filmov
tv
AI Disrupting Lending | Omri Yacubovich - Lama AI
Показать описание
Foundation Capital hosted our first Fintech x AI PortCo Summit in June 2023 to help executives across the Foundation Capital Portfolio answer the question “What should I be doing about AI?”
Founders and leaders from over 35 Fintech companies convened to share their learnings and gather perspectives on this essential question.
Traditionally, lending institutions have had to invest significant time, resources, and labor into cleaning and categorizing this data before feeding it into their underwriting models. LLMs, with their ability to directly process and act on unstructured data, allow Lama AI to bypass this costly preprocessing step. Lama AI is also able to continually train its LLMs with additional, domain-specific data to ensure data accuracy and better meet customer needs.
At Lama AI, LLMs have two main use cases:
First, LLMs can convert unstructured documents, such as financial statements, into structured datasets that can be used to train traditional AI underwriting models. They can also generate personalized credit memos and answer inquiries from prospects about proposed terms. Lama AI is also exploring the potential of generative AI in handling various types of data beyond text, including images, audio, and video.
Second, they enable a more streamlined application process by powering chatbots that interact with applicants in real time, guiding them through a series of questions to complete intake forms. This conversational approach makes it easier for applicants to submit the necessary information.
Turning to the challenges of building products using LLMs, Omri cautioned fellow founders about the potential for the models to hallucinate, or generate inaccurate information. To counteract this and other quality risks, Lama AI’s models are fine-tuned with verified data and user input and are further validated across specialized models and agents. These methods also address bias, privacy, and security concerns.
Founders and leaders from over 35 Fintech companies convened to share their learnings and gather perspectives on this essential question.
Traditionally, lending institutions have had to invest significant time, resources, and labor into cleaning and categorizing this data before feeding it into their underwriting models. LLMs, with their ability to directly process and act on unstructured data, allow Lama AI to bypass this costly preprocessing step. Lama AI is also able to continually train its LLMs with additional, domain-specific data to ensure data accuracy and better meet customer needs.
At Lama AI, LLMs have two main use cases:
First, LLMs can convert unstructured documents, such as financial statements, into structured datasets that can be used to train traditional AI underwriting models. They can also generate personalized credit memos and answer inquiries from prospects about proposed terms. Lama AI is also exploring the potential of generative AI in handling various types of data beyond text, including images, audio, and video.
Second, they enable a more streamlined application process by powering chatbots that interact with applicants in real time, guiding them through a series of questions to complete intake forms. This conversational approach makes it easier for applicants to submit the necessary information.
Turning to the challenges of building products using LLMs, Omri cautioned fellow founders about the potential for the models to hallucinate, or generate inaccurate information. To counteract this and other quality risks, Lama AI’s models are fine-tuned with verified data and user input and are further validated across specialized models and agents. These methods also address bias, privacy, and security concerns.