"Data processing has historically been at the very core of the business of insurance undertakings, which is rooted strongly in data-led statistical analysis. Data has always been collected and processed to inform underwriting decisions, price policies, settle claims and prevent fraud. There has long been a pursuit of more granular data sets and predictive models, such that the relevance of Big Data Analytics for the sector is no surprise."
Study participants were chosen from the 28 EU member states, where EIOPA received 222 responses, as it follows:
- regarding the tradition of the companies: 170 were incumbents (76.6%), 20 start-ups over year 2010 (9.0%) and the rest of 32 unknown age (14.4%).
- by the type of license/authorization: 134 insurance undertaking license (60.3%), 50 Insurance intermediary license (22.5%), 25 both (11.3%) and 13 unknown license/authorization (5.9%).
- by insurance segment: 105 Motor & Health insurers (47.3%), 69 Motor insurers (31.1%), 35 Health insurers (15.8%), 13 unknown insurance portfolio (5.9%).
1. Traditional data sources (like demographic and exposure data) are used in combination with newer BDA-related technologies more often, but not replaced. The symbiosis between traditional and modern practices offer greater statistical power at the hands of insurers. Better tailored products/services and more accurate risk assessments are the main outcome.
2. The use of data outsourced from third-party data vendors and their corresponding algorithms used to calculate credit scores, driving scores, claims scores, etc. is relatively extended and this information can be used in technical models.
3. Big Data Analytics tools such as artificial intelligence or machine learning are already actively used by 31% of firms, and another 24% are at a proof of concept stage. Models based on these tools are often correlational and not causative, and they are primarily used on pricing and underwriting and claims management.
4. Big Data Analytics tools enable the development of very accurate assessments, without or with limited human intervention, increasing the efficiency and speed of decision making and therefore reducing operational costs. However, any biases inherent in the historic data will be reinforced through machine learning algorithms if firms don't have adequate governance arrangements in place. This issue becomes more significant where certain judgements of a (black box) algorithms cannot be specifically explained in a meaningful way.
5. Cloud computing services, which reportedly represent a key enabler of agility and data analytics, are already used by 33% of insurance firms, with a further 32% saying they will be moving to the cloud over the next 3 years. Data security and consumer protection are key concerns of this outsourcing activity.
More details about the subject can be found in EIOPA study, BIG DATA ANALYTICS IN MOTOR AND HEALTH INSURANCE: A THEMATIC REVIEW.