Nowadays, insurers mainly face with 4 significant risk elements:
- Ineffective risk assessment.
- Poor diversification strategy.
- Inefficient performance in core insurance practices such as claims processing and fraud detection.
- Digitalization challenges.
Poor management of these risks resulted in zero or even negative net economic profit for the majority of insurance firms. To be a profitable company, insurers must use technical tools and employ certain strategies. This article will cover them.
1. Implement the most precise underwriting possible
Insurance is the exchange of money for the risk of someone else. If insurers evaluate someone else’s risk incorrectly, they may face bankruptcy. Therefore, precise underwriting or risk assessment is essential for insurance companies to minimize risk.
The problem is that; underwriters have a tradeoff between risk minimizing and market share. Premium price is determined more or less by the value established at the end of the underwriting process. Furthermore, because the best price is the most important factor for insurance clients, underwriters cannot use wide confidence intervals in order to reduce risk exposure as this would increase the premium price.
For a while, the bulk of insurance businesses generated roughly zero economic profit due to this tradeoff (see Figure 1). Underwriters must employ contemporary technology solutions to automate the underwriting process and boost underwriting efficiency in order to become one of the outliers.
Figure 1: Distribution of economic profits of insurance companies.
Effective underwriting is directly related to the amount of high-quality data used and computing power available. As a result, insurers should use the following instruments:
- AI/ML models: AI/ML models discover links between parameter values (worth of automobile insured, client driving history, full breaks per 100 mile, etc.) and risk more precisely. They also aid in the automation of underwriting. Unlike human underwriters, they can work around the clock and increase efficiency.
- Advanced analytics: Find the correlation between parameter values and unwanted incidents.
- Natural language processing: Help extraction of data from written text that is commonly used by insurers.
- IoT: Insured entities are more visible thanks to telematics. A smart car, for example, can provide information about how you drive. With more data available, advanced analytics can perform more exact computations.
- Application programing interfaces: APIs are software intermediaries that help the transfer of data from IoT devices to AI/Ml models.
- Blockchain: It encrypts the data. Therefore, blockchain can be used to transfer sensitive data that might affect underwriting of the insurers.
Virtual i Technologies’ cloud-based [VRS]™ Virtual Risk Space platform aids underwriters and reinsurers. Insurers can improve their risk rating and underwriting skills by using advanced analytics of the platform. Virtual i Technologies also offers a risk engineering solution, in which experts examine commercial facilities digitally or physically to conduct inspections that meet international reinsurance standards. Figure 2 shows the solution of Virtual i Technologies for the insurers.
Figure 2: Solutions of [VRS]™ Virtual Risk Space platform.
2. Create a portfolio that minimizes your risk
Insuring entities with the same risk characteristics could make insurance companies vulnerable. Consider a climate insurance company that sells policies to farmers to help them mitigate the effects of extreme weather conditions. If this company builds a portfolio by just insuring corn farmers in Iowa, a flood in Iowa could lead to bankruptcy due to an overwhelming number of claims filed in a short period of time.
As a result, professionals employ mean-variance analysis to combine entities with diverse risk characteristics to produce a portfolio with the lowest risk. For example, a street vendor might invest his/her money in umbrellas and ice cream machines that can be used in both rainy and sunny conditions.
AI/ML models are effective tools to perform mean-variance analysis and provide quick and precise results for the insurers.
Reinsuring is another diversification method that insurers should consider. In exchange for money, such policies shift a portion of the risk to another insurer. As a result, they help insurance firms manage risk.
3. Implement claims reserve policy
In many countries, including the United States, insurance companies are required to set up a statutory reserve. This fund meets the liquidity needs of insurance firms during periods of high claim volume.
In the US for example, insurers’ short-term obligations tend to rise after hurricane season. Statutory reserve regulations protect insurance companies financially in such occasions and help them to keep agreements with insureds.
However, a statutory reserve regulation may not always be enough to prevent an insurance firm from insolvency. To be protected in such scenarios, insurers should set up a claims reserve for future possible claims such as:
- Reported but not settled claims: An insured person may notify his or her insurance carrier about a car accident in which they were involved. However, the actual cost of damage may not yet be known. Nevertheless, the insurance company knows it will face financial liability soon. To predict it effectively, insurers need claim adjusters.
- Incurred but not reported claims: Some incidents might not be reported to insurance companies yet but insurers are aware that they will be reported at some point. Consider a workplace where workers are exposed to hazardous chemicals. If this company uses a workers’ compensation business insurance coverage, the insurance company knows that one day it will be likely to have a claim since your customers might get sued by one of the toxicated employees.
Insurers should examine the sufficiency of statutory reserve and set aside extra funds as a claims reserve if necessary by analyzing historical claim data and projecting and evaluating reported but not resolved and incurred but not reported claims.
4. Improve other core insurance practices
Underwriting, claims processing, and fraud detection are the three pillars that enable insurance. Improvements in any of these procedures allow insurance companies to increase their price margins by lowering costs or increasing customer retention.
Almost 90% of insureds think their retention rate is influenced by their satisfaction with previous claims processing. As a result, insurance companies must develop a practical claims processing strategy.
Though many people still prefer human touch for claims processing, lots of technologies can improve specific steps of claims processing as Figure 3 represents.
Figure 3: Technologies that enhance specific steps of claims processing.
To learn more about claims processing you can check our Top 7 Technologies that Improve Claims Processing article.
According to the FBI, insurance fraud costs more than $40 billion in the United States alone, excluding health insurance. As a result, fraud becomes one of the risk factors for insurers.
The delay between submitting a first notice of loss (FNOL) and the actual timing of the incident is advantageous to fraudsters to manipulate data. Thus, solutions that expedite the FNOL procedure also safeguard insurance companies against fraud costs.
- Telematics: Let insurers get notified about the loss immediately.
- Video calls: Let insurers examine the loss immediately.
- Chatbots: Assists the submission of FNOL.
5. Collaborate with Insurtechs
We have discussed various technologies that aid insurers’ risk management capacities. According to McKinsey, one of the main differences between profitable and nonprofitable insurance companies is effectively using at least some of those technologies.
Only a small percentage of insurance companies, however, have the capabilities to deploy these technologies as internal models. To keep solvent in that difficult industry, most of them must collaborate with insurtechs who allow them to use their cloud computing platforms on a “as a service” basis.
The article was originally published in the AI Multiple website and written by Görkem Gençer on May 11 ,2022.