Organizations that are making the most of their analyses are looking beyond traditional data sources for insights. The increased digitization of data means there's more information than ever before to consider, both within and outside of company databases.
In auto claims, for instance, machine learning algorithms can be trained on hundreds of thousands of images of vehicle damage to assess claims. This allows customers to start the assessment process remotely, and gives insurers another resource to identify claims trends. By looking at alternative or unstructured data sources, today's insurers can learn a great deal about their customers' preferences, how claims trends change in relation to lifestyle choices, and new areas that will need coverage in the future.
With today's technology capabilities, even data that was previously considered too noisy or difficult can become a vital resource. Envision Racing found something similar this year with race GPS data. Despite housing valuable insights into drivers' performances, this unstructured data had previously gone underutilized due to its more disorganized nature.
By using the latest AI and analytics tools to carefully clean and filter this data, the teams found that it provides valuable insights into driver tendencies. Customer invoices, GPS data, claims documents and social media could all be hiding similar insights that would help insurers improve pricing and identify new insurance product trends.