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When DeepSeek-R1 meets financial applications: benchmarking, opportunities, and limitations
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Shuoling LIU1,2, Liyuan CHEN2, Jiangpeng YAN2,4, Yuhang JIANG2, Xiaoyu WANG2, Xiu LI4, Qiang YANG1. When DeepSeek-R1 meets financial applications: benchmarking, opportunities, and limitations[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .
@article{title="When DeepSeek-R1 meets financial applications: benchmarking, opportunities, and limitations",
author="Shuoling LIU1,2, Liyuan CHEN2, Jiangpeng YAN2,4, Yuhang JIANG2, Xiaoyu WANG2, Xiu LI4, Qiang YANG1",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2500227"
}
%0 Journal Article
%T When DeepSeek-R1 meets financial applications: benchmarking, opportunities, and limitations
%A Shuoling LIU1
%A 2
%A Liyuan CHEN2
%A Jiangpeng YAN2
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%A Yuhang JIANG2
%A Xiaoyu WANG2
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%A Qiang YANG1
%J Journal of Zhejiang University SCIENCE C
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%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2500227
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A1 - Yuhang JIANG2
A1 - Xiaoyu WANG2
A1 - Xiu LI4
A1 - Qiang YANG1
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DOI - 10.1631/FITEE.2500227
Abstract: The ways in which the recent progresses of reasoning large language models (LLMs), especially the new open-source model DeepSeek-R1, can benefit financial services is an underexplored problem. While LLMs have ignited numerous applications within the financial sector, including financial news analysis and general customer interactions, DeepSeek-R1 further unlocks the advanced reasoning ability with multiple reinforcement learning–integrated training steps for more complex financial queries and provides distilled student models for resource-constrained scenarios. In this paper, we first introduce the technological preliminaries of DeepSeek-R1. Subsequently, we benchmark the performance of DeepSeek-R1 and its distilled students on two public financial question–answer datasets as a starting point for interdisciplinary research on financial artificial intelligence (AI). Then, we discuss the opportunities that DeepSeek-R1 offers to current financial services, its current limitations, and three future research directions. In conclusion, we argue for a proper approach to adopt reasoning LLMs for financial AI
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