Hi and welcome. In this video we’ll be looking more closely at a key technology in auto-classification  - Large Language Models or LLM technology. We’ll explore how Cytora applies this technology in classification to help transform digital risk processing and how LLMs offer benefits to commercial insurers worldwide.

Automated LLM-Driven Classification has enabled intelligent document management. This cutting-edge technology leverages advanced natural language processing models, like OpenAI's GPT series or Google's BERT, to automatically analyse and categorise various types of documents.

LLMs are incredibly powerful as they’ve been trained on vast datasets, enabling them to understand and interpret human language with remarkable accuracy. 

When applied to document classification, they swiftly and intelligently assess content, identify key attributes and assign documents to predefined categories or classes.

Let’s look into some of the advantages of LLM Driven Classification in detail:One of the key advantages of LLM-driven classification is its adaptability and scalability.

Unlike traditional rule-based systems, which require manual configuration and maintenance, LLMs can learn and evolve over time based on the data they are exposed to.

This means that as new document types or categories emerge, the LLM can quickly adapt to accommodate them without the need for manual intervention.

Efficiency and productivity skyrocket with LLM-driven classification. By automating the classification process, organisations can streamline their document management workflows, reduce manual effort and speed up processing times. This enhancement not only boosts operational efficiency but also allows organisations to handle a much larger volume of documents without losing speed or accuracy.

Another significant benefit is improved decision-making and insights generation. Accurate document categorisation and information extraction provide valuable insights - allowing organisations to spot trends and make more informed decisions.

LLM-driven classification represents a powerful tool to streamline document management processes, enhance efficiency and unlock new insights from data. As the technology continues to advance, its potential applications across various industries are boundless, offering transformative benefits for many businesses.

Let’s now take a look at how LLM Technology works in practice with the Cytora Classification process.

Cytora’s Classification Process involves automatically assigning documents into a taxonomy, which is basically a list of categories that the documents need to be sorted into. The taxonomy is created by the user who assigns the relevant data points.

Let’s put this into context. When handling a claim, you might need to extract data points like policy number, claim number, or the insurer’s number, depending on what information is required.

It’s always preferable to classify documents before extraction for several reasons. By knowing the type of document you’re dealing with, you can then target specific data more efficiently. 

For example, take a document like an accident report. Drivers' names are typically found in these, making it easier to extract them accurately 

Conversely, court documents are unlikely to contain claim reference numbers, but you may find them in hospital reports.

These fields are fundamental data points necessary for accurately processing and assessing insurance claims. 

In the next video we’ll explore how these data points inform triage and the role it plays in classification.

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