It’s well known that the insurance industry is one of the most competitive sectors with the highest rates of customer churn which is eroding profitability. Yet look at the speed at which new entrants, such as Lemonade in the US, have disrupted the industry. Five years ago it didn’t exist. Today it’s valued at $2bn. Using Artificial Intelligence (AI) combined with connected and automated third-party data, it has very quickly scaled its offering.
But it’s not just newcomers that can take advantage of AI, existing players in the insurance market can implement AI into their business to leap ahead of the competition.
Five ways AI can enhance your business:
1. Pricing risk outside of the norm
To be able to price risk accurately you need to rely on accurate data. In the past, models have been based on the ‘norm’. This approach causes problems for properties that fall outside of this e.g. large houses, listed buildings or properties located in prime postcodes. Price then comes with a large amount of uncertainly meaning that they risk becoming significantly over or under insured. This has served to erode profitability; at best leaving money on the table, at worst resulting in significant claims that could have been predicted. Creating new models that identify these properties and accounts for their ‘differences’ will enable insurers to manage pricing and therefore risk more effectively.
2. Improved confidence
Insurers may be reliant on their customers telling them the cost of rebuilding their property or the information may have been supplied by a model. If so, how accurate is the information and the model that predicted it? This is a key question as under data protection legislation and data ethics, explainability is a legal requirement. By applying AI to create confidence scores, insurers are able to make more informed decisions about whether to price the risk or ask more questions.
3. Better customer understanding
Data has long been vaunted as a panacea, but so far has failed to realise its potential. The reason for this is that whilst the industry is data rich, the data tends to be disconnected and channel centric. Understanding the ‘what’ and the ‘why’ when it comes to motivating customer action can be tricky.
One of the major benefits of AI is it is undaunted by big data. In fact, the more data (so long as it’s accurate) the better. This means that you can supplement your traditional customer data sets with new AI-led data. Traditional stable data like demographics or property type can be enhanced with more transient data such as opinions, clicks, like and emotions, engagement, transactions and societal trends. When brought together in the right way this holistic overview is incredibly powerful and can be used to create more predictive models.
4. More predictive models
Traditional statistics identify linear patterns in data or evaluate human hypotheses. With AI, highly complex, nonlinear patterns can be identified without human bias or pre-conceptions. Ultimately this means you can find patterns that would have been impossible to discover without AI. New models can be built using previously unknown insight as a foundation, leading to greater prediction power and ultimately enhanced business outcomes. What’s more, the models get more accurate as they learn and find new patterns in the data that can be used to both further enhance the model and further enhance the business outcomes
5. Identifying the what and the why through AI-powered customer segmentation
Through the application of computer and data science the process of developing customer segmentations can now be automated. This has significantly reduced the build time from months to weeks. As a result, segmentations can now be developed for specific use cases enabling organisations to better understand the motivations of their customers. For instance, anticipating when customers are most likely to move house due to downsizing. By predicting this future behaviour it becomes possible to tailor communications from short-term price reduction deals to longer term insurance planning; making the move from insurance supplier to insurance partner and thus reducing churn.
There is no doubt that the possibilities that AI affords are truly transformational. Broadly speaking it is still in its infancy and we are all still on a journey of discovery. Every day we uncover new applications that yesterday we didn’t know were possible.