In recent years, insurance firms have set out on their digital transformation journeys, gaining efficiencies, growing their revenue and boosting customer experience by automating operations and providing user-friendly mobile applications. And, Artificial Intelligence (AI) is further transforming the industry in unique and powerful ways we never imagined possible. New applications of machine learning in insurance make headlines every week. Following are trending AI solutions that are transforming insurance in auto, home, and healthcare markets.
The auto insurance industry has been disrupted by the rapid growth of services like Uber and Lyft, pioneers the sharing economy that created heightened demand for insurers. Yet, it will be really shaken to its core with the advent of self-driving cars. In order to better compete when driver risk is diminished, auto insurance companies are increasingly turning to to machine learning to reduce costs and boost the customer experience.
- Risk predictions based on driver habits- Root Insurance has a fully automated process for calculating premiums for specific drivers. Instead of relying on age and other demographic data, they track driver habits through a mobile app. After drivers take a test drive, they will combine the data with other factors and employ advanced predictive analytics to create a risk profile and provide a quote. All without a single agent.
- Computer vision for assessing car damage- Other companies like Liberty Mutual are using computer vision algorithms to automatically assess the damage in a car from photos uploaded via their mobile app. Trained with thousands of images, the AI algorithm can automatically identify different types of damage and provide a preliminary cost assessment.
- Customer service virtual assistants- Geiko provides virtual assistants to provide a delightful experience to customers that need guidance or have questions about policies and claims. Rather than searching through pages in a website, the virtual assistant provides a more organic and dynamic experience for the user. Customers can choose to talk to a real person at any time if the chatbot cannot answer a question.
Machine Learning in Home & Property Insurance
AI is transforming home and property insurance by providing better and more effective ways of providing services to clients: from customer support and getting quotes to fully processing claims.
- AI-powered claims processing- Lemonade is one of the unicorns that has pioneered machine learning in the insurance claims process. Its AI algorithms can detect fraud in claims and manage most of the claims process without human involvement. Lemonade’s friendly virtual assistants, Jim and Maya, provide a delightful experience for users.
- Automated property analysis via satellite imagery- Computer vision applications for insurance are on the rise. Cape Analytics empowers insurers with AI solutions that analyze satellite imagery and automatically feed data regarding roof type, roof conditions, solar panels, and wildfire risk, among other factors.
- Fraud Detection- Using AI to detect fraud is common in many insurance companies around the world. Artificial intelligence is particularly effective in detecting anomalies and there are many providers that have built specialized fraud-detection solutions in this space.
Proactiveness will be the core driver of most machine learning applications in health insurance. Health insurers are shifting strategies to proactively improve health outcomes instead of reactively processing claims for medical benefits.
- Churn prediction- Annual enrollment periods are the most stressful time of the year for health insurers. There is fierce competition among providers to attract and retain membership, while people shop around for the best deal in town. Churn prediction models aid health providers in creating proactive strategies in managing annual membership vs the traditional reactive strategies that have been employed up to this point.
- Predicting hospital readmissions and patients at risk- Detecting patients at risk of developing chronic diseases or getting readmitted to a hospital will help providers proactively look out for their health and prevent future critical conditions. CMS issued a challenge in 2019 to search for AI solutions that can prevent adverse effects and is actively investing in machine learning for insurance.
- Computer vision for early disease detection- IDx-DR is one of the country’s first autonomous FDA- approved clinical diagnostic tools and is powered by artificial intelligence. It uses computer vision to analyze retina images to diagnose diabetic retinopathy, a condition that can cause blindness. The exams are also eligible for reimbursement by the Centers for Medicare and Medicaid Services (CMS).
The wave of innovations using machine learning in insurance is just getting started. We can expect other technologies to continue to gain ground in the upcoming months and years and will likely see new business models for insurers.
- New Data– Tapping new data generated by wearables like FitBit and genetic reports provided by companies like ancestry.com, provide a whole new realm of possibilities for health and life insurers in terms of performing analysis at an individual level. While there are many questions surrounding privacy and ethics, the potential to provide real value to both patients and providers is undeniable.
- Personalized Benefits- As generations continue to embrace personalized recommendation systems like those offered by Netflix and Amazon, insurers will see increased pressure of finding ways to provide personalized insurance programs to fit an individual’s particular needs.
- Holistic Wellness- Health insurance providers will continue to expand their offerings to include holistic benefits that promote mental health, nutrition, exercise in addition to medical checkups to drive positive health outcomes.
Many organizations are implementing AI solutions for claims processing, fraud detection and virtual assistants. Machine learning in insurance is quickly becoming commonplace, but we are only seeing the tip of the iceberg in the digital transformation of a very traditional industry.