Round II

Interactive session 1

Unravelling data quality demands: How to keep it simple?
By Edward Roozenburg & Amba Zeggen (Probability & Partners)

Throughout the workshop, we explore several problems that pension funds may encounter during the implementation of the data quality framework of the Pension Federation. Funds encounter various dilemmas or issues that demand practical solutions. For instance, how does a pension fund effectively justify and validate the Materiality Threshold Amount (MTA)? How do the selected threshold values ​​for the MTA align with existing thresholds, for example, corrections policy? And how do you leverage existing data quality controls and processes to comply with the framework of the Pension Federation? The workshop will outline these challenges faced by funds. Participants are invited to discuss the presented dilemmas in small groups, brainstorm potential solutions, and share their findings with other workshop participants.

Interactive session 2

Outlier detection in pension asset data.
By Iris Nonneman (DNB)

Within financial reporting, outliers in data are often due to data quality problems. However, some data anomalies are real, and thus very informative. With the increasing granularity of data, using rule-based controls is not sufficient to verify the data. Therefore, we deploy an outlier detection approach using machine learning techniques to be able to detect errors in the data. In this break-out session, you will learn all about this technical model and the added value for actuaries.

Interactive session 3

The relationship between Data Quality and Artificial Intelligence In the light of the new pension scheme in the Netherlands.
By Aron Jeurninck (AethiQs)

In the interactive session, Aron will speak about the relationship between data quality and the use of AI applications in the pension sector, now and in the future. Moreover, he will show how AI applications can help to get relevant information from large amounts of data, among others from the data quality guidelines about the new pension law.

Interactive session 4

Bridging the Data Quality Gap: Integrating Traditional and Modern Techniques for a Seamless Pension Transition.
By Frans Kuys & Marino san Lorenzo & Bence Zaupper (Finalyse)

Join us as we explore how combining traditional and modern data techniques is crucial for ensuring high data quality in the transition to the new pension system in the Netherlands. We will explain how AI and machine learning methods such as data profiling, anomaly detection and automated monitoring can enhance the quality of pension data and briefly look at lessons learned in other markets.

Interactive session 5
(open to students)

Why are the data quality requirements important?
By Hinke Galle-Visser & Rene van Pul (Deloitte)

We will talk about the importance of data quality, requirements from the framework, how to gain insight into data quality and how to solve data issues using (advanced) data analysis techniques. All this is also from an actuarial perspective.