---
title: AI in Finance: Data and Opportunities
url: https://www.ml-quant.com/papers/repec/eee-pacfin-v-84-y-2024-i-c-s0927538x24000581/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:eee:pacfin:v:84:y:2024:i:c:s0927538x24000581
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0927538X24000581%3Bh%3Drepec%3Aeee%3Apacfin%3Av%3A84%3Ay%3A2024%3Ai%3Ac%3As0927538x24000581
featured: 2024-04-17
citations: unknown
topic: ML & AI Methods
---


# AI in Finance: Data and Opportunities

Data and Opportunities: The article highlights the significant role of big data and AI in the finance industry, suggesting a blend of financial knowledge and data analytics for improved financial systems.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0927538X24000581%3Bh%3Drepec%3Aeee%3Apacfin%3Av%3A84%3Ay%3A2024%3Ai%3Ac%3As0927538x24000581
- Identifier: RePEc:eee:pacfin:v:84:y:2024:i:c:s0927538x24000581
- Released: 2024-04-17
- First featured: Quant Letter No. 45 (2024-04-17): https://www.ml-quant.com/issues/2024-04-17/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: ML & AI Methods

## Related

- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://www.ml-quant.com/papers/arxiv/2312.00752/): Sequence Modeling: Mamba, a neural network architecture that doesn't use attention or MLP blocks, provides faster inference and better performance in language, audio, and genomics than Transformers.
- [Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality](https://www.ml-quant.com/papers/arxiv/2405.21060/): The research identifies a link between state-space models and Transformers in deep learning, leading to the creation of a faster language modeling architecture, Mamba-2.
- [Octo: An Open-Source Generalist Robot Policy](https://www.ml-quant.com/papers/arxiv/2405.12213/): Octo is a large transformer-based policy for robotic manipulation, trained on a vast dataset, that can be instructed via language or images and adapted to new domains.
- [Mastering Diverse Domains through World Models](https://www.ml-quant.com/papers/arxiv/2301.04104/): Algorithm Mastery: DreamerV3, a universal algorithm, excels in over 150 varied tasks, including diamond collection in Minecraft without human input, expanding the scope of reinforcement learning.
- [Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction](https://www.ml-quant.com/papers/arxiv/2404.02905/): The article discusses Visual AutoRegressive modeling (VAR), a new image learning method that outperforms diffusion transformers in terms of speed, image quality, and scalability.
- [SimPO: Simple Preference Optimization with a Reference-Free Reward](https://www.ml-quant.com/papers/arxiv/2405.14734/): Simple Preference Optimization: SimPO improves reinforcement learning from human feedback by using the average log probability of a sequence as the implicit reward, enhancing training stability and computational efficiency.