Prediction is a key component of successful insurance companies. The certainty of an outcome enables insurance firms to offer fair rates, generate value, and develop personalized ratings for their customers and stakeholders alike. It’s, therefore, no news that venture capitalists consider the insurance industry to be ripe for disruption.
An insurance company that uses machine learning software stands to benefit immensely. In this article, we’ll go through five ways machine learning software can impact insurance.
1. Improve customer experience.
Customer satisfaction is vital in any business, and artificial intelligence makes it a lot more attainable in the insurance industry. In the past, shopping for a homeowner insurance policy could be an exhausting process, especially when concerning homeowner insurance quotes. The customer would first have to visit different homeowner insurance companies or talk to their agents and provide tons of details about their home, family, and schedule visits.
To make matters worse, if you want to compare prices to know the best rate, you would have to repeat the same process over again. However, machine learning software makes the sending of home insurance compare quotes easier and in as little as 60 seconds.
2. Make workflow more efficient.
Insurance companies have a lot on their hands when processing claims and responding to customer queries. However, machine learning improves this process through the use of chatbots and AI assistants. This way, an insurance agency can focus on more demanding claims, thereby saving time. Furthermore, the data management and data analysis efforts of good software help insurance companies monitor market trends. They can also identify opportunities and understand market analytics and business risks better.
For instance, when calculating the coverage limit for a personal belonging like jewelry or property, insurance companies tend to rely on historical data like a good credit score, property damage history, earthquakes, or the history of natural disasters within the area. However, this data isn’t always accurate. Machine learning solutions can provide tools and methods that support accurate calculation and classification of risks.
3. Improve direct customer interaction.
For customer experience, machine learning utilizes deep learning techniques to gain insight into your customer’s patterns. It uses the data it collects to create personalized services. Advertisers find this useful, as they can create customized adverts that are more likely to be helpful to the customer.
In addition, it costs insurance companies a fortune to maintain claims personnel, and the task is time-consuming. However, AI can help reduce hiring costs by automating claims and payouts. For instance, in the event of a claim after disaster strikes in the form of a flood and a customer experiences property damage, they can easily make a flood insurance claim on their own by chatting with a bot.
4. Improve customer lifetime value prediction.
Customer Lifetime Value (CLV) represents how much value a customer brings to a company. It’s derived from the difference between the future income gained and expenses incurred to maintain a policyholder. Therefore, CLV allows companies to predict a customer’s potential profitability and forecast retention.
With machine learning tools, insurance companies can predict their customers and decide what type of insurance coverage will best fit them. The coverage options will depend on several factors like age in health or life insurance, replacement cost coverage of an item, and financial strength.
5. Help with fraud detection.
The U.S. loses about $40 billion yearly from insurance fraud, and this continues to be a challenge to many firms as it affects their profitability. This is why using a machine learning algorithm is an effective way to detect and prevent fraud. Insurance companies can easily spot anomalies in claims and identify misleading information that fraudulent customers use to gain lower premiums or bigger claim payouts.
