Optimization

The implementation of AI-supported budget optimization is not solely a technical task.

It requires clear organizational and subject-matter involvement.

The finance department must work with the relevant departments to define which projects will be considered and what data is available.

In addition, legal and political requirements must be modeled in a transparent manner.

A possible implementation process consists of five steps.

Step 1: Consolidate investment projects

The municipality creates a consolidated overview of all relevant investment initiatives.

Existing Excel lists or other structured project data can serve as a starting point.

Step 2: Define the financial framework

The finance department defines the available budget as well as the financial constraints relevant to the respective model.

Step 3: Identify Mandatory Projects and Dependencies

Legally binding measures, commitments already made, and relevant project dependencies are taken into account.

Step 4: Define Economic and Social Goals

The municipality determines which target values should be optimized and how different impacts can be made comparable.

Step 5: Calculate Portfolios and Evaluate Them Politically

The calculated project combinations are compared with previous plans.

The results serve as a basis for decision-making by the administration, committees, and the city council.

Mathematical optimization does not replace municipal responsibilities or democratic decision-making processes. It creates an additional, transparent basis for their implementation.

14. What does AI-supported budget optimization mean for Frankfurt, Cologne, Worms, and other cities?

Municipalities differ significantly in terms of population, financial strength, investment needs, and legal frameworks.

The fundamental mathematical question, however, is comparable.

Which combination of planned investment measures achieves the highest defined total benefit within the available budget?

For large cities, even small percentage changes in economic value contribution can result in substantial absolute amounts.

With a modeled investment volume of one billion euros, one percent corresponds to 10 million euros in mathematical terms.

This does not mean, however, that portfolio optimization automatically results in a one-percent savings or a corresponding release of budget funds.

Furthermore, a higher social impact index is not necessarily equivalent to a monetary savings.

The actual potential for improvement must be calculated based on specific project and budget data.

For cities such as Frankfurt, Cologne, or Worms, for example, the following questions may arise:

• Which combination of school and infrastructure measures generates the greatest defined impact within the investment budget?
• How can municipal real estate investments be combined more effectively from an economic perspective?
• Which measures should be prioritized when budget resources are limited?
• Which projects can be meaningfully implemented together, taking into account grants and follow-up costs?
• How does the investment portfolio change under different policy priorities?

The economic leverage does not necessarily lie in additional budget funds, but possibly in a better selection and combination of the measures already planned.

15. The City Treasurer as a Strategic Portfolio Architect

The role of the municipal treasury extends far beyond mere bookkeeping.

City treasurers and finance department heads are responsible for the financial management of the municipality and play a key role in preparing strategic budget decisions.

Mathematical portfolio optimization can expand this role.

Instead of merely identifying which projects are financially feasible, the treasury department can systematically compare different investment combinations.

This opens up new possibilities:

• Transparent comparison of alternative budget portfolios
• Traceable assessment of conflicting economic objectives
• Quantification of modeled opportunity costs
• Comparison of political priorities
• Analysis of different budget constraints
• More informed preparation for budget deliberations

This does not mean that the finance department becomes a substitute for political decisions.

It gains an additional tool to make the financial and societal consequences of such decisions transparent.

The management of individual budget line items can evolve into a more data- and model-driven approach to managing the entire municipal investment portfolio.

16. Conclusion: Municipal budget planning requires more than just prioritization

Cities and municipalities face significant financial and organizational challenges.

Rising investment needs, limited budget resources, a shortage of skilled workers, and long-term infrastructure obligations are placing greater demands on municipal decision-making.

The traditional prioritization of individual projects remains important.

However, it does not automatically answer the question of which combination of all available investment alternatives achieves the highest defined overall contribution.

With 50 projects, there are theoretically more than one quadrillion possible combinations.

This decision space cannot be fully assessed through additional committee meetings or a longer list of priorities.

Mathematical portfolio optimization makes it possible to systematically examine alternative project combinations.

In conjunction with a budget simulation, financial conditions, budget changes, and strategic priorities can be compared with one another.

To this end, StratePlan combines methods from operations research with AI-based approaches.

The political decision remains with the democratically elected officials.

The mathematical calculation provides an additional basis for evaluating alternatives in a transparent manner.

The crucial question for city treasurers is therefore not merely: Which projects can we finance?

But rather: Which combination of our projects achieves the greatest overall economic and social benefit with the available budget funds?

StratePlan – Same projects. Optimal combination. Better results.

FAQ – City Treasurer AI Solution and Municipal Budget Optimization

What is a City Treasurer AI Solution?

A municipal treasurer AI solution supports municipal finance officials in analyzing and optimizing complex budget and investment decisions.

In the context of mathematical portfolio optimization, different project combinations are examined under defined budget, resource, and impact constraints.

Can AI optimize a municipal budget?

AI-supported mathematical optimization can help improve the selection of municipal investment projects.

The calculation is based on defined objectives, project data, and constraints.

Complete budget optimization also requires consideration of the relevant municipal legal and financial framework conditions.

What is the difference between budget simulation and budget optimization?

A budget simulation examines the financial implications of certain assumptions and decisions.

Budget optimization, on the other hand, seeks a combination of projects that best meets a defined target within specified constraints.

Both approaches can be combined.

Why do 50 projects result in more than one quadrillion combinations?

If each project can either be selected or not selected, there are theoretically 2^N possible combinations for N projects.

For 50 projects, there are 2^50 = 1,125,899,906,842,624 combinations.

Do all 2^N combinations have to be calculated individually?

No.

Modern mathematical optimization methods can efficiently explore large decision spaces without having to fully evaluate every theoretically possible combination individually.

Can AI objectively determine the best budget combination?

A mathematical optimum can be determined within a defined model.

However, the evaluation depends on the specified objectives, weightings, data, and constraints.

This does not replace political and societal value judgments.

How are legally mandated responsibilities taken into account?

Mandatory measures can be specified in advance or incorporated into the optimization model as hard constraints.

As a result, only project combinations that meet the relevant requirements are considered.

Can funding be included in the optimization?

Yes, provided that subsidy rates, equity contributions, earmarking requirements, and other subsidy conditions are modeled as data and constraints.

This allows for the comparison of different investment combinations while taking into account their impact on funding.

Can municipal follow-up costs be taken into account?

Yes, provided the necessary data is available and the optimization model is designed accordingly.

These include, for example, operating, personnel, energy, maintenance, and financing costs.

Can StratePlan also optimize social impacts?

Social impacts can be taken into account if appropriate impact metrics are defined and incorporated into a consistent evaluation model.

These may include, for example, educational impact, climate impact, or social impact.

What data does StratePlan need for an initial optimization?

For a basic economic optimization model, three pieces of information are initially required for each project:

• Project ID
• Expenses – planned expenditures
• Income – expected revenue or economic returns

In addition, the available investment budget must be specified.

More complex municipal models can take additional constraints and impact parameters into account.

Can the optimization account for multiple fiscal years?

Multi-year analyses are possible if the model incorporates the relevant time-series data and constraints.

These include, for example, annual investment budgets, project durations, and follow-up costs.

Does AI replace the city treasurer or the city council?

No.

Political and legal responsibility remains with the relevant municipal bodies.

Mathematical optimization supports decision-making through transparent calculations and the comparison of different scenarios.

How much could be saved?

The potential for economic improvement depends on the specific list of projects, available budget funds, constraints, and defined targets.

A general savings rate cannot be derived from this.

Optimization can highlight both economic improvements and greater social impact within an existing budget.

Is mathematical budget optimization also suitable for smaller cities?

Yes.

The decisive factor is not so much the population size as the number of competing investment projects and the complexity of the decision.

Even with relatively small project portfolios, mathematical optimization can provide additional insights.

What is the most important takeaway for city treasurers and mayors?

Prioritizing individual investment projects does not automatically lead to the best overall solution.

Mathematical portfolio optimization makes it possible to systematically examine alternative project combinations within defined budgetary and impact constraints.

The key question is: Are we managing individual investment decisions—or are we optimizing the entire municipal investment portfolio?

How much more impact can be achieved with your municipal investment budget?

Examine which combination of your planned investment projects can achieve the highest defined overall contribution within the available budget.

Compare your current project planning with mathematically optimized alternatives and determine whether a different combination of existing measures could yield greater economic or social benefits.

You can optimize up to 15 projects for free.

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