Category Archives: Coding

Pilot Data Quality Rules

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Data Quality is receiving more and more attention within the financial sector, and deservedly so. That’s why DNB will start a pilot in September with the insurance sector to enable entities to run locally the required open source code and to evaluate Solvency 2 quantitative reports with our Data Quality Rules.

In the coming weeks we will:

With these tools you are able to assess the data quality of your Solvency 2 quantitative reports before submitting them to DNB. You can do that within your own data science environment.

We worked hard to make this as easy as possible; the only thing you need is Anaconda / Jupyter Notebooks (Python) and Git to clone our repositories from Github (all free and open source software). And of course the data you want to check. We also provide code to evaluate the XBRL instance files.

We are planning workshops to explain how to use the code and validation rules and to go through the process step by step.

Want to join or know more, please let me know (w.j.willemse at dnb.nl).

Code moved to GitHub/DNB

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It has been a bit quiet here on my side. But I have been busy moving the source code from the notebooks here to the GitHub-account of DNB. And in doing so we improved the code in many areas. Code development can now be done efficiently by using tools for Continuous Integration and Deployment. This enables everyone to work with us to explore new ideas and to test, improve and expand the code.

Let me shortly introduce our repositories (these are all based on code that I wrote for the blogs on this website).

Our insurance repositories

solvency2-data

A package for retrieving Solvency 2-data. Our idea is to provide you with one package for all Solvency 2-data retrieval.

Currently it is able to download the Risk Free Interest Term Structures from the EIOPA website, so you don’t need to search on the EIOPA website. And it implements the Smith-Wilson algorithm so you can make your own curves with different parameters.

This code is deployed as a package to the Python Package Index (a.k.a. the cheese shop), so you can install it with pip.

data-patterns

A package aimed to improve data quality of your reports. With this code you can generate and evaluate patterns, and we plan to publish validation and plausibility patterns in addition to the existing ones in the taxonomies. With this package you can evaluate your reports with these patterns.

This package is also deployed to the Python Package Index.

solvency2-data-science

A project with data science applications and tutorials on using the packages above. Currently, we have a data science tutorial using the public Solvency 2 data and a tutorial for the data-patterns package.

solvency2-nlp

Our experimental Natural Language Processing projects with Solvency 2 documents. I already published some results here with NLP (reading the Solvency 2 legislation documents and Word2Vec and Topic Modelling with SFCR documents) and we are planning to provide these and other applications in this repository.

All repositories were made from cookiecutter templates, which is a very easy way to set up your projects.

Take a look at the repositories. If you have suggestions for further improvements or ideas for new features, do not hesitate to raise an issue on the GitHub-site. In the documentation of each repository you can find more information on how to contribute.