Example of household contents insurance

Added value through chatbots

Chatbots can be the fastest way for customers to obtain information. They are also anonymous, which appeals to many users. The prerequisite for this is the use of so-called intelligent chatbots, which enable a natural dialog using artificial intelligence techniques instead of creating simple, process-based dialogs as we know them from typical voice portals in call centers.

Our value contribution

Our Intelligent ChatBots for insurance is an example of how convenient these solutions can be for customers. Who knows exactly which parts are included in a household contents insurance policy and under what circumstances they are covered? The ChatBot reads the contract, saves it and is then able to provide information. Is the bicycle also insured? Is cash insured? In case of “uncertainty”, the ChatBot shows the relevant parts of the contract. Over time, the ChatBot learns more and more clauses and formulations; it is trained by experts and constantly improved.

Corporate client advisor

Information as the basis for lead generation

Information is the necessary basis for generating leads. Today, this information is hidden on the Internet. Information is sent via news channels and social networks that needs to be intercepted and processed. Technical tools such as crawlers and interfaces (APIs) that allow access to the messages help with this. The main difficulty lies in selecting the right internet sources.

However, the decisive step is to summarize these intelligently. This is how investments are determined, house or asset sales discovered or new companies identified that are relevant for lead generation. This data is structured and linked to key figures. In this way, price trends for raw materials or machines can be forecast in order to derive the right sales strategy in addition to pure lead generation.

Portal for the corporate client advisor

One step ahead of the competition

The collective terms next-best-offer (NBO), next-best-action (NBA) and next-product-to-buy (NPtB) refer to complex systems that are essentially based on recommendation engines, i.e. automated recommendations. Depending on the variety of products and turnover rate, these can generate very precise hits for automated recommendations (e.g. in electronic media trading). In the financial sector, however, these products and actions are so complex that instead of a fully automated recommendation, a machine-supported solution is the best option.

Data from external sources on the internet is combined with internal information about the customer (e.g. the customer transaction history or the processed dialogs from chatbots). This is then used to better understand the customer’s life situation and use machine learning to find suitable patterns for what the customer’s next purchase decision is most likely to be. This provides the customer advisor with a tool that supports their sales strategy through objectification. It is very valuable when the algorithm transparently shows why certain recommendations were made.