For years the insurance industry relied primarily on people, namely underwriters, to evaluate and approve insurance policies. However, the industry is moving from being policy-centric to more people-centric. You may be wondering what’s behind the change. The short answer is technology. But the deeper explanation is a combination of artificial intelligence (AI), machine learning (ML), big data, and predictive analytics.
Fallout from COVID-19 and the pressure to ditch legacy systems for cloud computing are two motivating factors behind the insurance industry’s recent adoption of newer innovations.
New technologies, specifically AI, have the potential to improve the overall efficiency of the insurance industry. Implementing technology can be a game-changer regarding new product introduction, method of delivery, and claim settlement speed, all of which can drastically improve customer satisfaction and garner greater customer loyalty.
Problems Plaguing the Insurance Industry
As companies undergo their own digital transformation initiatives internally, they also must contend with external changes brought about by market conditions and increased customer demands.
Some of the other problems the insurance industry face include:
Putting high quality enterprise data in the driver’s seat can help organizations achieve their digital goals and avoid pitfalls caused by exterior forces. Indeed, data can propel the insurance industry forward. However, the challenge for many companies is analyzing the vast amounts of data they collect and utilizing it to make insightful product and customer-driven decisions. Touched on earlier, legacy systems and digitization challenges can thwart the best-meaning intentions.
Introduction to AI’s Innovative New Applications
By using data and new AI-driven applications, companies can improve the overall customer experience. Increasingly, companies are using mobile applications, chatbots, and AI-generated quotes for quicker customer engagement. McKinsey estimates AI investments across functions can bring a yearly value of up to $1.1 trillion for the insurance industry.
From a business standpoint, insurers can utilize AI to automate their underwriting process, reducing the time it takes to get a policy. Further, AI and ML enable insurers to automate risk assessment, helping to write policies with greater efficiency and accuracy.
Additionally, with the large amounts of data collected, companies can use automatic text processing to analyze data, increasing the probability of making better decisions. Accurate analysis gives greater insight into what consumers are likely to buy, helping to save customers money and improve their satisfaction.
AI for Underwriting
There’s a lot of actuarial science that goes into underwriting. Traditionally, actuaries would do the statistics and the calculations to determine a product rate, premium, and out of pocket expenses. Now, a great deal of underwriting is being done with AI tools. Using AI for underwriting, companies not only iron out the wrinkles for themselves, but also end up benefiting their customers, namely by introducing and selling plans that are right for them. It’s undeniable that AI can also speed up the application process by eliminating human errors and detecting fraud early. This is possible because AI can analyze and monitor transactions in real time.
In addition to detecting problems, AI can aid in solving fairness issues which plague the industry today. This is evident when it comes to pricing as credit-based insurance scores can affect premiums. As for risk, ML assists underwriting when it comes to risk management. Underwriters don’t have to focus as much on potential dishonesty amongst applications as ML can use generated information to assess potential risk for the carrier.
Data, AI, and ML all play a role in improving the overall efficiency of the underwriting process. Underwriters can shift their focus from manual data analysis and repetitive tasks to other more meaningful operational and management areas. From a customer standpoint, benefits include greater engagement, streamlined services, and easier artificial intelligence claims processing.
AI in Insurance Use Cases
By harnessing the power of data and implementing AI, ML, and other applications, insurers are seeing the value of their investments through increased efficiency and enhanced customer satisfaction. One example of value is cost savings. The total cost of insurance fraud (non-health) is more than $40 billion per year. With AI’s ability to assist with fraud protection, it helps insurance companies to save money and prevents American families from paying increased premiums.
Cost savings can be found on the customer side of the insurance equation as well. With the insight companies gain through the collection of data, coupled with their use of ML and predictive analytics, they can offer customers better insurance premiums. This practice saves customers money and increases their loyalty.
As for enhanced customer satisfaction, AI-powered chatbots help to personalize experiences for customers. From engagement through claims, customers experience a higher level of support throughout their journeys. Our final example is related to claims. Quicker claim turnarounds are beneficial for all parties involved. Using optical character recognition (OCR) and computer vision, manual data entry and claim cycle times are reduced.
AI: Blazing a New Trail for Insurance Operations
We live in a world of connectivity. With the use of mobile devices and emergence of new technologies connecting us, insurers will continue to have an abundance of data at their disposal. For companies, this data acts as a catalyst for a deeper connection with customers, ensures better-real time delivery of products, and elevates the level of personalization offered to customers. Companies are afforded the opportunity for cross-selling or up-selling their products while consumers benefit from better pricing. It’s fair to say that data and AI-driven technologies will impact the insurance industry for the foreseeable future.
Apexon helps enterprises maximize the value of complex data ecosystems with its advanced analytics and AI/ML services. Complexity, poor data management, and ill-equipped infrastructure and tools are all obstacles to effectively putting your data to work for your business. If you’re interested in learning further about the power of modern analytics and data, check out Apexon’s Advance Analytics and AI/ML services or get in touch with us directly using the form below.
This blog was cowritten by Mike Boese and Dr. Atif Farid Mohammad