Let’s see why the use of Machine Learning in the insurance policy subscription process is a differentiating factor, and how it works.
Policy subscription process: insurance underwriter
Contracting a policy is a voluntary decision by a person to cover a certain risk. But the insurance company has the last word. If the company considers that the risk is high, it will reject the application. Let’s see how the policy subscription process works.
From the moment a potential client turns to an insurance company to buy a product, the companies start an analysis and evaluation process of the risk level of the potential taker. In this process, different technical controls are carried out that make the contracting phase long and complex. This often results in companies losing the opportunity to capture them as customers.
In the insurance sector, because of InsurTech companies, traditional companies must improve the efficiency of their policy underwriting and issuance processes. Streamlining decision making on whether or not a potential client is suitable for contracting the product.
Risk Analysis by the underwriter is one of the key and most critical stages in the contracting phase of a policy. He is responsible for the balance between the risk assumed by the company and the contract conditions offered to the potential client. For this reason, the subscriber is key to ensuring the profitability of the business while satisfying the customer.
An underwriter also controls the accident rate, reviews the conditions of loss-making risks, and supports the commercial network (resolving doubts about coverage, as it requires detailed knowledge of the insurance company’s technical standards). Thus, the underwriter is a key figure in insurance companies that demand solutions to improve decision making.
Policy underwriting process: threats and opportunities
Currently, policy underwriting processes are supported by business intelligence solutions, with poor predictive and prescriptive support for decision making. This means long and inefficient contracting processes, which have a negative impact on potential customer satisfaction.
Customer churn at big insurance companies has increased in recent years. This is due to manual contracting processes, increased competition and the emergence of Insurtech companies (with lower costs and faster processes). According to the latest survey of customer dropouts in insurance (Gain Dynamics), customer churn has increased by 1.9% in car insurance and 1.2% in home insurance. In addition, 30% of clients who renewed their policies in the last year say that they could go to another company in the next renewal. Which has forced subscribers to be more aggressive, and to analyze risk and proposals faster.
New technologies can help make the subscriber’s job easier and reduce their workload. In addition to reducing the time in the process of underwriting policies, improving customer satisfaction. Streamlining Risk Analysis in early stages of contracting would allow underwriters to focus on other non-trivial and competitive tasks such as tracking results or detecting deviations in accepted risks.
AI and Machine Learning in the insurance policy subscription process
“IA has enormous potential to improve the insurance value chain. It will help automate insurance processes to provide better service to customers. Policy issuance and claims processing can be faster and cheaper.” Michael Bruch (Global Head of Risk Consulting Liability)
Using Machine Learning in the insurance policy subscription process, to speed up decision making in the risk evaluation of a potential client is a differentiating factor. These techniques support the decisions that subscribers must make:
- Creating a risk scoring (to predict acceptance or rejection early in the process), with minimal error margin.
- Identifying the variables with the greatest weight in the decision, to recommend which actions are the most suitable to change the valuation.
That’s what we get:
- To automate a process that today is still manual.
- To standardize the evaluation criteria to apply them in each new contract (currently it depends a lot on the subscriber).
- Easily adjust the business conditions and rules, with overall results.
- To reduce the valuation time of each new contract, optimizing the process and minimizing its risks.
How can we help you at decide?
In DECIDE we help insurance companies to incorporate data intelligence through Advanced Analytics and AI techniques, to improve the efficiency of their business processes.
For more than 10 years we have been providing services of this nature to leading insurance companies, allowing them to update their business processes to the new times. If you are interested in knowing more about it, please contact us. We will be pleased to help you.