How Machine Learning Transforms Insurance
We like our insurance carriers to be risk averse. So it should come as no surprise they are often last to innovate. Insurers need to feel very comfortable with their risk predictions before making a change. Well, machine learning is writing a new chapter in the old insurance book. There are 3 key reasons why this is happening now:
- New InsurTech players are grabbing market share and setting new standards. Traditional carriers have no choice but to follow suit.
- Customers are expecting Netflix/Spotify like personalization, and have no problem changing provider — this is expected to grow as we see more millennials maturing out of their parents’ policies;
- Getting started with machine learning is becoming VERY easy: open source frameworks, accelerated hardware, pre-trained models available via API’s, validated algorithms, explosion of online training.
As with any innovation it only takes two things for wide spread adoption:
- Potential to improve business goals.
- Easy to pilot impact on business goals.
With time, we see that successful pilots become products. Teams are hired / trained, resources are allocated, business goals gain more ‘appetite’ and models are tweaked.
For P&C carriers we see the opportunity for improving business goals, and easily pilot machine learning in the following areas:
Given the complex and behavioral nature of risk factors, Machine Learning is ideal for predicting risk. The challenge lies in regulatory oversight and the fact that most historic data is still unstructured. This is why we often see machine learning applied to new products such as those using data from IoT sensors in cars (telematics) and home (connected home). But innovative carriers are not limited there. They use pre-trained machine learning models to structure their piles of unstructured data: APIs to transcribe coupled with natural language understanding (NLU) extract features from recorded call center calls, handwriting and NLP/NLU tools for written records. Leading towards identifying new risk factors using unsupervised learning models.
Carriers can get an actuarial lift even without designing and filing new actuarial models. Using machine learning to better predict risk factors in existing (filed) models. For example, a carrier may have already filed a mileage based rate-plan for auto insurance, but rely on user reported, or other less accurate estimates to determine mileage. Machine learning can help predict mileage driven, less biased and more accurately. Similarly, API’s to pre-trained chat bots using lifelike speech and translators, can turn website underwriting forms into more engaging and personalized chats, that have a good chance to reduce soft fraud.
Claims handling is a time intensive task often involving manual labor by claims adjusters on site. Innovative carriers already have policy holders take pictures and videos of their damaged assets (home, car…) and compare to baseline or similar assets. Carriers could easily leverage existing API’s for image processing, coupled with bot APIs to build a high-precision model, even at the expense of low-recall. Compared to having 100% of their book handled manually, a triage bot that automates even a mere 20% of the claims (with high precision) can enable carriers to start with a low risk service that’s on par with new InsurTech players, and improve ratios over time. Such a tool can even be leveraged by adjusters, reducing their time and cost.
While personalized pricing may be regulatory challenging, personalizing the insurance product offering is expected in this Netflix / Spotify age. As basic coverage is commoditizing, carriers differentiate their products based on riders and value added services. Not to mention full product offerings based on life events. Carriers can (with consent of course) leverage social media data to tailor and personalize the offering. Similarly marketing departments can use readily available recommendation algorithms to match and promote content about the benefits of certain riders / value-adds to relevant customers at the relevant time.
The world of insurance distribution is growing in complexity. Carriers are struggling with the growing power of intermediaries and agents are having hard time optimizing their efforts due to lack of predictability of loss commissions. Point of sale and affiliation programs are growing and with them the need for new distribution incentive models. Both traditional and new distribution channels could benefit from machine learning. Brokers, point-of-sale partners and carriers can leverage readily available machine learning models and algorithms designed for retail, in order to forecast channel premiums. Carriers can grow direct channels without growing headcount, using pre-trained chat bots, NLU and lifelike speech API’s.
Machine learning is part of our everyday lives. Innovative insurers are now jumping on the ML wagon with an ever growing ease, which carriers will be left behind?
This post was first published on Becoming Human: Artificial Intelligence Magazine on April 1, 2019