Artificial Intelligence: The new Kid in Town
Featured on knect365.com for the InsurTech Rising Live digital week
Artificial Intelligence (AI) is the new kid in town everybody is talking about. Lots of people are afraid of it or at least unsure what to think of the kid. Is AI going to affect me, my company, my industry? Is AI threatening my job? Is the kid just a show-off or can I befriend him and find common interests? There exist a lot of reservations about AI and in the following we will clarify some of these and showcase how insurers can greatly benefit, leading to improved customer touch-points, increased revenue, and decreased costs.
Apart from the likes of Google, Facebook, Amazon, and Tesla and their mainly digital business models and obvious applications of AI, a lot of traditional industries are employing intelligent algorithms to augment previously manual approaches. One such example is steel production where a mix of components are melted in a specific order and composition to achieve a certain grade of steel, defined by the mix of elements in the resulting product. Previously being led by the expertise and experience of the professionals, a data driven approach has shown to lead to an average decrease of 5% in ferroalloy use with no loss in steel quality, resulting in millions of annual savings.
Insurance — AI friend or foe?
It is easy to predict that AI is going to have a major impact in a lot of domains across all industries. The insurance industry, however, has yet been hesitant which will lead to a lot of preventable difficulties. As soon as AI techniques have become common mainstream tools across all industry sectors, it will be increasingly challenging to get into the game. Talents are rare on the market and already highly fought over, making it very difficult to enthuse them for the industry. Another problem is the competitive advantage that can be gained. Can you afford to compete with someone who is, let’s say, 20% more efficient?
Employing AI techniques only for the sake of AI, however, is not the way to go. A good strategy starts off with a business decision with a clearly defined goal and relevant metrics. Solving the business problem then most definitely includes one or more machine learning and data driven approaches to at least support the decision-making process. What these approaches generally need to make reliable predictions, apart from a clearly formulated question, are masses of clean data to learn from, highlighting another challenge for insurers, namely data quality, privacy issues and the integration into legacy systems.
So why bother with AI?
It is easy to let these challenges cloud our sight for the opportunities that lie ahead. AI gives us means to automate processes, personalize products, communications and care, predict personal and collective developments, discover trends and unusual patterns in the data, and more. It has the potential to impact the insurance industry in numerous areas, such as marketing, customer interaction, claims processing, fraud detection, and underwriting.
One use case for the application of AI lies in marketing and customer acquisition. First, it is important to know who our customers are and what their value for the company is. Only then we can decide how to approach these and take action. We can dynamically adapt the bidding strategies on search and social media platforms to efficiently win customers of value. We can personalize our offers and the advertisement to only present what is relevant for the user, leading to higher conversion rates and more satisfied customers that are not bothered by irrelevant messages. We can move beyond a shotgun approach and target even groups of size 1.
The goal is not to replace the human in the first place, but rather to remove the repetitive and time consuming robot-like tasks from the stack and to empower the insurer to be more human again.
There is, however, even more potential in core insurance processes, such as claims in the health sector. Every claim must be processed and checked for eligibility, coverage of the insured, and possible fraudulent cases, among other steps. Wit AI we can automate major parts of the process. The goal is not to replace the human in the first place, but rather to remove the repetitive and time consuming robot-like tasks from the stack and to empower the insurer to be more human again. Moreover, with the amount of data that is collected through the line we can seek to achieve a paradigm shift from care to targeted prevention. Instead of paying for treatments that are costly for the insurances and unhealthy for the patient, it would be beneficial to invest in prevention and early detection of diseases and risks. Using the historic data from many, we can learn to predict the risks for individuals. A cardiac disease, for example, costs hundreds of thousands of dollars for the insurance, thus being able to accurately predict risks will lead to a healthy patient and massive amounts of savings.
These are just two examples of where AI can already augment current approaches in the insurance business and more will follow. Now is the time to take the chance and to be part of the change.