Andrew Yeoman

By: Andrew Yeoman

Andrew is CEO and Co-founder of Concirrus. He has a successful track record in telematics, big data and insurance with extensive experience of fast-growth business strategies, turnarounds, and M&A. He’s passionate about the new business models that technology can unlock for insurers and their customers. Andy loves a bit of healthy competition and is a regular competitor in the weekend Parkrun – watch out, he’s quick over 5kms!

11 Mar, 2020

DIGITALISATION, MARINE INSURANCE, INSURTECH, ANALYTICS, BIG DATA

Differentiation Through Data

Real-time data collation and interpretation has a profound effect on the insurance value chain. Connected policies enable new products and services through automation and monitoring tools. Such policies mean that those within the value chain utilise the data available to add-value to their services. Brokers can expand their consultation efforts, whilst Underwriters can constantly manage risk throughout the life of the policy. In all cases, the quality of insight is determined by the quality of the data received. The benefits of automation will not be realised unless the platform used can provide accurate insight. Digital transformation is therefore dependent on carefully considered data sources and the methods of interpretation.

There are multiple ways of implementing analytics. Concirrus’ approach is to aggregate vast datasets from trusted partners with unique technologies. When collated, proprietary tools are applied to draw behavioural insight for a better understanding of risk.

The quality of data is so important that automated cleansing methods are applied to data sets received prior to aggregation. It’s an enrichment process that is vital to ensure the data is ‘analytics ready,’ providing accurate results. With specific datasets, anomaly detection significantly outperforms the industry standard by removing twice as many anomalies from source data. The result is a combination of static, semi-static and real time information for a comprehensive view on asset activity. This means that even if we share data sources with competitors, we will have a stronger data pool.

Our partners span core data sources as well as those that allow us to validate results. Using infrastructure such as AIS can lead to anomalies due to the volume of signals broadcast within a location. Such noise can make it hard to derive asset activity. Our partnership with Spire Maritime gives us access to a global coverage of AIS data delivered through innovative and customisable APIs allowing us to track in high vessel traffic using near-earth orbit satellites. The technology is highly reliable, the satellites pick up the AIS signal then downlink it to a vast network of ground stations where it is then hosted in AWS data centres. The use of alternative technologies is one example of partners adding value to our collective dataset.

 “For data-savvy insurers, innovative algorithms and APIs are giving way to a new era of data quality and processing. To achieve differentiation through data and remain ahead of your competitors, you need a global coverage of high-quality, customisable data-sources and space-to-cloud data analytics are setting a new benchmark” -- John Lusk, GM, Spire Maritime

Other partners provide entirely new variables, such as Meteomatics’ ability to monitor weather. The effective receipt and analysis of such information adds a rich level of predictability around a highly volatile variable that directly correlates to risk. With these two partners in mind you can see how validation of asset location and associated risk can correlate. With no two datasets or partners being the same, a holistic approach ensures maximum value when applied to machine learning processes.

Further datasets include those from established providers such as IHS. With a vast network of technologies in place, data collation can be achieved at scale.

In addition to third-party data sets, Concirrus derives proprietary marine data assets for analysis and optimisation. Examples of such data assets are regional, sub region, and maritime zones, including port polygons for every port in the world.

It is important to note that whilst we aggregate data, no one dataset rules them all and all datasets are all given equal weight. Accuracy is key, so the data we base interpretation on must offer a full view and be highly detailed to reinforce conclusions. With a holistic outlook, the actions associated with connected policies have a reliable foundation, making them viable for the wider market.

For more on our connected policies blogs check out 'Real-Time Data and Connected Policies' 'Data-led Brokerageand 'Always-On Underwriting'.


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