Mathematics is an exact science that has been used to calculate premiums and risks since the inception of insurance. This was traditionally done by actuaries. However, with the rise of machine learning (ML) and artificial intelligence (AI), it is possible to use advanced mathematical formulas (algorithms) to analyze large amounts of data and discover patterns and trends. This already led to more accurate premium calculations (example the VPI box). The combination of AI and the vast amounts of data that insurers have can also be used to make underwriting and claims handling processes much more efficient and offers opportunities for personalized customer service at scale. All of this means that tomorrow's winning insurers will definitely drastically reduce their cost loading, while the customer service will actually be much better.

The harsh truth is that this will put brokers and MGAs pressure on the revenue model of brokers and MGAs . After all, brokers and MGAs a relatively expensive distribution channel if informing, advising, and managing can largely be done by algorithms for a fraction of the commission that is currently paid.

Consumers, in turn, can find the information they are looking for increasingly easily on the Internet. For example, you can very simply askChatGPT what are the 20 most important points to consider when insuring a motorhome, for example. There are already several apps on the market that provide tailored financial advice using AI, such as Parthean or Mint. Admittedly not yet suitable for linking your Dutch bank accounts.

In this rapidly changing environment, where AI is having a major impact on the insurance sector, it is brokers and MGAs crucial for brokers and MGAs to make the right strategic decisions. On the one hand, by focusing even more on the use of human qualities and, on the other hand, by learning to harness the power of AI to support their work.

The power of account managers

Human qualities such as kindness, empathy, empathy, trust and respect are difficult to replicate by AI. It has been mentioned many times in this magazine, these qualities are and will continue to be essential for building good relationships with customers. Customer comes from the French word "chalant," which means as much as "attention. After all, we all know the word "nonchalant. With personal attention, advisors can create a loyalty factor beyond the purely transactional relationship between client and AI system. Especially if you also provide proactive support so that clients feel valued and well cared for.

Advisors can also specialize and build in-depth knowledge within specific niches or product areas. By staying up to date with the latest developments, account managers can account managers their intuition account managers provide valuable insights that go far beyond what AI can deliver.

So far so good, but how can brokers and MGAs sometimes tens of thousands of customers give everyone personal attention? Hiring more advisors is not scalable and far too expensive. So how do I ensure that account managers scarce time is used account managers effectively account managers possible and that time is invested in the right customer at the right time? Smart AI tooling can help with this.

The power of AI

AI is a systems technology, like electricity and the internal combustion engine, for example. A systems technology always has a major impact on society, which cannot be foreseen in advance. For intermediaries, this means that they have to (learn to) embrace this new technology anyway in order to make their work more effective and efficient. We distinguish two main forms of AI tooling namely generative AI and specific AI.

Generative 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 best-known example is on the website openai.com: ChatGPT-3 . GPT stands for Generative Pre-trained Transformer. It is a type of neural network architecture used to generate natural-sounding language. Advisors can already make good use of GPT to generate powerful answers to frequently asked questions, generate relevant content for Web sites or, for example, quickly request a summary of lengthy legal texts. Insurance software companies such as Wegroup are experimenting with this to use GPT in customer service as well. In the US, Sixfold.ai has developed a GPT model that can automatically estimate the risks of objects to be insured and provide appropriate coverage recommendations, within the acceptance guidelines provided.

For now, generative AI has some significant drawbacks. For example, manual corrections and quality control are always necessary especially when high-quality and accurate data is required. Also, the content must be validated because it is a black box. It is very difficult to understand the exact reasoning behind the generated content. Finally, generative AI requires large amounts of training data to effectively learn and generate new data. Those huge amounts of data can be found on the Internet, but almost never within a company for its own exclusive application. There are also legal risks, see for example www.arag.nl/nieuws/ chatgpt-legal-risk.

Specific AI

Besides generative AI, we know specific AI, also known as narrow AI. It is aimed at performing a specific task efficiently with a particularly high level of expertise and accuracy. This is something that is obviously very important in insurance land and there

brokers and MGAs an expensive distribution channel’

Voor de volgende use cases kan AI-technologieVoor de volgende use cases kan AI-technologie bij intermediairs en volmachten al succesvol ingezet worden;het verhogen van polisdichtheidhet voorkomen van royementactief klantbeheer op schaal inrichtenhet effectiever inzetten van de adviseursefficiëntere acceptatie- of schadeprocessenhet verbeteren van combined ratiohetidentificeren van bleeders en feedershetmonitoren van autoportefeuilleshet verbeteren van (digitale) klantbedieningom wordt deze AI meer en meer succesvol toegepast. Narrow AI is also used in applications such as image and speech recognition, recommendation systems and autonomous vehicles.

This AI requires (only) training data specific to the task for which it is designed. Suppose you want to use AI in this way to predict acceptance (probability) for the benefit of a motor vehicle STP, then you only need historical data of accepted and unaccepted motor vehicle applications. The relevance and representativeness of the training data are critical to the performance of these AI models. We mention some applications that are already successfully deployed at Dutch intermediaries and proxy companies.

'Advisors can make good use of GPT to generate powerful answers to frequently asked questions'

Churn Prediction: Based on historical customer and policy data, machine learning models can be trained to predict the likelihood of customers canceling their policies within a certain period ( churn ). Algorithms such as logistic regression, decision trees, or neural networks are used for this purpose. Advisors can use this information in a very targeted manner. Customers with a high churn probability require immediate attention and should be proactively approached for a maintenance meeting and/or with an incentive ( defend strategy ). Customers with a low churn probability are, of course, loyal customers, and the relationship with them can be strengthened in a structural manner ( nurture strategy ). Immediate action is not necessary. In addition, the model also clarifies at a diagnostic level why the risk of customer attrition is customer attrition or low. This can be addressed strategically.

CLV Prediction : Customer Lifetime Value (CLV) is an important metric for intermediaries who want to organize customer service in a systematic and future-proof—i.e., sustainably profitable—way. Technically speaking, CLV can be calculated using a machine learning model that analyzes the same historical customer 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 factors such as customer lifetime and cross-sell and upsell potential. This enables brokers and MGAs to make brokers and MGAs right strategic decisions, logically by investing in (the acquisition of) customers with a high predicted CLV.

Customers with a high predicted CLV and a high predicted churn probability should, of course, be given the highest priority by account managers. This allows marketing and customer-focused activities to be deployed efficiently and personalized services to be offered in order to actually realize the predicted CLV. Below is an example of 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 be used to predict which additional policies or coverages are most relevant and attractive to each individual customer. This involves techniques such as collaborative filtering, which identifies patterns and similarities between customers in order to make recommendations. This allows brokers and MGAs to brokers and MGAs and continuously inform all customers about relevant coverage on a large scale. If a customer shows interest, account managers can take account managers action to advise the customer ( enlarge strategy ). Policy penetration they can work specifically to achieve higher Policy penetration . An additional advantage is that customers with more policies are generally more loyal.

Robotic process automation (RPA): RPA is a technique used to automate repetitive and time-consuming tasks. This can be done just by using smart business rules, but it becomes even more effective when combined with machine learning (ML) models. For proxies or large intermediaries, the application is mainly in STP lanes for underwriting. Acceptors must verify provided data and check it against underwriting guidelines. RPA can automate these verification processes, thereby eliminating time-consuming manual work for "bulk" products. The RPA is then also configured to report any discrepancies to the acceptors. This keeps the human in the loop and immediately makes their work much 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 questions and requests. To do so, 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 proxies:

- Automatic processing of claims: NLP algorithms can analyze the contents of claim forms to extract relevant information, such as the nature of the claim, the parties involved and the details of the incident. It can also check the claim against policy conditions. This enables claims handlers to process claims faster and more accurately, resulting in improved customer satisfaction.

• Sentiment analysis: These NLP algorithms provide brokers and MGAs with brokers and MGAs and continuous insight into how customers feel about services or products. This allows real-time patterns in customer feedback and potential causes of declining customer satisfaction to be identified. In the Microsoft Platform, sentiment analysis can now be enabled by default for both written text (e.g., emails via Outlook) and spoken text (e.g., calls via Teams).

- Chatbots: With technology such as intent recognition or named entity recognition, chatbots can understand customer intent and generate relevant responses. This reduces the workload and improves customer service. Of course, everyone knows the "bad" chatbots. Much depends on the setup. At InShared, the well-trained chatbot is able to answer more than 95 percent of the questions automatically.

AI roadmap

The good news is that AI brokers and MGAs offers brokers and MGAs various opportunities to improve customer service. However, every office has its own course and its own customer group, so unfortunately there is no one-size-fits-all solution. That is why it is wise to work on an AI roadmap, in which AI is used effectively to benefit the company's goals (KPIs). It is a good idea to first explore possible use cases within the goals and strategy of the office together with the MT. The use cases are then prioritized together in a business value versus effort quadrant. Use cases with a high expected business value that, in the opinion of AI insurance experts, can be relatively easily realized for the firm with the use of AI are the quick wins. These come first in the roadmap. More difficult to realize use cases, the major projects, are addressed afterwards. In this way, one or two use cases are selected, for which a pilot is set up in an operational environment with a limited scope. It is important that the business case can be calculated from the pilot. In other words, does the investment in AI outweigh the expected business value in line with the organizational objective? Goals must be formulated in a SMART manner.

'AI already offers advisors several opportunities to improve customer service'

It is very important to also involve employees and give substance to change management. Thereafter, the implementation should be regularly evaluated and adapted to changing needs and technological developments. Also consider ethical considerations, data security and compliance with laws and regulations when implementing AI solutions.

It is up to brokers and MGAs quickly explore the possibilities of AI. There is no need to reinvent the wheel. There are AI solutions available that can be deployed immediately. Ask software suppliers or consult AI experts for more information. By usingAI tooling, brokers and MGAs can account managers leverage brokers and MGAs power of account managers , strengthen their competitive position, improve customer service, and pave the way for a future-oriented and successful insurance business.

Dennie van den Biggelaar (co-founder OneSurance) has been an AI strategist for more than a decade and helped organizations such as Johnson&Johnson, CZ, BasicFit, Corendon, Sligro, Samsung deploy big data and AI applications.

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