Leveraging Financial Data with Big Data Tools or Generative AI - 59th Hawaii International Conference on System Sciences (HICSS)
Wednesday, February 26 – Sunday, June 15, 2025
CALL FOR PAPERS

Decision Analytics and Service Science (Track)

Leveraging Financial Data with Big Data Tools or Generative AI (Mini-Track)

HICSS-59

Jan 6, 2025 - Jan 9, 2025

Hyatt Regency Maui, Hawaii, USA

Dear colleagues,

You are cordially invited to submit research papers to the Leveraging Financial Data with Big Data Tools or Generative AI mini-track.

HICSS is known worldwide as one of the longest-standing working scientific conferences in Information Technology Management. HICSS provides a highly interactive working environment for top scholars from academia and industry from over 60 countries to exchange ideas in various areas of business, technology, and system sciences. Papers published in HICSS proceedings have been cited extensively across a spectrum of disciplines for many years.

Financial markets have a long history of regulation, requiring public companies to disclose information to government agencies. Over the past two decades, regulators have increased measures to democratize financial information and adopted standardized data reporting formats such as XBRL to make it easier for the average investor to analyze company data. In the United States, the Securities and Exchange Commission (SEC) provides access to these structured datasets on its website (SEC Markets Data).

One of the largest and most detailed datasets available is the SEC’s Structured Financial Statements and Notes Data Set (link), which exceeds 230 GB of .tsv files and is also accessible via the EDGAR API. However, due to its size and the complexity of XBRL tags, extracting meaningful insights from this dataset presents significant challenges. As a result, many researchers still rely on proprietary financial databases such as Compustat and Wharton Research Data Services (WRDS). While proprietary databases offer convenience, they lack transparency regarding the source of financial figures, making it difficult to audit and replicate research findings. In contrast, publicly available datasets provide researchers with auditable data, fostering reproducibility and open inquiry.

Over the past decade, advancements in big data tools (e.g., Pandas, R, DuckDB, Malloy) and generative AI (e.g., ChatGPT) have made it easier to analyze large datasets, such as the SEC’s Structured Financial Statements and Notes Data Set. Artificial intelligence (AI) has advanced rapidly, driven by sharp increases in commercial investment. A striking example is the swift development and deployment of large language models (LLMs). AI is already transforming financial services, presenting both vast opportunities and potential risks to economic and financial stability. Recent debates highlight concerns such as existential threats and widening societal disparities. However, these tools can help level the playing field for individual investors.

We invite empirical, theoretical, and experimental papers exploring AI’s opportunities and risks in finance, accounting, and fin-tech. We encourage researchers to explore publicly available datasets and leverage modern analytical tools to generate novel and reproducible insights into financial markets and regulation. Our goal is to enhance understanding of how firms, investors, and other market participants use—or could use—AI and big data techniques, as well as the broader societal and regulatory implications.

Potential issues and topics include, but are not limited to:

Use of LLMs in financial statement analysis

AI for understanding economic data

Survey of AI techniques used by financial professionals

Quantitative analysis of risk factors or litigation disclosures

Comparative analysis of different data analysis tools

Analyzing financial restatements with AI

Using LLMs to understand board characteristics

ESG-related disclosures

Replacing proprietary data sources with free alternatives

AI in corporate finance

AI in trading and asset management

AI in banking and credit

AI in financial forecasting

AI in consumer finance

AI in fraud detection

Macroeconomic and market effects

Regulatory challenges: frictions, market failures, and policy solutions

Exploratory data analysis using SQL or other tools

Using data pipelines in research

Tools for cleaning and processing XBRL data

AI for social impact

Important Dates for Paper Submission

June 15, 2025 | 11:59 pm HST: Paper submission deadline

August 17, 2025, | 11:59 pm HST: Notification of acceptance/rejection

September 22, 2025, |11:59 pm HST: Deadline for authors to submit final manuscript for publication

October 1, 2025, | 11:59 pm HST: Deadline for at least one author of each paper to register

** Author Instructions**

HICSS accepts full papers only; abstract submissions are not accepted.

Joseph Johnston, PhD

Professor and Department Chairperson

Department of Accounting and Business Information Systems

Illinois State University

jajohn6@ilstu.edu

Timothy Olsen, Ph.D.

Associate Professor, Information Systems

School of Business Administration

Gonzaga University

olsent@gonzaga.edu

Location 200 Nohea Kai Dr, Lahaina, HI 96761
Country USA
Event Type Call for Paper, Conference
Call for Papers Deadline 06-15-2025
Submission Cost (USD) 0
Host Institution University of Hawaii at Manoa
Registration Link hicss.hawaii.edu
Link hicss.hawaii.edu