OCC AI Lab Advancements.

Optimized Transformer Architecture.

OCC is focusing on refining SLM transformer-based architecture:

We are implementing more efficient attention mechanisms to improve

performance while maintaining a small parameter count. We are using techniques like pruning to reduce

unnecessary connections in the neural network, making the model lighter and faster.

Specialized Training Data.

We are curating high-quality, domain-specific datasets for training,

similar to the MS "Code Textbook" for Phi models.

We are testing synthetic data generation techniques to expand training sets

while maintaining relevance to legal and financial claims processing.

Our platform's ability to handle various claim types allows us to implement transfer

learning techniques to adapt our base model to new claim categories quickly & efficiently.

We use ongoing fine-tuning processes to keep the model updated

with the latest regulatory requirements and claim patterns.

Given the real-time processing capabilities of our platform we are optimizing SLM

for edge computing, allowing for faster response times and improved data privacy

by facilitating claims processing directly on local devices. We are using model compression techniques to

further reduce the size of the SLM without significant loss in accuracy.

We are fine-tuning the SLM for particular tasks within all claims processes, such as document analysis, fraud detection,

and claim value estimation. We continue developing specialized versions of the SLM

for different aspects of claims processing.

We are developing our SLM with other AI technologies, such as computer vision for document processing

and specialized algorithms for financial calculations. We are implementing a hybrid approach, using SLM for most

leveraging larger models for more complex queries when necessary.

These advancements allow to maintain the most competitive edge in the legal technology sector

by providing efficient, accurate, and specialized AI solutions for claims processing.