Hi and welcome. In this video we will be exploring the importance of data extraction and the challenges and objectives driving the need for digital data extraction tools in the insurance industry.

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We’ll also look at its role in bringing structure and meaning to unstructured data and how it plays a key part in streamlining document processing and risk flows.

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What is data extraction?

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Data extraction is the process of retrieving specific data from sources and converting it into a format that can be easily analysed and used for decision-making. 

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As we explored in Module one, digital data extraction is enabling insurance professionals to gather, process and utilise critical information efficiently and accurately in order to assess risk in a more streamlined and efficient way than ever before.

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‍How Does Digital Data Extraction Work in Insurance?

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In the context of insurance underwriting or claim adjudicating, data extraction involves gathering relevant information from various documents and data sources such as broker presentations, emails, claims reports, court summons, police and hospital reports. These documents contain the essential data that risk professionals require to evaluate risks or claims accurately and make informed decisions. 

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However, the data received at this point is unstructured and relatively meaningless. This is where extraction comes in to organise data so it can be used in a meaningful way.

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With digital risk processing, the collected data is digitised, meaning it is converted into a format that can be read and processed digitally. This step often involves optical character recognition (OCR) to handle different document types.

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To achieve the full view of the risk however, the insurer needs to obtain data beyond extraction. The digitised data should be augmented with additional information from internal and external data sources. This ensures that risk professionals have a comprehensive view of the risk they are assessing. We will cover this topic in detail in the future Risk Flow Academy modules.

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How does digital data extraction work?

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By using generative AI, Large Language Models processing techniques, extraction systems can analyse the content of documents and identify key information such as names, dates, addresses, amounts and other relevant data that is crucial for determining whether a risk is within the appetite or for adjudicating a claim. 

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For example, let’s say a claim adjudicator might need to extract data from a claims document. Key data points could include claim numbers, insured names and dates of loss. Digital risk processing platforms’ can accurately and efficiently extract this data from various file types, including text-based and handwritten documents, images and scanned files. The extracted information is then structured and organised according to predefined templates or data schemas, making it easier to process and analyse.

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Why is data extraction important?

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For underwriters or claim handlers, turning multiple sources of unstructured data into an organised, digital format is transformative for a number of reasons. 

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While traditional underwriting brings a personal touch to clients, it's often a lengthy process with manual form-fills and long turnaround times. Drawing up and sending quotes can easily take days even in the most efficient manual underwriting-based operations. Automated processes significantly reduce the time required to gather and process data, allowing underwriters to respond to submissions faster. 

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Similarly in claims - it is of paramount that the communication is handled quickly so eliminating manual rekeying of data is of paramount importance.

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By extracting data accurately and efficiently, insurers can also improve their decision-making. With access to reliable and comprehensive data, insurers can make better-informed decisions about the risks they are assessing.

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Automated data extraction also enhances efficiency by reducing the time and effort required to manually gather and process information, allowing insurance professionals to focus their insight and expertise on other tasks. As we covered in Module 1, by using machine learning and artificial intelligence to digitise incoming risks, augment them with additional data and assess them accurately and efficiently, Cytora allows underwriters to receive accurate, decision-ready risks quickly.

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The Cytora platform is also able to minimise the risk of errors in data extraction, leading to more accurate risk assessments, whilst also handling large volumes of data, making it suitable for insurers of all sizes and ensures consistent data quality, which is essential for reliable underwriting decisions.

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Because of these advantages, automated extraction processes help companies deliver a better customer experience from the first contact with their customers. 

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In the next video, we’ll examine the complexities of digital data extraction in detail and how to overcome the challenges associated with it.

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