With over 6,000 insurance businesses operating in the United States alone, it’s safe to say insurance has become a red-ocean market where few companies can succeed.
To improve operational efficiency and deliver better customer service, insurers have been investing in modern IT solutions — insurance management systems (IMSs), customer relationship management systems (CRMs), document management systems (DMSs), and underwriting software, among others.
Known collectively as “Insurtech,” these systems help insurance agents reach out to new customers, tailor their offerings based on an individual’s risk profile, streamline the claims overview and approval process, and tap into more accurate risk assessment and dynamic pricing.
Despite positively impacting agents’ productivity (and insurance companies’ bottom lines!), traditional Insurtech solutions are starting to hit their limits.
Symfra, a company providing insurance software development services, will explore the factors rendering Insurtech solutions less effective — and explain how machine learning (ML) gives insurance companies a competitive edge.
Why Machine Learning Is Making Waves in the Insurtech Sector
Machine learning, a family of technologies that involve intelligent algorithms processing data and drawing conclusions from it, slowly but surely infiltrates traditional Insurtech tools.
There are several reasons for that:
- Data deluge. In the digital age, an enterprise of an average size sees its data volumes grow by 63-100% monthly. Meanwhile, the number of data sources used by organizations for making better-informed decisions fluctuates between 400 and 1,000. This is even more so for insurance businesses, who increasingly rely on telematics solutions and connected consumer electronics like smartwatches and fitness trackers for personalized policy pricing and claims validation. For obvious reasons, agents cannot review and interpret this data manually — even with the help of Insurtech tools. By integrating machine learning capabilities into insurance software solutions, companies can enhance or fully automate repetitive processes and gain deeper insight into their data.
- Automation potential. As an industry that heavily relies on data collection and analysis and is plagued with routine, repetitive processes, insurance has enormous automation potential. According to McKinsey, a quarter of all jobs in the sector can be fully automated by 2025, with the largest percentage of such jobs coming from the insurance operations and administrative support segments. ML-driven tools, such as intelligent process automation (IPA), will play a critical role in this transformation
- Machine learning maturity. While we’re still years away from the artificial intelligence revolution depicted in sci-fi movies, machine learning and other AI subsets have reached a significant level of maturity in various domains. Thanks to recent advances in electronics design, cloud computing, and generative AI, organizations can start experimenting with recommendation engines, image analysis, and predictive analytics at a relatively low cost — and reap tangible benefits faster.
How Machine Learning Transforms Insurtech
- Claims processing. One of the most potent machine learning use cases in the insurance industry is intelligent claims management. Thanks to automated data entry, fraud detection capabilities, claims classification, and chatbot-assisted communications, ML-infused software can easily take over claims registration and triage operations. Additionally, ML models help agents with claims volume forecasting and audits. An example of ML-assisted claims management comes from Fukoku Mutual Life Insurance, a Japanese insurance company that integrated IBM’s Watson Explorer into its IT infrastructure and achieved a 30% productivity increase, with annual cost savings of $1 million.
- Customer service. With the emergence of machine learning solutions that understand and generate text on par with humans, insurance companies massively give their customer support channels a digital overhaul. From tasking bots with answering routine customer questions on self-service portals to conducting sentiment analysis and detecting clients who are about to churn, ML unlocks new opportunities for insurers to personalize the customer experience. For example, Aviva, one of the leading insurance companies in the UK, automated up to 60% of initial customer inquiries by adding Zowie, an AI customer support solution, to their self-service platform.
- Underwriting. The process of evaluating the risk of insuring a person or asset and determining the terms and conditions of that coverage is time-consuming and knowledge-intensive. While machine learning cannot automate it completely, intelligent algorithms can aid agents in various associated tasks. Thanks to advanced data analytics capabilities, ML can assess insurance risks based on factors other than an applicant’s age, occupation, and medical history. For example, it’s now possible to pull and analyze data from external sources like IoT devices, wearable health tech, and telematics for auto insurance to provide more granular insights into individual risk profiles. Lemonade, a US-based insurance company, taps into precision underwriting to provide customized offerings to its clients. The company claims to collect 100x more data points per customer than an average insurer, diving into micro-segmentation, and is currently planning to use generative AI in customer support operations. The smart approach helped Lemonade grow its stock price by 11% amid the worst bout of recession in 40 years.
Even though the digital revolution does not happen overnight, the success of ChatGPT and other generative AI solutions prompts insurance companies to revamp their IT strategies and systems.
Currently, most insurers only use 10-15% of the data they have — simply because traditional Insurtech tools cannot handle the increasing data volumes effectively.
Machine learning can help insurance professionals spot subtle patterns in operational data, adjust their products to better serve the digital-first customer base, and automate repetitive and error-prone manual processes.
And this is where your competitive advantage might lie.
