INSURANCE
Quantexa ’ s Alex Johnson continues : “ For the overwhelming majority of insurers , the key challenge is generating a contextual , connected view of this data – or , in other words , a single customer profile . Insurance organisations that can fully leverage ML and DL techniques are typically those that have looked to resolve a holistic customer view first .”
In its latest State of AI in Financial Services report , Quantexa found that a third of respondents in financial institutions ranked data readiness and the ability to integrate internal and external data sources into a single source of truth as key challenges for AI adoption .
“ Despite the opportunities in front of the industry , insurance ’ s adoption of ML and DL has been fairly slow ,” says Hedvig ’ s John Ardelius . “ Incumbent players are still struggling to make the best use of their data due to its distributed and unstructured nature .”
JOHN ARDELIUS HEDVIG
According to Ardelius , increased adoption will result in more opportunities for selfservice and tailoring of insurance products to smaller segments of users , but warns that there is a need for collaboration and sharing of anonymised structured data across companies .
What can insurers do to accelerate uptake of ML and DL ? “ There are a few key actions that insurance firms might take to improve data readiness ,” says Alex Johnson . “ The critical first step is to ensure that single views of both retail personal lines and commercial lines customers are available across all brands , channels , and internal data systems .
“ Then an integration of the external data assets , which are used throughout the value-chain by operational teams , should be embedded within this in order to enrich the internal data and build a bigger picture .
“ Next is to ensure that the full context is uncovered by using solutions that automatically build upon relationships and network associations through knowledge graphs across the data . This will provide a solid foundation to ensure that AI models are maximising their accuracy .
“ As well as this , there is a cultural aspect to the adoption of analytical techniques . Insurance companies should also ask themselves if their C-suite has the appetite for a transformative AI ‘ moon shot ’ or if there is more focus on using AI to capture low-hanging fruit – easy-to-accomplish applications that will deliver short-term value . If a company doesn ’ t have the necessary data science and analytics capability in-house , then they will need to enlist a network of service providers . On the other hand , if a firm expects to be implementing longer-term AI projects , it will need to recruit expert talent .”
68 November 2022