When it comes to insurance companies use of data and analytics, it can be viewed as a good news/not so good news situation. The good news is that insurers are at a mature level in terms of using analytics for underwriting and pricing. Also positive is that insurers are investing in infrastructure to harness Big Data, and beginning to develop use cases to gain insights from the data. Carriers are beginning to focus specifically on data as an asset. Theyre setting up data organizations and employing data officers, and theyre working to have a dedicated view of the data.
The not-so-good news is that insurers are lagging when compared with other industries in terms of their actions to leverage predictive analytics specifically as it applies to customer engagement. In this aspect, insurers have a long way to go, especially compared to other industries such as retail that have redefined customer expectations.
The main challenge we see is in the usage of analytics. There are several reasons for this. First, is un-trustable data quality. Data often isnt good enough to make informed decisions. Second, data is not available in real time, which prohibits insurers from making meaningful changes to strategies and approaches. Third is the limited availability of good data sources and data marketplaces to get high-quality data.
As a result of these issues, business users often lack confidence in the results of analytics efforts. This can either be because of the quality of data or because the findings are too complex for them to interpret. Business users need to make deliberate efforts to understand the analytics processes and outputs.
Three significant issues contribute to the challenges around insurers use of data and analytics:
- The use of analytics in insurance is complex by itself because of the large number of variables that influence an insurance situation. Predicting outcomes such as the likelihood of an accident is extremely complex, often requiring data from across departments, and even industries. Fundamentally, insurance analytics includes many variables, and current models are often inadequate in reflecting real-world situations.
- Insurers dont have all the data and correlations, all the items that influence an event at the same point in time. The amount of data we have and what we operate on to create good quality analytics is limited.
- Analytics and understanding the data is a complex topic by itself. In insurance, there is a growing need for skilled data analysts and data scientists who understand the business, and the correlation to data.
So how can insurers overcome these challenges to use data and analytics to their advantage?
Take the long term view
Theres no quick fix to addressing these challenges. Optimizing data analytics isnt just a project, but a long-term objective. In order to manage the complexities and ensure that their employees have the right skillsets, insurers should take these actions:
- Set up a cross-functional organization composed of business experts, data experts and analytics experts altogether. Its not easy to build this organization, but its foundational.
- Start looking to set up the right infrastructure for data consumption. You will get a lot of data from many sources and need the technology infrastructure to do that.
- Seek new data from untraditional sources. Insurers should be looking out for new sources of data and develop strategies to integrate the new data in terms of decisions and pricing. For instance, insurers previously never had telematics data, a relatively new source of data that looks at driving behavior patterns and insights.
These steps are not an exhaustive list, but will give you a better idea on how to leverage data and analytics in a more effective manner. This is just a first step in helping you drive the right engagement with the customer, in the right place, the right manner, and at the right time. And although the road is long, the journey promises to be exciting. Its time to push the boundaries of your Big Data and analytics further.