AI: Blessing or Concern for Intermediaries?

In the innovation special of VVP, the leading platform for financial advisors in the Netherlans, this article by colleague Dennie van den Biggelaar was published this month. The article clearly highlights the difference between the two forms of AI, namely generative AI (such as the hyped Chat-GPT) and specific AI.

Specific AI, in particular, already provides intermediaries with concrete tools to use the scarce time of advisors much more effectively. Drawing from his 10 years of experience in data science and AI, Dennie also describes how to start with AI in your company with minimal downside risk.

With the rapid rise of AI, the insurance sector is on the brink of a revolution. This revolution affects insurers but also has significant implications for intermediaries. Will AI replace intermediaries, and how can intermediaries remain relevant in this rapidly changing environment?

Mathematics is an exact science that has been used in insurance since its inception to calculate premiums and risks. Traditionally, actuaries performed these calculations. However, the emergence of machine learning (ML) and artificial intelligence (AI) enables the analysis of vast amounts of data using sophisticated mathematical formulas (algorithms) to discover patterns and trends. This has already led to more accurate premium calculations (e.g., the VPI-box example). The combination of AI and the massive amounts of data that insurers possess can also be utilized to significantly enhance the efficiency of acceptance and claims handling processes and provide opportunities for personalized customer service at scale. Consequently, the winning insurers of tomorrow will drastically reduce their cost loadings while significantly improving customer service.

However, this advancement puts further pressure on the intermediary’s business model. The intermediary becomes a relatively expensive distribution channel if informing, advising, and managing can largely be automated through algorithms at a fraction of the current commission.

Furthermore, consumers can now easily find the information they seek on the internet. For example, they can simply ask chatGPT about the 20 most important points to consider when insuring a camper. AI-driven financial advisory apps like Parthean or Mint are already available on the market, though currently not yet suitable for linking to Dutch bank accounts.

In this rapidly changing environment where AI has a significant impact on the insurance sector, it is crucial for intermediaries to make the right strategic decisions. On one hand, they should focus even more on leveraging human qualities, and on the other hand, they should learn to harness the power of AI to support their work.

The Power of the Advisor

Human qualities such as kindness, empathy, understanding, trust, and respect are difficult to replicate through AI. As mentioned before, these qualities are and will remain essential for building strong relationships with clients. “Customer” comes from the French word “chalant,” meaning “attention.” Personal attention allows advisors to create a loyalty factor that goes beyond the purely transactional relationship between the client and AI systems. This is especially true if advisors provide proactive support, making customers feel valued and well taken care of.

Advisors can also specialize and build in-depth knowledge within specific niches or product areas. By staying up-to-date with the latest developments, advisors can provide valuable insights based on their intuition, going beyond what AI can currently offer.

However, how can intermediaries with tens of thousands of clients provide personal attention to everyone? Hiring more advisors is not scalable and too expensive. The challenge is to ensure that the scarce time of the advisor is effectively utilized and invested in the right client at the right time. Smart AI tools can help with this, which we will explore further.

The Power of AI

AI is a system technology, much like electricity and the combustion engine. System technologies always have a significant impact on society that cannot be predicted in advance. For intermediaries, this means that they must embrace this new technology to make their work more effective and efficient. There are two main types of AI tooling: generative AI and specific AI.


Generative AI refers to the use of algorithms capable of autonomously generating new content, such as texts, images, or sounds, based on existing data. The most well-known example is chatGPT-3, available at GPT stands for “Generative Pre-trained Transformer,” a type of neural network architecture used for generating natural-sounding language. Advisors can already use GPT effectively to generate powerful answers to frequently asked questions, create relevant website content, or quickly obtain summaries of lengthy legal texts. Insurtech companies like Wegroup are experimenting with using GPT in customer service. In the US, has developed a GPT model that can automatically assess risks for insured objects and provide appropriate coverage advice within acceptance guidelines.

Generative AI currently has some important drawbacks. Manual corrections and quality control are necessary, especially when high-quality and accurate data is required. Additionally, content generated by generative AI needs validation, as it operates as a “black box,” making it challenging to understand the exact reasoning behind the generated content. Lastly, generative AI requires large amounts of training data to effectively learn and generate new information. While enormous amounts of data are available on the internet, it is rarely available exclusively for a company’s proprietary use. There are also legal risks associated with generative AI, as discussed on


Apart from generative AI, there is specific AI, also known as narrow AI. It is designed to efficiently perform a specific task with a high level of expertise and accuracy. This is especially important in the insurance industry, where specific AI is increasingly applied. Narrow AI is also used in applications such as image and speech recognition, recommendation systems, and autonomous vehicles.

Specific AI requires training data that is specific to the task it is designed for. For instance, if you want to use AI to predict the acceptance probability of a specific motor vehicle, you only need historical data of accepted and rejected motor vehicle applications. The relevance and representativeness of the training data are crucial for the performance of these AI models. Here are some successful applications of specific AI already used by Dutch intermediaries and underwriting companies:

Churn Prediction: Machine learning models can be trained based on historical customer and policy data to predict the likelihood of customers canceling their policies within a certain period (churn). Algorithms like logistic regression, decision trees, or neural networks are used for this purpose. Advisors can use this information effectively. Customers with a high churn probability require immediate attention and proactive approaches, such as maintenance conversations or incentives (defend strategy). On the other hand, customers with a low churn probability are loyal, and their relationship can be strengthened through consistent nurturing (nurture strategy). Immediate action is not necessary. Additionally, the model provides insights into why the churn probability is high or low, allowing strategic actions to be taken.

Customer Lifetime Value (CLV) Prediction: CLV is a crucial measure for intermediaries aiming to establish systematic and sustainable profitability in customer service. Technically, CLV can be calculated using a machine learning model that analyzes historical customer and policy data. By combining this with advanced algorithms such as regression analysis or survival analysis, the model can predict the future value in Euros for each customer, taking into account customer lifetime and cross-selling potential. With this information, intermediaries can make the right strategic decisions, specifically investing in (acquiring) customers with high predicted CLV.

Customers with both a high predicted CLV and a high predicted churn probability should be a priority for advisors. This enables efficient marketing and customer-focused activities, offering personalized services to realize the predicted CLV. Below is an example of a simplified AI-driven customer segmentation based on churn and CLV predictions.

Next Best Policy Recommenders: By analyzing customer profiles, policy data, and external data sources, machine learning models can predict which additional policies or coverage options are most relevant and attractive to each individual customer. Techniques like collaborative filtering are used to discover patterns and similarities between customers, making personalized recommendations. This way, intermediaries can continuously inform all customers about relevant coverages. If a customer shows interest, advisors can provide personalized advice (enlarge strategy), thereby aiming to increase the density of policies. Additionally, customers with more policies tend to be more loyal.

Robotic Process Automation (RPA): RPA is a technique used to automate repetitive and time-consuming tasks. While it can be achieved using smart business rules, it becomes even more effective when combined with machine learning (ML) models. In the case of underwriting agencies or large intermediaries, RPA can be used in straight-through processing (STP) for acceptance. Acceptors must verify and assess submitted data against acceptance guidelines. RPA can automatically perform these verification processes, eliminating the manual work for “bulk products.” RPA can also be configured to report any discrepancies to the acceptors, keeping them in the loop and making their work more interesting.

Natural Language Processing (NLP): NLP algorithms can understand and process natural language and are applied in communication channels to provide quick and personalized responses to customer queries and requests. Techniques such as text classification, entity extraction, and sentiment analysis are used to understand the structure and meaning of textual data.


Some relevant applications for intermediaries and underwriting companies include:

  • Automatic Claims Processing: NLP algorithms can analyze claim forms to extract relevant information, such as the nature of the claim, the involved parties, and incident details. Claims can also be checked against policy conditions. This enables claims handlers to process claims faster and more accurately, leading to improved customer satisfaction.
  • Sentiment Analysis: Using NLP algorithms, intermediaries can gain continuous insights into customer sentiments regarding services or products. This helps identify real-time patterns in customer feedback and potential reasons for a decline in customer satisfaction. The Microsoft Platform already offers sentiment analysis for written text (e.g., emails via Outlook) and spoken text (e.g., calls via Teams).
  • Chatbots: By using techniques like intent recognition or named entity recognition, chatbots can understand customer intentions and provide relevant answers. This reduces the workload and enhances customer service. Everyone knows poorly designed chatbots; the success depends on the setup. Inshared’s well-trained chatbot, for example, is capable of automatically answering over 95% of questions.

Creating an AI Roadmap

The good news is that AI already offers various possibilities for intermediaries to improve customer service. However, each office has its own direction and customer group, so there is no one-size-fits-all solution. Therefore, it is wise to develop an AI roadmap, effectively leveraging AI to achieve the company’s goals (KPIs).

Start with a joint exploration with the management team (MT) of potential use cases aligned with the office’s goals and strategy. Then, prioritize these use cases together in a business value versus effort quadrant. Use cases with high expected business value that can be relatively easily realized with the help of AI according to AI insurance experts are considered “quick wins” and should be prioritized. More challenging use cases, or “major projects,” come next.

For intermediaries and underwriting companies, the following use cases can already be successfully implemented using AI technology:

  • Increasing policy density
  • Preventing policy cancellations
  • Implementing active customer management at scale
  • Enhancing advisor efficiency
  • Streamlining acceptance or claims processes
  • Improving combined ratios
  • Identifying opportunities and risks in the customer portfolio
  • Monitoring auto portfolios
  • Enhancing (digital) customer service

Select one or two use cases, and set up a pilot in an operational environment with limited scope. It is essential to calculate the business case from the pilot—evaluating whether the investment in AI outweighs the expected business value in line with organizational objectives. The goals should be formulated in a SMART manner.

It is crucial to involve employees and focus on change management. Regularly evaluate the implementation and adapt it to changing needs and technological advancements. Also, consider ethical considerations, data security, and compliance with laws and regulations when implementing AI solutions.

The ball is in the court of intermediaries to explore the possibilities of AI quickly. There is no need to reinvent the wheel, as AI solutions are readily available and can be deployed immediately. Consult software providers or seek advice from AI experts. By leveraging AI tooling, intermediaries can further empower their advisors, strengthen their competitive position, improve customer service, and pave the way for a forward-thinking and successful insurance business.

Dennie van den Biggelaar (co-founder OneSurance) has over 10 years of experience as an AI strategist and has assisted organizations such as Johnson & Johnson, CZ, BasicFit, Corendon, Sligro, and Samsung in implementing big data & AI applications.



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