From online retail to stock exchange trading, the efficient transfer of high quality and relevant data has a critical and immediate impact on revenue streams in global industries.
Data quality and oversight has long been a boardroom topic at pioneering companies like Amazon and Tesla, and is also a critical factor for all insurtech and fintech start-ups.
The reality is that every company operating today is a data company. But while the traditional re/insurance sector is alert to this fact, due to the complexity of the market the direct link between streamlined data processing and improvements to the bottom line is not always clear.
Like many other sectors, insurance companies often handle a myriad of incoming and outgoing data sources. The consequences of not managing data cleansing effectively across these systems can be significant and they reverberate throughout the re/insurance chain.
In particular, data cleansing for downstream analytics is an area that is in need of innovation. Poor catastrophe inputs impact underwriting, reinsurance, claims/event response and business planning to name but a few.
From data ingested into risk models, to analysing statement of values (SoV) reports for property portfolios, too often these data processes are still handled manually in the re/insurance industry, and it’s completely unnecessary.
The tools are there to work smarter, not harder. This is where augmented intelligence comes into play. CAI is an augmented software platform, which means it uses a learning algorithm – once a human has shown it how to do something, it does not need to be told again.
Configurable outputs provide inputs into a re/insurer’s data streams and it extrapolates its learning across its applications to make every task more efficient and timely while avoiding unnecessary distractions. It cleanses unstructured, low quality data – turning it into something far more valuable.
We’ve worked with many re/insurers to demonstrate how better use of quality data can support superior risk selection – and better and more accurate risk pricing – while also allowing for the redeployment of skilled staff into value-added activities.
Ultimately. viewing data streams through the lens of their direct impact on revenue streams is essential to sharpening the focus on how important data is at a business.