Young Choi
Sandeep Shrivastava
When most stories about AI in finance begin, they start with financial firms introducing AI to gain an edge. But innovation rarely follows a straight line - as demonstrated by LG AI Research.
Their journey flips the typical narrative, showing how breakthroughs born on the factory floor can redefine what’s possible in financial markets:
- From solving high stakes manufacturing challenges, LG AI Research built AI systems where accuracy, reliability, and explainability were essential.
- Those same principles later powered a shift into finance, where they blended time series forecasting with language understanding to mirror how real analysts think.
- This insight explores how that foundation led to innovations like AI Powered Equity Forecast Score, marking a new era where financial AI delivers both predictions and the reasoning behind them.
Manufacturing first
Founded in December 2020, LG AI Research was created to strengthen LG Group’s AI capabilities across its global businesses. With more than 300 scientists situated in multiple labs, the team initially focussed on solving some of the toughest challenges in manufacturing: supply and demand forecasting, raw material dynamics, scheduling optimisation, and learning from noisy time-series data that shifts as real-world conditions change.
In manufacturing. reliability isn’t optional. AI must run at scale, support sourcing and planning decisions - and deliver accuracy under governance constraints. Explainability on top of accuracy adds immense value – because when systems drive operations, stakeholders need to understand what the model sees and why it reacts.
LG AI Research mastered these principles early, delivering accuracy, governance and explainability across LG Group’s multiple mega-enterprise entities. That foundation set the stage for a bold new chapter.
Why finance?
After success in manufacturing, LG AI Research turned to Financial Services – a domain rich in data, but equally demanding in trust. The team recognised that financial workflows share similar priorities: accuracy, governance and explainability. But they also learned something critical: forecasting alone doesn’t earn trust.
Investors act on numbers and narrative. News, disclosures, and research can shift expectations faster than fundamentals appear in reported data. To address this, LG AI Research leveraged its in-house large language model EXAONE, to interpret unstructured news and combine it with structured time-series signals – closer to how experienced analysts reason.
Explainability at the core
Two persistent frictions in financial AI are hallucinations and black-box decision making. If a model can’t justify its conclusions in human terms, it’s hard to govern – and harder to use. LG’s approach treated explanation as part of the system, not an optional overlay. Every output was designed to translate into decision-ready language.
The philosophy powered LG’s entry into M6 Financial Forecasting Competition in early 2023, where the team place in three categories. That success led to a collaboration with Qraft Technologies and the launch of the LQAI ETF, a public testbed where AI ranks a US large-cap universe, selects 100 stocks, rebalances every four weeks, and explains its rationale in natural language.
From prediction to decision support
In September 2025, LG AI Research and LSEG announced the commercialisation of EXAONE Business Intelligence – a financial AI agent designed to produce both predictions and explanations. The multi-agent system curates new and disclosures, forecasts economic prospects, generates equity analysis, and evaluates decision scenarios. These outputs are processed into the AI-Powered Equity Forecast Score, now available to institutional investors globally.
The bigger picture
Institutional clients increasingly demand AI that can synthesise structured data and unstructured data, operate at scale, and communicate rationale clearly enough for portfolio, risk, and compliance teams to interrogate. This isn’t just about beating benchmarks – it’s about earning trust through transparency.
LG AI Research’s journey suggests that durable financial AI may come from teams trained outside of markets, in environments where reliability is non-negotiable and systems must work end-to-end. The path from factory floor constraints to market forecasting isn’t a detour and might just be a blueprint.
Legal Disclaimer
Republication or redistribution of LSE Group content is prohibited without our prior written consent.
The content of this publication is for informational purposes only and has no legal effect, does not form part of any contract, does not, and does not seek to constitute advice of any nature and no reliance should be placed upon statements contained herein. Whilst reasonable efforts have been taken to ensure that the contents of this publication are accurate and reliable, LSE Group does not guarantee that this document is free from errors or omissions; therefore, you may not rely upon the content of this document under any circumstances and you should seek your own independent legal, investment, tax and other advice. Neither We nor our affiliates shall be liable for any errors, inaccuracies or delays in the publication or any other content, or for any actions taken by you in reliance thereon.
Copyright © 2025 London Stock Exchange Group. All rights reserved.
The content of this publication is provided by London Stock Exchange Group plc, its applicable group undertakings and/or its affiliates or licensors (the “LSE Group” or “We”) exclusively.
Neither We nor our affiliates guarantee the accuracy of or endorse the views or opinions given by any third party content provider, advertiser, sponsor or other user. We may link to, reference, or promote websites, applications and/or services from third parties. You agree that We are not responsible for, and do not control such non-LSE Group websites, applications or services.
The content of this publication is for informational purposes only. All information and data contained in this publication is obtained by LSE Group from sources believed by it to be accurate and reliable. Because of the possibility of human and mechanical error as well as other factors, however, such information and data are provided "as is" without warranty of any kind. You understand and agree that this publication does not, and does not seek to, constitute advice of any nature. You may not rely upon the content of this document under any circumstances and should seek your own independent legal, tax or investment advice or opinion regarding the suitability, value or profitability of any particular security, portfolio or investment strategy. Neither We nor our affiliates shall be liable for any errors, inaccuracies or delays in the publication or any other content, or for any actions taken by you in reliance thereon. You expressly agree that your use of the publication and its content is at your sole risk.
To the fullest extent permitted by applicable law, LSE Group, expressly disclaims any representation or warranties, express or implied, including, without limitation, any representations or warranties of performance, merchantability, fitness for a particular purpose, accuracy, completeness, reliability and non-infringement. LSE Group, its subsidiaries, its affiliates and their respective shareholders, directors, officers employees, agents, advertisers, content providers and licensors (collectively referred to as the “LSE Group Parties”) disclaim all responsibility for any loss, liability or damage of any kind resulting from or related to access, use or the unavailability of the publication (or any part of it); and none of the LSE Group Parties will be liable (jointly or severally) to you for any direct, indirect, consequential, special, incidental, punitive or exemplary damages, howsoever arising, even if any member of the LSE Group Parties are advised in advance of the possibility of such damages or could have foreseen any such damages arising or resulting from the use of, or inability to use, the information contained in the publication. For the avoidance of doubt, the LSE Group Parties shall have no liability for any losses, claims, demands, actions, proceedings, damages, costs or expenses arising out of, or in any way connected with, the information contained in this document.
LSE Group is the owner of various intellectual property rights ("IPR”), including but not limited to, numerous trademarks that are used to identify, advertise, and promote LSE Group products, services and activities. Nothing contained herein should be construed as granting any licence or right to use any of the trademarks or any other LSE Group IPR for any purpose whatsoever without the written permission or applicable licence terms.
