The insurance industry is notoriously competitive, and companies need every advantage to minimize risk and move forward with certainty. There are many ways businesses that sell insurance products can gain the upper hand, but one of the most reliable and technologically advanced methods is known as predictive modeling. This strategy assists insurers in identifying and reaching potential markets, reducing risks and using behavior patterns to forecast what the future holds. While no predictive technique is 100% accurate, the best technology can be extremely advantageous.
Most actuaries and insurance companies use this modeling technique in one form or another, and the professionals in the industry are likely familiar with the strategy. However, do you know whether your company uses it for its best benefit? Lewis & Ellis' reliable experts do a deep dive into predictive modeling techniques, everything from what they are to how to use them correctly. So let's get into it.
Because predictive modeling has been around for such a long time, most people familiar with the insurance industry have at least a loose understanding of what it is. At its most simple, predictive modeling is the process of using patterns to determine whether those patterns will repeat themselves in future scenarios. A data scientist will use a dependent variable and an independent variable through data mining to forecast future events. Predictive modeling can also be a great way to curate an algorithm that works well for a particular insurance company.
With such a wide breadth of technology available in the modern business world, predictive modeling is an effective and efficient way to use artificial intelligence to stay ahead. Moreover, a higher level of predictive big data analytics can create an incredible predictive model of what the future may hold. For example, actuaries might use predictive modeling to understand the likelihood of car accidents, failed health or property damage depending on the product in question. This kind of data science can go a long way in the insurance industry and reduce pricing errors.
The predictive analytics technique is reliable and can help with forecasting correct product pricing. One of the most challenging things in the insurance industry is that the market shifts in reaction to external factors. Predictive modeling can help companies prepare for this and set future outcomes as well as have a reliable decision tree in place.
There are many other reasons why this technique has continued to grow in popularity according to new technology and forecasting requirements. The future of the insurance industry is intrinsically connected with predictive modeling. These tools, with an associated data set or data point, will only increase in capability, and insurance companies need to use them to their advantage.
Predictive modeling is by no means a new technique and has been revolutionizing the insurance industry for over ten years. The business analytics provided by data prediction gives insight and the ability to understand customer behavior and potential risk. Having this data is an advantage that insurance companies have been trying to capitalize on for years, and the technology has only gotten better since its inception. Here are some of the most compelling benefits of predictive modeling techniques:
For the insurance industry, risk assessment is the name of the game. It's a tricky balance of assuming a certain amount of risk without taking on too much and ultimately having to raise prices to counterbalance this mistake. Actuaries calculate the dangers of nearly every kind of insurance policy to help the company set a reliable premium. With a predictive analytics model, risk assessment and forecasting future outcomes become much more reliable.
As previously suggested, actuaries and insurance companies take special care to set insurance product rates accurately and protect the customer or business itself. Predictive analysis and customer analytics can also be a great way to quickly pay out customer claims.
Predictive modeling and the use of a forecast model doesn't only help the insurance company itself but are also beneficial for the customer. The insurance product is more likely to be priced correctly with a more accurate risk assessment. When the insurance policy premium is set using information from a predictive model, the customer receives a more positive overall experience. In addition, claims handling can be sped up and assist in reducing the chance of fraud.
Another extremely important advantage of predictive modeling techniques is the opportunity to reduce operational costs with a neural network. The technology helps boost underwriters' abilities by reducing the amount of time that is spent on the underwriting process, which can be costly. This is especially true with manual prediction analyses which leave room for human error.
According to a survey by Willis Towers Watson (WTW), insurance companies that use predictive modeling techniques have reported a 67% reduction in expenses. Not only were businesses saving money, but responders also found that they were able to see a 60% increase in sales.
Predictive modeling trends
Staying current is extremely important for any company wanting to remain competitive in the insurance industry and reduce logistic regression. So what does the future of predictive modeling techniques hold for insurance companies? The reality is that predictive tools are constantly improving and changing, which is part of what makes them reliable.
For example, most predictive modeling software takes and uses data from a variety of sources in order to be more accurate when creating a predictive model. For P&C insurance products, predictive models might pull information from:
Another area of predictive modeling that is on the rise is the usage of "what-if modeling." This is an essential part of risk assessment and management for insurance companies. Now, technology is more reliable than ever, which helps insurers, underwriters, and filings.
With the right predictive modeling tools in your pocket, you can focus more on customer loyalty, manage fraud risk, and even identify outlier claims. In general, the claims process will be seeing a transformative renaissance as the result of predictive analytics and machine learning.
Users will be able to understand their customers and potential risks better and even find new markets. In addition, with the right leadership and technology in place, insurance companies will be able o provide a more personalized experience than ever before. According to WTW, more than "40% (of insurance companies) are working toward ways to improve organizational understanding of risk and apply this in risk models that support decision making." This is why using the most updated technology is so important.
With a strengthened technological infrastructure in place, insurance companies should focus on micro-improvements to prove their value to the customer.
For any business that hopes to thrive in the insurance industry, in most cases, external assistance is necessary to remain competitive. This is where actuaries can come in and lend their expertise, a reliable predictive model, data mining and the integration of new data. In addition, the latest predictive modeling software can give an insurance company an advantage over other companies in the industry.
For example, Lewis & Ellis combines industry experience, creative expertise and the best technology available. No two insurance companies are the same, and unique problems require unique solutions that predictive modeling techniques can help acquire. Our actuarial firm operates with strict adherence to ethical standards and provides timely solutions.
Are you ready to make predictive modeling techniques work for you? Reach out today to Lewis & Ellis to learn more and get started.