Enterprise Knowledge Retrieval Using LLMs and Vector Databases in Financial Services

Abstract

This study examines how Large Language Models (LLMs) can be used in conjunction with a database of vectors to enhance knowledge retrieval by enterprises involved in housing financial services. The study also shows the massive enhancement of the speed of data retrieval and accuracy in valuing properties by these technologies. Particularly, AI-driven systems shortened the July process to as little as 1 second, 90% less time than 3-5 seconds to respond to queries, which considerably improved operational efficiency. The accuracy in property price prediction increased by 15%, with AI models reaching 92% accuracy, in contrast to 85% with the traditional methods. These systems are assessed on the basis of real-life housing market data by the study, which examines the systems in terms of influencing decision-making speed, predicting reliability, and also the cost of operation. The results indicate that not only do the LLMs and the vector databases improve decision-making as they allow the faster and more precise retrieval of the data, but also the operational costs will decrease by a quarter because of reduced manual input. The implications of such advantages on financial institutions are widespread, which means that the application of AI to the housing finance sector can optimize resource allocation and reduce risks, and enhance customer satisfaction. The future Trends in AI, scalability, and the question of the ethics of AI implementation in the financial services would be applicable in future work.