As insurers strive to become more relevant to their customers and more efficient, they have embraced the strategic importance of their data. Insurance companies have been using various data streams to predict property damage and loss for generations. But while they have been collecting increasingly large stockpiles of consumer data, until recently they have lacked the tools and talent to operationalize it -- particularly with the level of transparency required by regulatory bodies -- to drive better products and services and operational efficiencies.
Advances in AI and machine learning have enabled insurers to improve the customer experience and boost policyholder retention while cutting claims handling time and costs, eliminating fraud and protecting against cybercrime. These new tools and platforms have generated increased interest in using data science across the industry, and insurance companies have been investing accordingly.
According to a recent study
, 27% of large life/annuities insurers and 35% of large property/casualty insurers are expanding their data science efforts to some degree, while 13% of large life/annuity insurers are piloting an initiative. Midsize insurers are similarly active in the space, with 20% of life/annuity carriers and 24% of property/casualty carriers looking to expand their data science efforts.
See also: Turning Data Into Action
But while investments in AI are growing, insurance organizations are often finding that their existing analytics and business intelligence technology and talent aren’t capable of meeting their current and expanding needs. Challenges in resources, technology infrastructure and the ability to operationalize models quickly and efficiently can prevent insurers from fully leveraging AI and data science to drive business impact. To overcome these challenges, and maximize the ROI on AI investments, insurance companies must look to innovative solutions such as data science automation.
While data science is becoming a valuable tool in the insurance industry, implementing a data science program is not easy. A typical enterprise data science project is highly complex and requires the deployment of an interdisciplinary team that involves assembling data engineers, developers, data scientists, subject matter experts and individuals with other special skills and knowledge. This talent is scarce and costly. This is neither scalable nor sustainable for most insurance organizations.
Data science automation platforms
fully automate the data science process, including data preparation, feature engineering, machine learning and the production of data science pipelines - enabling insurance organizations to execute more business initiatives while maintaining the current investments and resources. Data science automation allows data scientists to focus on what to solve rather than how to solve. End-to-end data science automation makes it possible to execute data science processes faster, often in days instead of months, with unprecedented levels of transparency and accountability. As a result, insurance organizations can rapidly scale their AI/ML initiatives to drive transformative business changes.
There are several key areas where data science automation can make a big impact in the insurance industry. For increasing operational efficiency, AI-based automatic underwriting and claims management will be a major trend that we will see in coming years. In customer relationship management, AI will be used more frequently to help profile customer behaviors, helping insurers to get a better and deeper understanding of their customers’ wants and needs. This, in turn, will help to drive revenue growth.
See also: Role of Unstructured Data in AI
In the near future, data science and AI will be widely implemented in the insurance industry, and the barrier to adoption for data science and AI will become low. Once this happens, accumulated critical use cases will be key differentiators for insurance companies implementing these technologies. Data science automation accelerates the data science process, enabling insurers to explore 10X more use cases than with the traditional method of data science. Early adopters have already started to leverage automation to scale their data science initiatives.