Please note: The algorithm descriptions in English have been automatically translated. Errors may have been introduced in this process. For the original descriptions, go to the Dutch version of the Algorithm Register.
Algorithm fine and transaction estimate Justice and Security
- Publication category
- Other algorithms
- Impact assessment
- Field not filled in.
- Status
- In use
General information
Theme
Begin date
Contact information
Responsible use
Goal and impact
The algorithm helps make better estimates with fewer employees. Predictions previously differed greatly from the real numbers. And it took a lot of time to check where the differences were due to. The algorithm does not use personal data. The data shows the number of fines per offence (offence code) and the place of offence (offence location). JenV checks the accuracy and completeness of the algorithm. For example, by checking whether the figures used for the number of fines match the number of fines coming in. Annually, JenV determines which version of the algorithm it uses per enforcement tool (e.g. speed cameras, route checks or standing orders) and whether they need to adapt it to new policies.
Considerations
The algorithm helps make better estimates with fewer employees. Predictions used to differ greatly from the real numbers. And it took a lot of time to check where the differences were due to.
Human intervention
There is no automatic decision-making. Employees can look at the results and make decisions based on them. For example, adjust expected spending or allocate more staff. The algorithm provides Excel files stating that it is a forecast.
Risk management
The algorithm does not use personal data. The data shows the number of fines per offence (offence code) and the place of offence (offence location). JenV checks the accuracy and completeness of the algorithm. For example, by checking whether the figures used for the number of fines match the number of fines coming in before the model is trained. Annually, JenV determines which version of the algorithm it uses per enforcement tool (e.g. speed cameras, route checks or standing orders) and whether they need to adapt it to new policies.
Legal basis
The Directorate of Financial and Economic Affairs (DFEZ) is responsible for the financial management of the entire Ministry of Justice and Security (Article 4.1d and Article 4.1f of the State FEZ Decree).
Links to legal bases
Operations
Data
Monthly reports provided by the CJIB were used, showing the number of fines issued per enforcement tool and agency over the past 5 years.
No personal data are used in this algorithm.
Technical design
A number of models are used to predict fines and transactions: The mean, last year and two time series algorithms (ETS and Arima). First, it is determined which model will be used. To determine which model to use, the different models are run over a full year for which the realisation figures are known. The model with the smallest deviation is finally chosen. To finally produce the estimate, the model finally selected is applied over the entire dataset (including the evaluation period used earlier). The result of this estimate is the so-called policy-neutral estimate. A number of further edits are then made to this, based on policy changes. In addition, a translation is made to revenues, which is done on the basis of an average fine amount. This amount is then adjusted based on established indexation.
External provider
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