Insurtech is the scene of fierce competition between companies, which seek to attract clients from those competitors who have not yet adapted their businesses to new needs. Today consumers demand new practices and tools that improve their experience.
Customers shifting to insurtech companies with a higher level of modernization is very costly. Therefore, recovering them implies not only the investment necessary to make a leap in innovation but also to launch reactivation campaigns to attract customers. Transforming data into information to generate knowledge and optimize intelligent decision-making in business is a fundamental and valuable tool, and the insurtech industry is no exception.
Specifically, it allows us to measure effectiveness, efficiency, and consumer satisfaction. Data is the gateway to product and service development. In the case of insurtech, data analysis allows insurers to categorize users and identify what type of service to provide them.
Machine learning
Relying on Machine Learning techniques to accelerate the risk assessment of a potential policyholder is a key and differentiating factor. These techniques support the decisions that subscribers must make:
- Creating a risk scoring of the new hire to take precautions against a client with a high risk of suffering an accident, or to avoid fraudulent applications.
- Identifying variables of greater weight in the valuation of service with the possibility of risk, without incurring an eventual loss.
As a result, insurtech companies reduce costs and improve the effectiveness of services. In this way, they offer competitive prices in the market that, at the same time, allow compensation to be covered. Having the adequate infrastructure to capture and process information becomes vital to adapt to the current technological progress.
Impact
In the insurance sector, increased competition and the birth of insurtech generated an increase in customer churn. Onboarding new customers cost three times more than preserving current ones. Therefore, anticipating the eventual leak is vitally important. So that customer retention is not threatened by excessively high prices, the study of the data should aim in insurance to "build a bridge" between the three main objectives: pricing, marketing, and loyalty. To attract new customers and retain current ones, the sector resorted to what is called cross-selling and up-selling strategies, which favor the personalization of offers.
Insurers with purely digital businesses simplify the process of purchasing policies and claims, making it a simple, fast, and satisfying experience. If they do not want to be left behind, insurance companies with a traditional structure must adapt to these digital trends, improving the customer experience, designing processes from their perspective, and optimizing administrative processes to provide faster claims management, both for managers and intermediaries. and clients.
The challenge for insurers is to find the balance between giving the user the freedom to manage online and providing them with direction, advice, and support in real-time. This digital transformation is what will offer quality, coexistence, and trust.
Techniques
The techniques that have application in the insurtech sector are:
- In the first place, Machine Learning techniques detect behavior patterns that allow knowing the client, facilitating the offer of individualized services. We can anticipate the needs of a client and even detect situations of dissatisfaction.
- The early detection of claims is essential for the client to renew their confidence. Artificial Intelligence learns from past experiences, claims, and successful resolutions to offer a more appropriate response.
- Machine Learning tools provide training data to detect the customer's life cycle (Lifetime Value) and, from there, act accordingly: Next Best Action, Next Best Offer.
- Machine learning helps predict accident risks, solving a key aspect.
- Improvements, cost savings, and increased efficiency means a greater competitive advantage. The use of Machine Learning can offer advantages associated with a more adjusted and personalized definition of products and services.
- In addition, the knowledge of clients obtained thanks to the detection of behavior patterns provided by Machine Learning, facilitates the treatment and offer of individualized services, anticipating the needs of a client.
- Finally, the increase in the response speed and the success in the resolution of incidents have an impact on a greater number of satisfied customers.
Solution
Faced with the vertigo that a whole process of digital transformation could cause, the use of machine learning techniques is a good first step. It promises more real results and resolution of the following challenges:
- Optimal customer segmentation, according to their behavior.
- Fraud detection.
- Definition of the optimal product portfolio for each segment.
- Recommendation systems.
- Calculation of LTV (Lifetime Value).
- Prevention of casualties.
- Improvement in actuarial calculations.
- Monitoring of outsourced sales processes.
- Prevention of inquiries and claims. Acceleration in its resolution.
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