AI is a included in most insurer's strategies whilst they are typically unable to surface and make "AI ready" the data vital to digital transformation.
Ask yourselves a simple question.
What percentage of the data stored in silos inherited through the years of M&A, can you access?
It is unlikely to be more than 20% and may be as low as 10%. That makes any ambitions to leverage AI fully doomed to disappointment.
First work on the details of master data management and access all the data required. 360Retrieve is a vital tool to leverage the data
- Gathered in every claim
- Found in emails, scanned mail, medical reports, CNFs
- Hidden in metadata
- Found in diverse formats and sources: -
- External data sources
You are now in the first step of having data "AI Ready" to help achieve the goals you set to leverage AI.
Whilst you should plan to leverage AI be sure to explain WHY. It is not enough to say the reason is because 72% of insurers rate AI as a priority to reduce costs and automate.
You may wish to automate the process of automating the process of assessing damage to vehicles in auto claims by using AI to assess images of the damage. But much of the damage is hidden so images are of limited value unless augmented with the evidence of impact speed, the actual spec of all vehicles involved, the points of contact and so on.
What starts as a simple solution turns out far more complex.
It is important to plan the role of AI, machine learning and RPA but it will take longer and cost more than you think. See "DeepMind’s losses display the challenges of the AI industry".
In the meantime you will make a greater impact on improving the combined ratio, reducing claims costs and increasing customer satisfaction by deploying the digital claims platform that has already processed 3.6 million claims. See the proven improvements .
Deploying 360Siteview will free up cash to significantly help you pay for the investment necessary in AI and with its API architecture integrate the best solutions from industry leaders over the next two to three years.
There is considerable interest from policymakers and scientists around the world around how artificial intelligence is going to transform their work. In their haste to jump on the AI bandwagon, however, everybody is forgetting we have not solved some older, deeper problems about data that will stymie attempts to get the technology off the ground.