ABN AMRO Clearing partners with Starcheck to implement a structured and objective assessment process for selection and development. The collaboration spans multiple job levels, from international top talent to management and C-level positions. Through online testing, psychological questionnaires, and live assessments, the partnership ensures consistency, quality, and deep insights into talent worldwide.
IT professionals are scarce. That’s an understatement.
So, what do you choose to do? Do you want to compromise on the selection criteria? Or do you invest long-term with an improved recruitment approach, more data-driven selection, and a closer and better team?
In this case study, learn how Intermax improved its recruitment and selection processes through an integrated approach.
How do assessments contribute to the growth and development of finance professionals? This case study explores how Finance Factor and Starcheck collaborate to create sustainable matches and innovative solutions.
How do you select top international talent?
Since 2017, we have developed, maintained, and provided the global assessment center to identify top international talent for ABN AMRO Clearing Bank.
One year after onboarding, the business highly values all of these hires, and there has been no attrition among them.
Over five years ago, Saint-Gobain Construction Products B.V. adopted an in-house assessment solution from Starcheck. Alongside former HR director Rian Mourik, we reviewed its implementation a year later. We addressed key questions: What is the purpose of an in-house assessment? What are its pros and cons? What role does HR play? What is involved in the implementation process?
At Ordina, we killed two birds with one stone. We realized a link between the assessment portal and their ATS, and we developed and automated the interview briefing for hiring managers based on the results of online assessments.
As a result, the interview briefings for hiring managers became very accurate and data-driven about the most relevant discussion topics. Starcheck developed the algorithm of this expert application.
Together with game developer &Ranj, Starcheck developed a platform for serious gaming.
With this platform (DEX), students have trained their behavioral competencies in a variety of situations and games. Thanks to DEX’s psychometric attribute measurement system, students receive feedback on their practiced behavioral competencies.
Walnutt believed that assessments could be more effective, quicker, cheaper, and, in particular, more fun. That’s why they developed the serious game Walnutt Quest. With Walnutt Quest, applicants do not answer any questions but instead make decisions.
This enables potential employers to really get to know their candidates. Via the WalnutTalentTree, they see how candidates score on characteristics such as flexibility, resourcefulness, and decision-making ability. As a result, many applicants are quickly reduced to a small group of contenders.
This is what we call a game-changer in assessment. Starcheck developed this game’s psychometric attribute measurement system (PAM) and has handled the scientific validation.
This fact sheet provides an overview of the most commonly used (psychological) selection methods, both classical and modern. The figures are based on meta-analyses and dominant scientific literature.
| Method | Predictive validity (r) | Typical reliability |
|---|---|---|
| Cognitive ability (GMA test) | .51 | High (.85-.95) |
| Work test | .54 | High (inter-rater ≥.70) |
| Structured interview | .51 | Medium-high (.60-.75) |
| Unstructured interview | .18-.38 | Low-medium (.40-.55) |
| Integrity test | .41 | High (α ≥.80) |
| Conscientiousness (Big Five) | .31 | Medium-high (α ~.75-.85) |
| Job knowledge test | .48 | High (≥.80) |
| Years of service | .18 | Not applicable |
| Video/asynchronous interview (incl. AI) | .30-.40 | Good at structuring; algorithmically variable |
| Machine learning / algorithmic models | .20-.50 | Depends on dataset; generalizability limited |
| Serious games / game-based work samples | .35-.50 | High on objective metrics |
| Social media screening | .00-.20 | Low and variable |
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