The big shift in Insurance operations: Intelligent automation & smart data analysis
What Makes Data Analytics and AI Important for Insurers
Insurance has had a rough couple of years. Natural disasters such as major wildfires and hurricanes have wreaked havoc on every sector of the industry, from life insurance to large commercial lines, in addition to the widespread effects of the COVID-19 pandemic. These patterns are unlikely to change. Black swan events are becoming more common, just as some risks have become more measurable and predictable. In order to thrive in this environment, insurers must improve their risk assessment and model the potential consequences of capital-intensive disasters.
This will necessitate not only traditional actuarial models, but also the use of data analytics in insurance. This entails leveraging data sets ranging from weather models to personal health tracking—a task that necessitates specialised knowledge of data analytics and the use of AI in insurance. Insurers will also require expertise and documentation to effectively explain their methodology to regulators.
Insurance companies can cut costs, better position themselves to handle unexpected crises, and ensure they don't fall behind their competitors by highlighting potential areas of risk, improving underwriting effectiveness, and reducing the human inputs required for basic tasks. This is where upskilling and reskilling, either organizationally or individually, come into play.
The purpose of this article is to discuss how digital transformation is empowering the insurance sector to grow and build its business.
1. Use of Data Analytics and AI for Actuaries
Actuarial science, as it has traditionally been practiced, shares many similarities with data analytics. As a result, it should come as no surprise that the rise of big data and AI has numerous implications for actuarial work. Actuaries have long relied on financial and statistical theories and models to assess and advise on financial risk. The quality of data inputs influences success in part.
With access to new types of data, actuaries can fine-tune rate tables and risk predictions more effectively than ever before. However, the volume and speed of data inputs now available exceed the capacity of traditional methods. Data sources could include product developers, reinsurers, distributors, and others.
Actuaries will be able to parse massive, rapidly changing data sets to identify risk predictors using data analysis that relies on programming and statistical knowledge. While human judgement is still required, actuaries will need to have a basic understanding of data analysis in order to collaborate with data scientists, especially if they are not doing the programming themselves. Actuaries, in particular, will need to understand the role of predictive analytics as opposed to traditional inferential statistical models.
As the effects of climate change continue to wreak havoc on the insurance industry, data analysis that can parse complex weather and satellite inputs to predict potential damages will become increasingly important. Insurance companies will need to commit to the ongoing use of this strategy because the full effects of climate change are currently unknown.
2. Automated claims processing and management
One of the insurance industry's pain points is claims handling and management. Most insurance companies have relied on their employees to handle claims and settlements; however, this reliance opens the door to financial leakage due to human error and negligence. Recent research by Mckinsey & Company indicates that automation can lower claims journey costs by as much as 30%. Insurers can reduce their reliance on manual labour and streamline labour pools by leveraging automation.
3. Providing a personalised customer experience
Insurers must develop strong CX strategies aimed at increasing customer satisfaction, loyalty, and advocacy.
Customers expect superior service and an unparalleled experience from insurance companies, and digitalization is constantly upending old business models. Businesses that want to improve customer acquisition and retention should prioritise the customer experience. A case study on providing the best customer experience will help you understand this better.
According to McKinsey, up to 30% of underwriting roles could be automated by 2030, with another 30% involving increased use of analytics tools and collaboration with data scientists. This necessitates the upskilling of underwriters.
And, just as data science and AI will enable more accurate risk prediction at scale, underwriters can use these skills to better predict risk and write policies on an individual level, allowing them to remain competitive on pricing without taking on undue risk.
Insurers can provide instant quotes to customers with lower risk profiles thanks to big data and algorithms, allowing underwriters to focus on more nuanced cases. Another area ripe for disruption is life insurance. Underwriters will continue to integrate new data sources, such as prescription medication data, pet ownership data, and credit scores. With access to robust data analytics and artificial intelligence in insurance, effective underwriting will necessitate fewer invasive requirements and simpler applications.