Hi, and welcome. In this video we’ll be exploring the best practices and approaches for implementing data extraction to enhance efficiency and accuracy of insurance operations, and decision making.

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In the previous video, we addressed the complexity of data extraction in insurance operations, recognising a number of strategies and tools that can be used to handle and organise the unstructured data that’s initially received in at the beginning of the digital risk processing.

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However, before any data can be extracted and organised in a meaningful way, there are a number of strategies that need to be in place before an effective and accurate extraction process can begin.

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Client collaboration and creating a defined schema

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Firstly, it is crucial that clients establish clear definitions and accuracy testing for classification and extraction. This collaboration is essential because it ensures that the classification and extraction processes are aligned with the client’s specific goals and objectives, so it’s best practice that they are able to share a well defined schema.

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A schema, which we will explore in more detail further on, is simply the information the client/an insurer needs to extract from the various documents that have been provided in order for them to make a decision on the risk.

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Let’s take a look at a couple of different types of schema that might be used:

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A basic schema would be fairly straightforward - the insurer may only want to extract essential data points such as the name, address, renewal date and target premium. 

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An Advanced Schema would be more complex. For example, take property insurance. This might require additional details such as the insured address, specifics of what’s insured, stock values and the business and building insurer.

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The complexity of the schema will depend on the line of business and the specific requirements of the data extraction process, but it’s important to note that the more detailed the schema, the more precise the data extraction needs to be. 

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This is why, when planning your data extraction strategy, it’s crucial to start with a small set of data to ensure accuracy as a larger schema can often lead to less accurate results. So it's best practice that the client defines which data is mandatory and which data is less important. 

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Pilot Testing for accuracy 

Extracted field data is a valuable resource, facilitating deeper insights and analysis once turned from unstructured data through extraction and validation, which we’ll look at more closely in Module 4.

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By leveraging this extracted data, clients can uncover trends, patterns and actionable insights that drive informed decision-making. We’ll explore this further in the next video.

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This is why it’s crucial to test the model to evaluate check accuracy. As mentioned with basic and advanced schemas, It's best practice to start on a small scale. This allows any issues to be identified and necessary adjustments can be made before a full-scale deployment.

When looking at a claim, for example there could be 100 various data points you could look for but only one police reference number. By looking for one particular data point it works as the ‘ground truth’, as we mentioned in Module 2. 

As Cytora applies an Agile methodology involving iterative development, continuous improvement of the data and incremental updates, any inaccuracies are swiftly addressed, ensuring the integrity and reliability of the extracted data.

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Integration with existing systems 

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Ensuring that the data extraction solution integrates seamlessly with an organisation’s systems and workflows is another key consideration when trying to effectively deploy data extraction. Software and systems must be modern and up to date to be able to extract data in an effective and meaningful way as well as to help minimise disruption and enhance user adoption. 

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Effective deployment of data extraction solutions requires clear objectives, defined success criteria and strategic approaches. By pilot testing and continuously monitoring performance, insurers can achieve significant improvements in underwriting efficiency and accuracy.

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In our next video, we’ll delve deeper into the functionalities and capabilities required for digitising and deploying digital risk flows effectively, providing insights into how digital data extraction platforms facilitate a seamless and integrated process.

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