What happens when the big boys of the insurance industry meet under one roof?
Apart from assessing how deep everyone’s pockets are, they discuss what they can do to make them deeper. The last 10 years have been incredibly profitable for the insurance industry as a whole – Personal, Commercial, Property, Life, Annuity, Healthcare – you name it! However, in an ever changing landscape where Social networking is shifting the balance of power to consumers, environmental pressures needing to be addressed, rise of economic power in emerging markets, Geo-political issues and last but not the least explosion of data and technology offer great risk to the insurance industry. And, typically, insurers tend to take risk, seriously!
It was a pleasant summer morning in a big conference hall in Chicago where the leaders of the Insurance industry descended. All of these leaders were Chief Data/Insurance/Integration/Digital/Analytics/Innovation/Customer/Data Science Officers or basically anyone who had anything remotely to do with the data in insurance. A key theme of the day was how Insurance firms can move from a data centric approach to a data centric approach that solves business problems. A number of issues ranging from – Leveraging big data technologies & abolishing legacy systems, creating a culture of analytics in the organization, hiring the right people to work with data, using advanced machine learning and what can be done with Internet of Things. As an Analytics as a service company, TEG Analytics fueled passionate discussions on how they leverage advanced analytical techniques to drive business value from data. Here’s a summary of things that were discussed.
As users of technology, insurers are typically laggards when compared to technologically progressive industries. In an environment of data proliferation and inexpensive computing & storage, as well said by a CTO, there is little scope of finding an excuse for not embracing a technology ecosystem that can drive gains from automation, operational efficiencies and improving the customer experience. Historically, insurers have used structured data to make tactical and operational decisions around customer targeting, risk pricing, loss estimation etc. However, with the augment of Internet of Things – massive volumes of unstructured and sensor data is being made available. According to one CDO, this is giving rise to a new generation of consumers who demand speed, transparency and convenience reversing the age old wisdom that ‘insurance is sold and not bought’. Choices are becoming complex to comprehend in the digital world of multiple interactions as choices become more dependent on trust defined by social networks and not agents/intermediaries. To harness this trend of ‘Big-data’ and complement structured with unstructured data, financial and intellectual investments are being done to allow insurers to make strategic forward-looking decisions from data. A unanimous view was to add new types of information, integrating external data sources, incorporating granularity to the data. With presence of NAIC, the standard-setting, regulatory support body in the room, the Importance of adhering to data governance policies was demonstrated.
Some gasps were let out when a head of Data science said 75% of models do not see the light of the day of implementation. A key theme that pervaded the entire conference was – developing a culture of analytics within an organization. Data has always been used as a key ingredient in the different functions of the insurance industry. Risk, Underwriting, Pricing, Campaigns, Claims etc. all use data and key metrics in some way or the other. The problem arises when executives are unwilling to operationalize insights from data into making decisions. This is where having leaders in the space of Data, Information and Analytics must work together to inculcate a data driven decision making process in the organization. Seamless integration of processes of these three divisions can potentially transform the business without losing the sight of feasibility and risk. Harmonizing the analytics and business functions is imperative in capitalizing the tactical and strategic benefits of data. With new competitive pressures, risks, opportunities available in the market, the CAO must build a case for change with other business leaders. TEG Analytics believes that the analytics folks must work collaboratively with business leaders to define a clear, well-defined goal rooted completely in business strategy. Undertaking an analytics project with a business sponsor driven by a desired outcome and insights delivered @ the speed of business can create immediate, implementable value for the business function. Arvind (CEO of TEG Analytics) said that data science teams are sometimes infamous for interacting with the business teams in a language only they understand. They should engage project owners in a more holistic way and take them on a journey from the start to the end; finishing with a go-to plan or recommendation that is implementable.
Sophisticated analytics progresses to a point where no more useful information can be extracted and all key decision-making has been automated to provide sharp & quick insights. Different functions in the insurance domain have historically used data. However, there is a big gap in using data and using data to make decisions swiftly.
Underwriting in insurance can be automated and made intelligent by using structured data, sensor/IoT information along with unstructured data. Use of process mining techniques, NLP and deep learning algorithms, we can build personalized underwriting systems that take into account unique behaviors and circumstances.
With the onset of internet, mobile and social; the way consumers interact has changed. This has led to disappearance of two things – distributor sales channels and the concept of ‘advice’ before buying an insurance product. Insurers must track the entire consumer journey to understand its needs and sentiments to be able to design personalized products. Advanced Machine learning techniques can be leveraged to infer customer behavior from this data. This machine-advisor evolution will offer intelligence based on customer needs by building recommender systems to advice products.
Analytics will also help in improving profitability from operational efficiency. Multiple staffing models can be built and tested to increase resource utilization while increasing underwriting throughout sales performance. A machine learning based Claims insights platform can accurately model and update frequency and severity of losses over different economic and insurance cycles. Insurers can apply claims insights to product design, distribution, and marketing to improve overall lifetime profitability of customers. In order to determine repair costs, use of deep learning techniques to automatically categorize the severity of damage to vehicles involved in accidents. Use decision tree, SVM, and Bayesian Networks to build claims predictive models on telematics data. Use of graph or social networks to identify patterns of fraud in claims. These predictive models can improve effectiveness by identifying the best customers thereby refining risk assessments and enhancing claim adjustments.
All in all, the Chief Data Officer conference was an insightful discussion on the current state of the insurance industry, its evolution in a world of massive data propagation and how firms must evolve with the changing landscape of the industry. Various players from different domains within the insurance vertical discussed key themes like abolishing legacy systems, moving to technologically advanced ecosystems capable of handling data from every sphere and leveraging advanced analytical techniques to derive business value for various functions of the industry.