Financial inability towards AADE

From the claim “I cannot pay” to numerical evidence

In procedures for settling or easing debts owed to the Greek tax administration (AADE), the debtor often needs to show their actual financial position. Which documents are required is set each time by the rules in force and by the specific procedure. MLC3 does not replace those rules; it organises the information so that the picture is complete, consistent and verifiable.

Note. This page is illustrative (demo). It is not legal or tax advice; for your own case, consult a lawyer or an accountant.

What usually needs to be shown

Income and costs
  • The income of every household member, fixed and variable.
  • The necessary costs of living, housing, health and energy.
  • The instalments of other debts already being paid.
Assets and liquidity
  • Property, vehicles and deposits, with their value and encumbrances.
  • What can be realised, and how quickly.
  • How cash flows develop over the coming months.

Sensitivity analysis (Tornado)

Tornado chart of the monthly repayment capacity, base €90. Each factor changes by ±15%. The full list of values is in the table below the chart.
Figure 1. Monthly repayment capacity when each factor changes by ±15%.

We start from the base estimate (€90 a month) and change one factor at a time by ±15%. The longer the bar, the more the conclusion depends on that factor. Here net income dominates: a 15% fall is enough to make the capacity negative.

Data table of Figure 1
Monthly repayment capacity (€), base €90
FactorUnfavourable −15%Favourable +15%Range
Net income−135315450
Living costs−2182184
Housing27153126
Other instalments6311754
Energy7410632
Health7610428

Monte Carlo simulation

Histogram of 10,000 scenarios of the monthly repayment capacity. Median €52, probability of no positive capacity 33%.
Figure 2. Distribution of the monthly repayment capacity over 10,000 scenarios.

Sensitivity changes one factor at a time; in reality they all change together. The Monte Carlo simulation draws values from distributions (income: normal; costs: triangular) and computes the capacity in 10,000 scenarios. The result is not one number but a probability of inability.

Data table of Figure 2
Monthly repayment capacity (€), 10,000 scenarios
MeasureValue
5th percentile−144
Median52
Mean52
95th percentile248
Probability of no positive capacity33.0%

What it means for the file

With a base estimate of €90, the file would show a “small but positive” capacity. The analysis shows something more precise: in about one scenario in three the household cannot pay anything, and the risk comes mainly from income. That is an argument based on data — and it can be checked.

Both sample reports are available as PDFs among the scientific reports.