Hi and welcome. In this video we’ll explain the concept, challenges and objectives of Classification when using the Cytora platform and its integration into insurance operations. 

We will define the role of classification in document management and explain how it contributes to workflow efficiency. We will also look at the problems it attempts to solve, its benefits and the importance of user interaction when using auto-classification.

The term, Classification refers to the process of categorising or assigning items or data points into predefined groups or classes based on their attributes, characteristics or features. 

It is a fundamental task in various fields, including machine learning, data mining and pattern recognition. In insurance operations, document classification is a key component of risk digitisation. Historically, this is a process which underwriters have completed manually for documents like accident reports or court summons to ensure they are processed correctly and efficiently.

The Cytora platform uses automated classification to digitally categorise documents and data and by doing so it solves two key issues:

The manual classification of documents, such as claims and policy applications, is performed by skilled individuals, but can result in delays and inefficiencies.

While a human could likely classify a single document in seconds, scaling this process without incurring significant operational costs is impractical. By digitising classification, the process can be accelerated, enabling the immediate classification of multiple documents in seconds instead of hours, reducing processing delays for new submissions. This digitised approach effectively enables scalability, turning what would be a limitation into a significant advantage.

Some key data points will only be present in certain documents so it is crucial to understand how a document has been classified to efficiently locate the relevant data.  

Imagine an email with ten documents attached, each needing different handling. Cytora can swiftly classify and direct them to the respective teams, streamlining document management.

For example, a claims adjuster might need to extract specific details from a claims report, such as accident dates, policy numbers, or claimant information. By knowing that a document has been classified as a claims report, the platform can quickly and accurately locate these critical data points without sifting through irrelevant information. 

This targeted approach allows for more precise data retrieval, enhancing overall efficiency and accuracy in data processing.

While automated classification solves these issues, it’s important to remember that ultimately, you are in control of the configuration, meaning that the model will only output what it is asked to.

Automated systems can classify documents and target specific data points at pace but users of the platform are a critical quality control layer that ensures accuracy and reliability.

When an automated system encounters a document that doesn't fit the predefined categories, human expertise is needed to correctly classify the document and extract the necessary data. This collaboration between human expertise and technology creates a more robust and reliable data processing workflow.

Auto-classification presents a transformative solution for insurance organisations aiming to enhance their document management processes. By utilising this technology to categorise and sort documents, businesses can unlock heightened levels of efficiency, productivity and scalability.

By automating the labour-intensive task of sorting and categorising documents, organisations can free up valuable resources for other tasks, allowing for greater efficiency and productivity.

By accurately categorising documents, organisations can streamline the extraction of relevant information, ensuring that critical data points are identified and processed efficiently. 

By automating the classification process, organisations can handle larger volumes of documents without the need for additional resources. This scalability is particularly advantageous in industries experiencing growth or facing fluctuating workloads, for example, renewal periods or catastrophic events.

In summary, the auto-classification process serves two main purposes:

1) Facilitating efficient backend operations by directing documents to the appropriate recipients and locations accurately the first time. 

2) Supporting the targeted identification of specific data points, which helps streamline risk intake and informs downstream decision-making processes.

Cytora drives this process by using Large Language Models (LLM) technology to swiftly yield positive results, so high quality classification can now be accomplished by the platform without training data being required.

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We’ll go onto explore LLMs in more detail in the next video.