How insurance companies can enhance the overall customer experience, streamline processes, and better assess risk with AI-powered tools such as predictive analytics and machine learning technology?
Rapid advances in technology, the continuing talent shortage and rising customer expectations for experiences of all types have disrupted the insurance industry over the last several years, challenging carriers to evolve their service strategies and business processes accordingly.
Will AI take the risk out of insurance, leaving nothing but pure, unadulterated coverage?
Artificial intelligence (AI) is a rapidly growing field that has the potential to revolutionize the insurance industry. As a result, 74% of insurance executives are planning to increase their investments in AI.
AI can significantly improve the efficiency and effectiveness of group insurance. McKinsey estimates that across functions and use cases, AI investments can drive up to a whopping $1.1 trillion in potential annual value for the insurance industry.
In this article, I review how insurance companies can enhance the overall customer experience, streamline processes, and better assess risk with AI-powered tools such as predictive analytics and machine learning technology (see How Artificial Intelligence Can Help Insurers Reduce the Inflation Impact?).
Costs for everything from food to healthcare to insurance premiums have increased in the past few years – especially after the 2020 lockdown (see How AI is Modernizing HealthCare Industry?). While inflation is expected, the pandemic has also changed how the insurance industry feels the impact.
Some experts believe that artificial intelligence (AI) could completely disrupt the insurance industry as we know it. So what does the future hold for insurers?
Let’s take a closer look at the impact of AI on the future of insurance. The risks insurers cover and the ways they underwrite, distribute, and manage claims are also changing. In an increasingly digitalized world some risks will become less frequent, while others, like cyber, will gain in importance, and again others may cease to exist.
- Artificial intelligence (AI) can help insurers assess risk, detect fraud and reduce human error in the application process. The result is insurers who are better equipped to sell customers the plans most suited for them.
- Customers benefit from the streamlined service and claims processing that AI affords.
- Some insurers think that, as machine learning progresses, the need for human underwriters could become a thing of the past – but that day might be years away.
There are several ways in which insurance companies can tackle these challenges. One of the best ways forward would be to invest in technology solutions powered by artificial intelligence in insurance.
Machine Learning in insurance
Machine learning in insurance is a class of AI and computer science that uses algorithms and data to mimic how humans learn. It takes current data to train models and algorithms and gets more intelligent, accurate, and effective the more it’s used.
Insurers are being forced to explore ways to use predictive modelling and machine learning to maintain their competitive edge, boost business operations and enhance customer satisfaction.
They are also examining how they can take advantage of recent advances in artificial intelligence (AI) and machine learning to solve business challenges across the insurance value chain. These include underwriting and loss prevention, product pricing, claims handling, fraud detection, sales and customer experience.
Machine learning systems can analyze and contextualize data and automatically trigger actions without human interference.
This can help insurance companies improve operations, better serve customers, and increase profitability.
AI and advanced machine learning are among the top 10 strategic technology trends leading organisations are currently using to reinvent their business for a digital age (see How Rapid Advances in Technology Reshaping the Insurance Industry?).
Predictive analytics in insurance
Predictive analytics uses statistical models, machine learning algorithms, and data mining techniques to analyze large amounts of data and predict and forecast future events or outcomes.
Predictive analytics can help insurance companies better understand and manage their risks, improve operations, and increase profitability.
Predictive analytics is a branch of advanced analytics that makes predictions about future outcomes using historical data combined with statistical modeling, data mining techniques and machine learning. Companies employ predictive analytics to find patterns in this data to identify risks and opportunities. Predictive analytics is often associated with big data and data science.
Today, companies today are inundated with data from log files to images and video, and all of this data resides in disparate data repositories across an organization.
To gain insights from this data, data scientists use deep learning and machine learning algorithms to find patterns and make predictions about future events. Some of these statistical techniques include logistic and linear regression models, neural networks and decision trees. Some of these modeling techniques use initial predictive learnings to make additional predictive insights.
Personalizing customer experiences in insurance
Predictive analytics can help carriers better understand their customers’ buying habits and tailor their products to their needs, helping boost sales. In fact, 60% of insurers say predictive analytics has helped increase sales.
Supporting this trend, 76% of employers would be open to sharing basic employee information to inform personalized product recommendations.
With predictive analytics, employee benefits insurers can analyze data gathered from their core sales platforms, call centres, and social media channels to profile customers and segment them into different categories and create digital personas.
With segmented audiences, group benefits carriers can create more targeted marketing campaigns and suggest optimal coverage or products during enrollment based on the plan member’s demographic profile in addition to more personalized factors.
AI can help insurers create and underwrite more accurate and personalized insurance products and identify more high-risk individuals who may need additional support or intervention.
Insurance claim estimates and processing
Predictive analytics and machine learning models can automate claims processing and help insurers estimate future claims payments.
By analyzing historical claims data, predictive analytics can identify patterns in claims amounts and develop statistical models that predict the probability and risk of claims for new policies.
In addition, predictive analytics can identify trends, patterns, and opportunities that might affect future claims, such as workforce trends or the growth of specific industries. By understanding future risks, carriers can price their policies more accurately to mitigate potential losses.
Furthermore, machine learning can automate the process of reviewing and approving claims. For instance, machine learning can enable claim triaging, helping insurers improve workflows by differentiating between non-urgent claims that can be quickly settled or more substantial claims that need an agent’s review.
This can significantly speed up the claims process, and improve profitability and customer loyalty. In fact, automation can reduce the cost of a claims journey by as much as 30%.
How artificial intelligence is helping the underwriting process in insurance?
In the commercial lines, every application posits a new set of risk variables that are becoming increasingly complicated to assess and account for in the risk analysis strategy with precision.
Using artificially intelligent systems that assess an application profile against billions of data points accrued from 3rd party sources, underwriters can now gain visibility into the most relevant risk factors associated with a client profile.
In specialty lines like cyber insurance, gaining complete visibility into the risk exposure of an enterprise’s IT systems and appropriately translating these risks into profitable numbers for the business can be an impossible task for humans.
Small businesses with under $25M in revenue were particularly vulnerable in 2021. We saw a 56% increase in the average claim cost, increasing to $149,000 by the end of 2021. We also observed dramatic increases in the frequency of attacks; small businesses also saw a 40% increase in ransomware attacks and a 54% increase in funds transfer fraud incidents. Small businesses are especially vulnerable to threat actors as they often lack the resources to respond quickly.
Identifying insurance fraud
The FBI reports insurance fraud costs insurers $40 billion annually, and a fraudulent insurance claim is uncovered every five minutes.
Predictive analytics and machine learning can identify unusual patterns and anomalies that may indicate a higher risk of fraud by analyzing data on past claims, policyholder behavior, and other factors.
In 2022, The Canada Life and Health Association (CLHIA) launched an industry-wide initiative to pool anonymized claims data and use advanced artificial intelligence tools to analyze and enhance the detection and investigation of employee benefits fraud. By identifying patterns across millions of records, the program improved the effectiveness of benefits fraud investigations across the industry.
The key issue is there is not a one size fits all approach. Instead, insurers will be challenged to create specific machine-learning and predictive analytical models based on context.
Improved processes for first notice of loss
AI-driven solutions can also help customers notify their insurance providers as soon as possible using mobile applications. In this case, providing this option to customers can help cut operational costs. All they have to do is use their smartphone to capture evidence, and the automated system takes care of the rest.
Insurers can provide an estimate, process the claim, and procure the necessary spare parts or materials much faster, as manual intervention is very minimal in these cases.
It also reduces the key-to-key times for customers currently in the claims processing cycle, providing an improved and swifter experience.
AUTHOR: Mike de Waal, President & CEO at Global IQX – Group Insurance Software