LLM Integration Special Report 2024

Models for Finance Report

Learn about how, by using CompatibL AI, institutions and banks can overcome the limits of large language models (LLMs) and improve the accuracy, computational efficiency, and handling of natural language documents while maintaining human control.

CompatibL recognized early on that the emergence of modern LLMs provided an opportunity to automate the comprehension, validation, and generation of complex natural language documents.

CompatibL’s R&D team developed novel prompt-engineering and fine-tuning techniques to achieve optimal LLM performance for each document category. These innovations enable CompatibL AI to assist with time‑consuming and tedious tasks in trading, risk management, and quant research, with greater accuracy and competence than out-of-the-box LLMs.

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    Alexander Sokol, CompatibL

    Alexander Sokol

    Executive Chairman & Head of Quant Research

    In 2022, Alexander was awarded the Fintech Person of the Year Award for his expertise and developments in a new class of machine learning risk models that can work with short pandemic-era historical time series. In 2018 Alexander won the Quant of the Year Award, together with Leif Andersen and Michael Pykhtin, for their joint work revealing the true scale of the settlement gap risk that remains even in the presence of initial margin. Alexander’s other notable research contributions include systemic wrong-way risk (with Michael Pykhtin, Risk Magazine), joint measure models, and the local price of risk (with John Hull and Alan White, Risk Magazine), and mean reversion skew (Risk Books, 2014).

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