Mortgage Update | Q3-2020 | Despite COVID-19 pandemic this year might become a record year for the Dutch mortgage market

By Banking, Hypotheekupdate, Mortgage Update, News

Utrecht, November 11th 2020 – Mortgage revenue in the third quarter of 2020 has grown by 26% compared to the third quarter in 2019 to 41 billion. The number of mortgages has grown by 22% to 120 thousand. Compared to the second quarter of 2020, numbers also continue to grow: mortgage revenue increases by 8% and the number of mortgages grows by 5%. The numbers rose among all groups, but it is noteworthy that new homeowners and transferors show the strongest growth. 

Download the IG&H Mortgage Update (in dutch) 

 “The COVID-19 pandemic appears to boost the Dutch mortgage market. In the second quarter of 2020, we already saw historical growth among people taking out refinancing and additional loans, and this quarter the numbers are also increasing for new homeowners and transferors” according to Joppe Smit of consulting firm IG&H. “All numbers point in the direction of new record this year with the highest mortgage revenue so far”. 

The average mortgage value grows by 2,8% compared to the previous quarter to €342.000For people taking out refinancing and additional loans, the average mortgage value increases by 3,8%. For new homeowners and transferors, the average mortgage value grows by approximately 2%. 

New homeowners and transferors show strongest growth 
Mortgage revenue of people refinancing and taking out additional loans grows again this quarter to €16 billion. Growth for this group is relatively low compared to the previous quarter with 5,2% growth, while new homeowners (+13,1%, 8 bn.) and transferors (+8,8%, 17 bn.) show stronger growth. “We are seeing a significant increase in the number of home buyers again. Now that people are forced to spend more time at home, this could be increasing the desire to move for some of them. In any case, the figures show no Corona-induced reluctance. The mortgage market benefits from this ”, says Smit 

Market share of insurers under pressure
The market share of insurers decreases by 2,5 percentage points compared to the previous quarter to 11,8%Foreign parties increase strongly (+1,9 percentage points), and the market share of the banks recovers slightly compared to decreases earlier this year (+0,8 percentage points) and reaches a market share of 56,6%. ING passes ABN AMRO in the top 10 and secures the 2nd spot. 

Over 6,200 advisors have passed the course for advisors in sustainable housing
Since the beginning of this year, IG&H reports on the progress of industry collective Duurzaam WonenTheir aim to educate at least 80% of all mortgage advisors in sustainability by the end of 2020 is now in sightTo date, 7,070 advisors have applied, an increase of 25% compared to the previous quarter. This implies that 70% of all mortgage advisors have now applied.  

Subscribe to IG&H’s Mortgage Update

Joppe Smit
Director at IG&H 
T: 06 2035 2438 

Author & data-analysis IG&H mortgage update: 
Chris van Winden ( & Annelies van Putten-Stemfoort ( 


Replatforming legacy systems with low-code to increase adaptability and lower costs

By Announcement, News, Pensions

Pensioenfonds Horeca & Catering has undergone a successful transition towards a new administrative platform. The pension fund, known for its good service and low operational costs, aims to lower costs even further while maintaining the capability to adapt to future changes.

Replacing their current legacy employer-administration system is part of Pensioenfonds Horeca & Catering’s transition towards a modern cloudbased technology within the IT landscape. In total, data of 85 thousand employers, approximately 1 million employment contracts and well over 3 years of historical data have been successfully migrated on the new platform. The platform was developed by the pension fund and IG&H, in close collaboration with international techology partners OutSystems and Microsoft. IG&H was responsible for developing and implementing the pensions administration module.

The complete process of implementation and data migration took only seven months, made possible by using OutSystems’ ‘low-code software’ and IG&H’s pension administration module. By combining the two, a major increase in implementation speed and agility was achieved. Moreover, Pensioenfonds Horeca & Catering was able to significantly decrease administrative costs. By allowing the new administration platform to fully function on the Microsoft Azure cloud, Pensioenfonds Horeca & Catering achieved maximum scalability.

Paul Braams (CEO Pensioenfonds Horeca & Catering):
“There are three reasons why we are very satisfied with the IG&H collaboration: the professionality of the people, the collegiality throughout the collaboration and the fact that our partnership goes beyond
temporarily commercial deal. Together we have assessed the future and created a joint vision, which we are working towards step by step. Through IG&H we were introduced to low code as the driving force behind developing new applications. Our first project, replacing the employer-administration is made possible by combining low code and the ‘hard’ pension administration core of IG&H. This combing together makes the project unique: low cost adaptability, allowing us to adjust to all the imminent changes within the pensions landscape, quickly and cheap.”

Frans Liem (partner IG&H):
“Pension administrators are facing the great challenge of replacing and updating their ICT-landscape within the years to come. Together with Pensioenfonds Horeca & Catering, IG&H has developed a futureproof approach by assembling the best people within both the pensions and technology platforms industry. We are pleased that Pensioenfonds Horeca & Catering embraced and successfully implemented our solution. Together with the professionals of Pensioenfonds Horeca & Catering, we were able to deliver on our promise of speed and accuracy in these 
transitions with significant impactThis is important in the context of the large changes that the Dutch pensions sector faces.

Read the interview with Paul Braams and Frans Liem about this topic in PensioenPro.

About IG&H
IG&H is a leading consulting – technology firm specialized in the retail, financial services and healthcare sector. We drive business transformations through alignment of People, Business and Technology and are committed to make a sustainable impact to society. We combine unmatched sector knowledge with digital transformation and technology capabilities, providing services in strategy, digital design and platform technologies – powered by world-class technology partners.  With more than 300 professionals in Europe IG&H is rated as Great Place to Work and committed to deliver and bring continuous innovation.  IG&H has won the Outsystems EMEA partner of the year 2020 award.

Frans Liem

When AI-driven decisions are acceptable to clients ànd Risk & Compliance

By Data science, News

Artificial Intelligence (AI) algorithms are commonly used in many sectors. Yet, many financial services companies still apply AI very limited. Especially for those decisions that could very significantly impact the business results. Uncertainty about the acceptance of its uses by internal and external stakeholders is a main reason. What is this uncertainty and what handles can help? We offer a client-centric framework for such handles and illustrate these with one of our projects. The framework is composed from the perspective of the end customer, but also helps specifically to meet internal stakeholders as well.

Potential for more value with less work
Specialists like underwriters, risk experts and insurance claim handlers spent a lot of time on repetitive assessments and decisions. When these points in the business process can be automated the specialists can focus on more challenging activities and provide the clients with much added value. Often rules-based decision models are composed first, but these have their limitations. From experience we know that decision models augmented with AI can further increase the straight-through-processing ratio and make the process more accurate and consistent.

Two parties are cause for uncertainty; clients and Risk & Compliance
Those AI models that can most contribute to business results can also potentially most impact clients. An AI model that wrongly assesses credit worthiness or insurance risk can of course cause much more harm than a model that wrongly assesses online click behavior or cross-sell opportunity. These high-stakes models are therefore very much in the attention of several internal and external stakeholders such as; regulators, risk officers, clients and client representatives.

Because of this Risk & Compliance teams often place strict demands on model validation and monitoring. The more complex and more impactful the model, the more difficult it is to meet those demands. Many organisations also do not have much experience with this. Next to that, financial services providers are uncertain whether their clients and intermediaries will accept the outcomes of these strongly differentiating, high-impact models. These uncertainties in both front office and back office result in resistance and slow down innovation.

Client acceptance as the leading perspective
The framework below provides guidance to get AI accepted by both internal and external stakeholders. It is composed from the client perspective, so it appeals more and provides more energy compared to only thinking in terms of rules and obligations.

In short it comes down to this: As a client you want to understand how a (automated) decision was reached; you want to feel it’s a decision you can live with; and you want to be able to work with it. We elaborated on these points.

1.     The outcome must be explainable
You must be told what data was actually used and how that data led to the outcome. But to be really considered as explainable the explanation of the outcome must also be perceived as ‘logical’. In other words, the explanations must align to our understanding of how the world works.
Of course, the model must also be consistent, such that outcomes and explanations for similar cases do not differ much.

2.     The explanation must be acceptable
The data used must not be perceived a violation of privacy and so too for the insights obtained from it (using a predicted possible pregnancy, or upcoming company ownership transfer, deducted from a change in transaction patterns is not appreciated by many clients).
Next to this, the model’s outcomes need to be unbiased towards vulnerable groups, or towards sensitive characteristics such as gender or ethnicity of individuals or in the composition in a labor force.

3.     With a sense of control and benefit for the client
To provide clients with a sense of satisfaction, even in the case of a negative outcome, they must experience a sense of control. This requires the possibility of human intervention in the decision process ànd that the AI model is predictably influenceable. This last requirement allows explaining the client what she can do herself to obtain a better outcome. Of course, it is important that ‘good behavior’ subsequently does indeed lead to better scores and lower interests and rates as a result.

When a decision model can realize the perceptions above, there does not need to be much uncertainty about whether clients will accept the application of AI. Naturally, the service provider must still be able to communicate all of this well enough so that clients are informed complete and in such a way that the above perceptions are indeed effectively transferred.

Killing two birds with “one” stone
Underneath the three client perceptions are quite a few requirements. However, we do have the technology and methodology to realize these. The good news is also that once you realize the three client perceptions you will be able to cover almost anything that Risk & Compliance may need to demand. Because in order to make AI models explainable and acceptable to clients and to have control over the outcomes you must apply specific methods and reach a certain level of control and transparency. With it, you can also meet the Risk & Compliance demands.

So the next time you contemplate a solution to apply AI in high-impact decisions only three questions are relevant: “Can we explain this to the client?”, “Will he/she find it acceptable?” and “Will there be a certain level of control for the client over the outcome?”.

This will reduce much uncertainty and many puzzle pieces will fall into place. The client perspective energizes most professionals more than the necessity of following difficult rules.

We are open to discussing all sorts of matters that involve data science and AI. Especially those that touches upon organisational aspects. Feel free to contact us for a cup of coffee!

Mando Rotman

Low-code in healthcare: 5+1 real life examples

By Health, News

If there is one benefit of the Covid-19 crisis, it is the growth of digital remote care. Resuming regular care in a 1.5-meter setting is simply not possible without digital applications. However, new solutions are needed quickly. These are preferably also affordable, easily adaptable and scalable without any problems. This is at odds with how we have known IT development in healthcare up to now and therefore a different approach is needed. Low-code platforms can provide a solution. They are known for being fast, cheap and flexible. This article uses five plus one examples to illustrate how low-code can make healthcare more digital. 

The advantages of remote care such as less travel, less waiting and less risk of infections have often been highlighted in recent years. Nevertheless, development has always lagged far behind expectations. This has now changed due to the Covid-19 pandemic. The use of digital applications in communication, monitoring and treatment increased rapidly, as did the demand for new applications. More and more patients and healthcare providers are opting for “at home when possible and at the healthcare provider if necessary“. 

Now more than everhealthcare does not benefit from too complex and costly IT processes, which will result in a cumbersome solution after a long time. On the contrary, applications with high ease of use are needed within weeks so patients and caregivers can use them quickly and care delivery can continue and improve. If care provision changes, rapid and controlled adaptation of care is a must. Also, to prevent us from reverting to old behaviour. 

It is striking that, in contrast to other sectors, little is developed with low-code in healthcare. While low-code is intuitive, iterative and flexible and lends itself to (patient) portals, apps or even complex back offices. Developers do not need to master a programming language, but only need to know a program where they set configurations in a graphical user environment. Low-code is therefore fast and adaptive: developers can test the (new) needs of healthcare providers and / or patients directly during development. Another advantage is that it easily integrates with existing IT systems and standards (such as HL7), so new functionalities are added to the existing systems without disrupting the current operation. Leading research firm Gartner expects that by 2024, 65% of all applications will be co-developed or managed with low-code. Well-known players are OutSystemsMendix and Betty Blocks, which already have various applications in healthcare, especially internationally. 

National Coordination Center for Patient Distribution (The Netherlands)
Shortly after the seriousness of the Covid-19 crisis in the Netherlands became clear, the National Coordination Center for Patient Distribution (LCPS) was established. The aim of LCPS is spreading the patient care workload as effectively as possible throughout the Netherlands. To perform this assignment properly, insight is required into the most up-to-date information about available beds and transport capacity. In less than two weeks, an application, the coordination platform, was developed and made operational with low-code to provide this insight into all hospitals in the Netherlands and some in Germany. The coordination platform is used to process the transport movements of patients on request by matching supply and demand. Part of this is finding the best hospital and suitable transport for each patient based on 90+ different input variables. In addition, the platform provides reports that are in the news nationwide.  

Kermit (United States)
The American Kermit developed a low-code analysis platform for medical implants such as pacemakers and insulin pumps within nine months. The application manages contracts and invoices and monitors supplier compliance. The entire process is transparent: from unpacking the material during the treatment to sending the invoice and payment to the supplier. The data-driven platform maps trends to optimize processes, provides buyers with information about fraud and prices, and provides specialists with information for treatment choice. The Kermit platform is now running in 23 hospitals, saving on average 30% of their costs for medical implants. 

Saga Healthcare (United Kingdom)
Years ago, the English Saga entered the homecare market in its own country. The big difference with other healthcare providers was that Saga focused on an agile technology platform. The IT team of 

Saga was able to deliver SACHA, a homecare planning system, within six months. The built application automates a huge amount of manual tasks so that caregivers can use this time for personal care of clients. Building with low-code was mainly of added value for Saga because the expertise was immediately embedded within its own IT department. As a result, it kept control in its own hands without having to commit to third parties. 

Medtronic (United States)
Medtronic has been one of the market leaders in medical devices such as heart implants for years. These implants are constantly collecting data from patients all over the world. It is very complex for healthcare providers to extract timely and actionable insights for the care and well-being of patients from the enormous amounts of data. Therefore, Medtronic built FocusOn in six months based on low-code, which filters 80% of the data for healthcare professionals. In addition to the fact that healthcare professionals can now deliver faster and better remote triages, the application of the low-code platform has also resulted in 50% IT budget savings. The platform makes it quite simple for new clinics to join this new technology: within 15 minutes, new customers and end users are ready to use. Since its launch in 2018, more than 335,000 triages have been performed through FocusOn, saving clinical staff time for 27 year.  

Kuwait Maternity Hospital (Kuwait)
Kuwait Maternity Hospital is one of the largest hospitals in Kuwait. The biggest problem for the hospital was the lack of insight into patient and capacity information due to the paper administration. Within twelve weeks, an external party put a Hospital Management System (HMS) live on low-code. This system offers the user a uniform patient view and provides real-time information for care managers: from the number of occupied beds and appointments to the number of operations and emergencies per day. Within a few weeks of implementation, the total registration time per patient decreased from 45 to 15 minutes. The number of errors in the patient file has also been reduced by 60 percent and communication between hospital departments has improved significantly. Due to its success, five other hospitals are now also using the system. 

National Health Service (United Kingdom)
The National Health Service (NHS) is known as the United Kingdom’s public health system. Especially for doctors with mental health problems, there is a Practitioner Health Program (PHP) within the NHS with free confidential care. The idea behind this is that doctors can return to work faster and more vital after treatment. The NHS started the program for doctors in the London area, but wanted to expand across the country in 2016. To also be able to offer the same confidential service nationwide, PHP has built a mobile app and a fully automated GP care system in seven weeks in low-code. With the app, healthcare providers can find therapists in their area and make an appointment anonymously. The app has now been used by more than 2000 doctors. 

The development of remote and connected care is complicated enough for healthcare providers. Who provides which care and when, who bears what responsibility for the quality of care and who pays for which care? Technology should therefore not be the problem. The development of low-code applications may be easier and faster, but not happens automatically. That is why we end this article with 5 tips to be part of the low-code revolution: 

1) Start small and finish big: start with the (agile) development of a working prototype in a pilot and discover the value of low-code development (proof of value);
2) By the patient, not for the patient: design continuously from the patient’s point of view and experiment with the flexibility of low-code development;
3) From doittogether to doityourself: get advice on the right platform, acquire the right low-code competencies and experience and then build them yourself;
4) Complexity is failed simplicity: work under architecture and don’t allow IT to add unnecessary complexity;
5) You go faster alone, you go further together: never develop alone, but learn from each other by working together. 

Walter Kien

This article has also been published on: ICT&health


IG&H recognised as Partner of the Year EMEA by OutSystems

By Announcement, IGH, News

Exactly 3 years ago IG&H decided that low-code technology could help our clients to make a sustainable impact in their sectors’. After we joined forces with highly experienced teams of platform technology experts in Portugal and the Netherlands, a lot has happened and thanks to smart collaboration it turned out to be a true winning combination. Today IG&H is being recognised as Partner of the Year EMEA by OutSystems in two separate categories. 1. Rainmaker: most Annual Recurring Revenue (ARR). 2. Pioneer: most new logos.

On top of that it is even greater to see smart collaboration with our client Medtronic EMEA is being recognised by an Innovation Award!

A big thank you to everyone that contributed to the pillars of this success.

Mortgage Update | Insights from Q2 2020 | Highest number of mortgages since 2008

By Banking, Mortgage Update, News

Mortgage revenue grows to new record of €38 billion, despite COVID-19 pandemic

Download the IG&H Mortgage Update (in dutch) 

Utrecht, August 6 2020 – Mortgage revenue during the second quarter of 2020 grows by 30% compared to the second quarter in 2019. The number of mortgages grows by 26% to 114 thousand. This is the highest number of mortgages since 2008. The numbers rose among all groups, but people taking out refinancing and additional loans showed the strongest growth. Their numbers rose by 64% compared to the second quarter in 2019. For the first time, the number of mortgages for people refinancing and taking out additional loans exceeds the number of mortgages for new homeowners and transferors.

“The COVID-19 pandemic is yet to negatively affected the Dutch mortgage market. We still see an increase in the number of mortgages for new homeowners and existing homeowners who transfer to a new home. The large increase in the number of mortgages for people refinancing and taking out additional loans shows that the pandemic even seems to have a temporary positive effect on the market” according to Joppe Smit of management consulting firm IG&H.

The average mortgage value continues to grow for new homeowners and transferors (+0,9%). For people taking out refinancing and additional loans, the average mortgage value decreases by 2,8% compared to the previous quarter. Collectively, this explains the decrease of the average mortgage value by 1,3% to €333.000.

People refinancing and taking out additional loans cause strongest growth in 5 years
Mortgage revenue of people refinancing and taking out additional loans grows by 64% in the second quarter of 2020 compared to the same quarter in 2019. Their mortgage revenue of €15 billion encloses 40% of the total market revenue. The number of mortgages grows as well by 64%. This is the strongest growth in 5 years for both revenue and numbers. “It seems that many people take the time to refinance, possibly in anticipation of an increase in interest, or to take out additional loans for renovation during this pandemic. That clearly has a positive effect on the Dutch mortgage market” according to Smit.

Market share Top 3 banks drops to an historical low
The market share of the top-3 banks has decreased to 45,6% this quarter. This decrease by -2,3 percentage points compared to the previous quarter brings their market share to the lowest level since the start of our measurements in 2006. Banks experience the strongest decrease of their market share among people refinancing and taking out additional loans

(-5,8 percentage points). ING experiences the strongest decrease for two consecutive quarters.

Over 4,950 advisors have passed the course for advisors in sustainable housing
Since last quarter, IG&H reports on the progress of industry collective Duurzaam Wonen. They are getting closer to achieving their aim to educate at least 80% of all mortgage advisors in sustainability by the end of 2020. To date, 5,980 advisors have applied, an increase of 21% compared to last quarter. This implies that 60% of all mortgage advisors have now applied.

We wish you great joy in reading this article and would like to invite you to respond!

Joppe Smit,
Director bij IG&H
T: 06 2035 2438

Author & data-analysis IG&H mortgage update
Annelies van Putten-Stemfoort (

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Why ethical reasoning should be an essential capability for Data Science teams and two concrete actions to kickstart your team on ethical knowledge 

By Data science, News

Wherever new technology is introduced, ethics and legislation will trail behind the applications. The field of data science cannot be called new anymore from a technical point of view, but it has not yet reached maturity in terms of ethics and legislation. As a result, the field is especially prone to make harmful ethical missteps. 

How do we prevent these missteps right now, while we wait for — or even better: work on — ethical and legislative maturity? 

I propose that the solution lies in taking responsibility as a data scientist yourself. I will give you a brief introduction on data ethics and legislation, before I reach this conclusion. Also, I will share a best-practice from my own team, which gives concrete actions to make your team ethics-ready. 

“But data and models are neutral in itself, why worry about good and bad?” 

If 2012 denoted the kickoff of the golden age of data science applications — through the crowning of data science as the ‘Sexiest job of 21st century’, 2018 might be the age of data ethics. It is the year where the whole world started forming an opinion on how data may and may not be used. 

The Cambridge Analytica goal of influencing politics clearly fell in the ‘may not’ camp. 

This scandal opened up major discussion about the ethics of data use. Multiple articles have since then discussed situations where the bad of algorithms outweighed the good. The many examples include image recognition AI erroneously denoting humans as gorillas, the chatbot Tay which became too offensive for Twitter within 24 hours and male-preferring HR algorithms (which raises the question: is data science the sexiest, or the most sexist job of the 21st century?). 

Clearly, data applications have left neutral ground. 

In addition to — or maybe caused by — attention from the public, large (governmental) organisations such as Googlethe EU and the UN now also see the importance of data ethics. Many ‘guidelines of data/AI/ML’ have been published, which can provide ethical guidance when working with data and analytics. 

It is not necessary to enter the time-consuming endeavour of reading every single one of these. A meta study on 39 different authors of guidelines shows a strong overlap in the following topics: 

1) Privacy
3) Safety and security
4) Transparency and explainability
5) Fairness and non-discrimination 

This is a good list of topics to start thinking and reading about. I highly encourage you to deeper investigate these yourselves, as this article will not explain these topics as deeply as their importance deserves. 

Legal governance, are we there yet? 

The discussion on the ethics of data is an important step in the journey towards appropriate data regulation. Ideally, laws are based on shared values, which can be found by thinking and talking about data ethics. To write legislation without prior philosophical contemplation would be like blindly pressing some numbers at a vending machine, and hoping your favourite snack comes out. 

Some first pieces of legislation aimed at the ethics of data are already in place. Think of the GDPR, which regulates data privacy in the EU. Even though this regulation is not (yet) fully capable of strictly governing privacy, it does propel privacy — and data ethics as a whole — to the center of the debate. It is not the endpoint, but an important step in the right direction. 

At this moment, we find ourselves in an in-between situation in the embedding of modern data technology in society: 

  • Technically, we are capable of many potentially worthwhile applications. 
  • Ethically, we are reaching the point we can mostly agree what is and what is not acceptable. 
  • However, legally, we are not in a place where we can suitably ensure that the harmful applications of data are prevented: most data-ethical scandals are solved in the public domain, and not yet in the legal domain. 

Responsibility currently (mostly) rests on the shoulders of Data Scientists 

So, the field of data cannot be ethically governed (yet) through legislation. I think that the most promising alternative is self-regulation by those with the most expertise in the field: data science teams themselves. 

You might argue that self-regulation brings up the problem of partiality, I do however propose it as an in-between solution for the in-between situation we find ourselves in. As soon as legislation on data use is more mature, less–but never zero–self-regulation is necessary. 

Another struggle is that many data scientists find themselves in a split between acting ethically and creating the most accurate model. By taking ethical responsibility, data scientists also receive the responsibility to resolve this tension. 

I am persuadable with the argument that the unethical alternative might be more expensive in terms of money (e.g. GDPR fines) or damage to company image. Your employer or client may be harder to convince. “How to persuade your stakeholders to use data ethically” sounds like a good topic for a future article. 

My proposal has an important consequence for data science teams: next to technical skills, they would also need knowledge on data ethics. This knowledge cannot be assumed to be present automatically, as software firm Anaconda found that just 18% of data science students say they received education on data ethics in their studies. 

Moreover, just a single person with ethical knowledge wouldn’t be enough, every practitioner of data science must have basic skill in identifying potential ethical threats of their work. Otherwise the risk for ethical accidents remains substantial. But how to reach overall ethical knowhow in your team? 

Two concrete actions towards ethical knowledge 

Within my own team, we take a two-step approach: 

1)group-wide discussion on what each finds ethically important when dealing with data and algorithms 

2)construct a group-wide accepted ethical doctrine based on this discussion 

In the first step we educate the group on the current status in data ethics in both academia and business. This includes discussing problems of data ethics in the news, explaining the most prevalent ethical frameworks, and conversation about how ethical problems may arise in daily work. This should enable each individual member to form an opinion on data ethics. 

The team-wide ethical data guidelines constructed in the second step should give our data scientists a strong grounding in identifying potential threats. The guidelines shouldn’t be constructed top down; the individual input that comes out of the group-wide discussions forms a much better basis. This way, general guidelines that represent every data scientist can be constructed. 

The doctrine will not succeed if constructed as a detailed step-by-step list. Instead, it should serve as a general guideline that helps to identify which individual cases should be further discussed. 

Precisely that should be a task of the data scientist: ensure that potentially unethical data usage will not go unnoticed. Unethical usage not only by data scientists, but by all colleagues who may use data in their work. This way, awareness for data ethics is raised, which enables companies to responsibly leverage the power of data. 

In short: start talking about data ethics
We are technically capable of life-changing data applications, however a safety net in the form of legislation is not yet in place. Data scientists walk a tightrope over a deep valley of harmful application, where overall knowledge of ethics acts as the pole that helps them balance. By initiating the proper discussion, your data science team has the tools to prevent expensive ethical missteps. 

As I argue in the article, discussion on data ethics propels the field towards maturity, such that we can arrive at a “rigorous and complex ethical examination” of data science. So, engage in discussion: be critical about this content, form an opinion, talk about it, and change your opinion often as you encounter novel information. This not only makes you a better data scientist; it makes the whole field better. 

Tom Jongen


IG&H’s contribution to the National Coordination Center for Patient Evacuation (LCPS)

By Health, News

Due to the national increase in patients with COVID-19, the workload of patient care across the Netherlands needed to be spread as effectively as possible. Not only for patients with COVID-19, but for all patients. The aim of the National Coordination Center for Patient Evacuation (LCPS) is to spread the workload and care capacities across hospitals.

LCPS is being led by Bas Leerink and Bart ter Horst. The Dutch Army offers advice and support in the design, organization and operation. They are being strengthened by experts in the field of acute care, logistics, ICT, statistics and crisis management.

Just before the peak in the number of COVID-19-patients, IG&H, together with Erasmus MC, the Ministry of Defence and other partners, coordinated the setup of LCPS with the aim to spread the workload and care capacities across hospitals.

Journalist Mark de Bruijn has recorded the setup of the LCPS and reconstructed it into an exciting documentary.



What Data Science Managers can learn from McDonalds

By Data science, Insurance, News

Insurers and intermediaries digitize their companies more and faster. This has implications for the organizational functions that support this such as the Data Science and AI teams. From our recently conducted Data Science Maturity Quick scan among Dutch Insurers we learn that nearly all companies currently organize their Data Science in the same way, centrally. However, the front runners are now about to transition to a different, hybrid, organizational model. Determining the best organizational model for the Data Science function turns out not to be simple.

How do you organize Data Science and AI as a scalable corporate function, when you can no longer keep it centrally organized and also do not want to switch to a very decentralized, hybrid model?

Three basic models
Actuary expertise and business intelligence have been part of the insurance business for a long time. But since about two decades people with the title Data Scientist started to appear. These professionals usually worked on non-risk use cases, such as in Sales, Marketing & Distribution, Fraud and Customer Service and they wanted to apply the latest, often non-linear, Machine Learning techniques (ML). The introduction of this kind of work often happened very decentralized and scattered throughout the organizations. But as the reputation and expectations of their work grew, these new professionals were often grouped together in central Centers of Competence (CoC).

Fig 1. Three basic models for organizational functions

The CoC model brings some advantages for a new data science function, when compared to the decentralized model. Especially when the company is not yet really functioning like a digital, data driven organization. Five out of six organization in our Maturity Quick Scan have organized their data scientists in a CoC model. However, at some companies, the digital transformation is getting serious and, in that case, strong centralization can result in a capacity bottleneck. Or be the cause of too big of a gap between business and data sience teams in terms of knowledge, priorities and communication.

Switching from a centrally organized model to a more hybrid model is often advised as a best-of-both-worlds solution. This should make it easier to scale up and align knowledge and application of data science closely with the day to day business, while a select number of activities and governance can remain central.

Concerns over hybrid models
Spotify developed a popular hybrid version that has become known as the Spotify model with its Squads, Tribes, Chapters and guilds1. This type of model has become popular among the digital natives and e-commerce companies in this world. But many data science managers in Dutch insurance companies have their concerns about this highly decentralized version of a hybrid model. These concerns are;

  • Data culture and scale of the AI applications may be insufficient still to maintain the needed continuous development and innovation
  • There are often still company-wide use cases yet to be developed that could be realized much more efficiently centrally
  • Next to that, IG&H has identified there is a large gap in data science maturity between corporate insurance and consumer insurance2. A more central organization model can help to narrow that gap

This is why in practice we often choose for a more central version of the hybrid model. But every hybrid (re)organization raises questions such as;

  • How do you keep the complexity of funding and governance down?
  • How do you maintain sufficient control over standards, continuous development and innovation?
  • Which activities and responsibilities do you keep central and which not?

A different perspective; a successful retail business model
Thinking about these questions from a different perspective or from the perspective of a different sector can help to find answers. We do this a lot to solve problems at IG&H. In this article we make an analogy with a good ol’, trusted, business model. In the late fifties of the 20th century fast food giant McDonalds started to conquer the world in rapid pace. The McDonalds concept (branding and methodology) was a proven success, but the explosive, profitable growth was partly made possible by the chosen business model for expansion, the franchise model.

A franchise is a type of business that is operated by an individual(s) known as a franchisee using the trademark, branding and business model of a franchisor. In this business model, there is a legal and commercial relationship between the owner of the company (the franchisor) and the individual (the franchisee). In other words, the franchisee is licensed to use the franchisor’s trade name and operating systems.

In exchange for the rights to use the franchisor’s business model — to sell the product or service and be provided with training, support and operational instructions — the franchisee pays a franchisee fee (known as a royalty) to the franchisor. The franchisee must also sign a contract (franchise agreement) agreeing to operate in accordance with the terms specified in the contract.

A franchise essentially acts as an individual branch of the franchise company.

Fig 2. Illustration of the franchise model

The franchise model offers some important benefits to a business concept that help to expand fast and durable;

  • Access to (local) capital
  • Entrepreneurship of local establishments
  • Presence close to local customers
  • Ability to facilitate centrally what locally cannot be realized in a cost-efficient way, or what is an essential part of the success formula (e.g. R&D, Production, Procurement and Marketing)

Each of these qualities can be related to lessons learned and best practices of scaling up Data Science within an organization. As the franchise principle made it possible for McDonalds to rapidly expand into the world, it can also help expand Data Science throughout the company.

Applying franchise model principles
Many insurers desire a more adaptable and entrepreneurial company culture. Ownership, client focus and flexibility are key words. The principle of local entrepreneurship in the franchise model fits very well with this. It encourages both the business responsible as well as the data scientist to work very client-focused and always with a business case to achieve.

Data Science is a broad discipline and it rapidly evolves while it is often not yet embedded well in most companies. This makes a central organizational element very valuable to ensure quality standards, continuous development and innovation. Just like the franchiser in the franchise model often facilitates certain means of production, R&D and best practices. One of the front runners in the Dutch insurance market with respect to organizing their data science is therefore transitioning to franchise-inspired organization model for their data science function.

So, franchise-inspired thinking can be valuable for Data Science organizations that want to move beyond the CoC, but not (yet) want to move to a very decentralized hybrid model like the Spotify model. However, which organizational model and specific choices are the best fit is naturally dependent on multiple variables. Among others; company structure and company culture, the digital maturity of the organization and the maturity of the data science function itself. Because the value of data science and AI are completely dependent on the quality of collaboration with the business and on broad application throughout the company it is vital to objectively assess the current situation and to strike the right balance between centralized and decentralized, with a company-specific touch.

Mando Rotman
Manager Data Science IG&H

Jan-Pieter van der Helm
Director Financial Services IG&H



Digitalisation can radically change the debt assistance and administration into a more people-oriented approach

By Banking, News

All signals are on red in the debt sector[1]. The sector is all about people in often difficult situations. More than 2,5 million Dutch households suffer from late payments[2]. About half of those households have structural debt problems. Due to the impact of COVID-19, the number of indebted households is predicted to increase significantly[3]. With digitalisation the more important focus in the debt market is inevitable. Preventing people to obtain income on alternative matters. More cost-efficient administration and focus on the root cause of debt, the household in debt. Digitalisation of the financial administration processes can let the sector undergo a metamorphosis into a person-oriented approach. The ingredients are there.

Social impact and growth potential of the sector
The impact of debt is considerable: for the debtor, creditors, and society at large. The Dutch government wants more people to get out of a hopeless debt situation and gives high priority to this[4]. A rough estimation is that the debt sector in the Netherlands costs the society € 11 billion a year (BKR, 2014)[5].

Figure 1 – Debt assistance decreases while administration by court increases significantly, the market potential is huge. 2020 and 2021 estimated avg. by Deloitte (June 2020)

What is remarkable about the sector figures is a significant increase in the number of administrations compared to debt assistance, which puts considerable extra pressure on the legal system.  Households with debts need the right support. Getting the right support is not easy according the National Ombudsman[6]. It takes an average of eleven days to get in contact with the municipality. The intake asks a lot of trust from people with debts, to share all information and their terse situation. Social and culture believes can make the step towards asking for help even harder.
The causes of debts are a combination of factors and in most times multicomplex circumstances:

  1. Environmental factors (economic situation, complexity of society, structural poverty).
  2. Conscious and unconscious behaviour (motivation, financial knowledge, and skills, but also a feeling: doing what others are doing and unconscious psychological processes).
  3. Unexpected events (life-events such as divorce, unemployment, disability, bankruptcy, etc.).
  4. Personal factors (addictions, mild intellectual disability, psychiatric problems).

What strikes is that the root causes of debt are all social economical and psychological. The current debt assistance and administration is mainly focussing on the financial administration to get control on the settlement and prevention of debt.

Sector growth demands digitalisation to get focus on personal debt causes
Working in the current debt administration sector is tough work. The work mostly consists of mail handling, communication, and financial administration. All this to relieve the ones with debts or to handle their financial administration. Until the debts are settled, someone is tied to the debt relief, and a calculated amount of income to live on each week (VTLB in Dutch). On a high level, debt assistance is characterized by three different phases:

The sector and government create great initiatives to innovate this process. Most innovations are focussed on the exchange of data, such as the data hub of the ‘Dutch association for debt assistance (NVVK)’. For municipalities and private debt assistors, digitalisation of internal processes is more vital than ever. Not only to integrate all data and to handle the foreseen increase of demand, if not to reduce throughput times for clients, improve customer service and reduce operational costs tremendously.

The experience of IG&H in the sector is that a return on investment of less than a year is many times possible. Combining sector initiatives and the current possibilities of technology can change debt assistance significantly. Based on IG&H experiences the intake of the debt process can be up to more than 60% more efficient due the several sector initiatives. A more efficient administrational intake process results in more time to understand the cause and situation of the household in debt.
Examples for the intake part of the process are:

  • The law entry of debt assistance and data exchange, which is going into force on January 1st, 2021, will give debt controllers access to all necessary personal data. Making the intake a lot easier for people in debt and will increase data quality and security. Technology to connect is highly mature technology, on the government side as well on the commercial side with solutions as the ‘Makelaarsuite’ of PinkRoccade Local Government.
  • With PSD2 and solutions as Budlr, Ockto and Buddy Payments the setup of a budget plan and allocating the financial administration can be digitised and set up automatically.
  • Early signalling of debt is trending and as of January 1st, 2021 a duty of municipalities by law. The ‘Dutch association for debt assistance, social banking, and administration (NVVK)’ developed a debt hub to exchange debt data of households. The hub has great potential to have insight into debts and to reach mutual agreements, restructuring or prioritisation.
  • CDD (Customer Due Diligence) or KYC (Know Your Customer) in the debt sector is not as mature as in the banking sector who invest fully in this lately. Aligning with banks in this process to allocated accounts faster and more secure, the improvement can be made on both sides making the client process more trustful. Digital identification and fraud prevention solutions as,,, and can improve this process easily. The above-mentioned law entry makes the use of DigiD and eID for highly reliable identification an even better solution. Digital identification is the essential start for data interaction.

The main part of the debt process can be much more efficient and as much automated with the following examples. Resulting in a game changer of the debt sector with a focus to coach and support clients on the causes and making them more self-sufficient in their personal household finances.

  • The use of low coding increases the execution, adaptability, and deployment of the debt operation. Low code as OutSystems makes it possible to adapt quickly and have a release and multi-platform application ready in short release phases.
  • Chatbots and AI can support easily in customer contact for most common questions and intelligent dashboarding towards the debt counsellor and customer. Microsoft and OutSystems have mature configurable chatbots available and they are getting better each day.
  • The debt data hub of the NVVK can assist debt mediation and rescheduling, reducing postal mailings significantly.
  • The use of AI in postal mail recognition can relieve the operations work even more. Solutions as Anntac will recognise postal mails after scanning up to 90%.
  • Bank account batching for new accounts and API transaction data for instant payments make payments better and faster for all parties. Solutions as Cashfac with the Ebury bank are there already, filling the gap traditional banks lacks.
  • Most important is an intuitive app and portal for the customer in debt. With the use of AI and a task manager the process can be less complex and faster. Increasing self-sufficiency of the client which is mainly the most important goal of debt assistance.
  • Restructuring loans are trending. Especially with the low ECB-interest a great opportunity to lower and simplify the debt at once for all debtors and creditors for parties with a banking license or partnership with such a party. Last year the amount of restructuring loans increased with 16% to 8.952[7].

The outflow is most important for a sustainable financial future. The earlier mentioned reasons of debt are different. For a large group of households’ financial stability remains difficult. Monitoring and signalling support of AI, and in second phase personal coaching, can be the needed support.
There are several examples available to improve the last part of the debt process:

  • Financial insights are the base for self-sufficiency after the debts. AI and an easy UX makes the adoption and assist of clients very supportive.
  • The early signalling process will keep an eye on new debt risks and can be made into automatic insights and signals towards the client.
  • Financial coaching should be part of a portal and app for clients. Goal is to increase the financial capabilities and decrease any stress factor on finance. Preventing clients to return into the debt processes. There are a lot of solutions on this topic like, and and can support clients very well.

Bringing the change together towards a people-oriented approach
It is only symptom control if the main part of the time within debt assistance and administration focusses on operation. There are multiple and easy to implement solutions to digitize the operation. Given that, the focus needs to be on a person-, and situation-oriented approach. The expertise of the debt assistants and administrators changes almost completely towards a people- and cause oriented expertise. In such the debt sector can be more tailormade, fitting in a multidisciplinary approach with different professionals suitable for the cause. Let us innovate and help people.

Gerwin woelders