Stay informed with our comprehensive Financial News Dataset, designed for investors, analysts, and businesses to track market trends, monitor financial events, and make data-driven decisions.

Dataset Features

Financial News Articles: Access structured financial news data, including headlines, summaries, full articles, publication dates, and source details. Market & Economic Indicators: Track financial reports, stock market updates, economic forecasts, and corporate earnings announcements. Sentiment & Trend Analysis: Analyze news sentiment, categorize articles by financial topics, and monitor emerging trends in global markets. Historical & Real-Time Data: Retrieve historical financial news archives or access continuously updated feeds for real-time insights.

Customizable Subsets for Specific Needs Our Financial News Dataset is fully customizable, allowing you to filter data based on publication date, region, financial topics, sentiment, or specific news sources. Whether you need broad coverage for market research or focused data for investment analysis, we tailor the dataset to your needs.

Popular Use Cases

Investment Strategy & Risk Management: Monitor financial news to assess market risks, identify investment opportunities, and optimize trading strategies. Market & Competitive Intelligence: Track industry trends, competitor financial performance, and economic developments. AI & Machine Learning Training: Use structured financial news data to train AI models for sentiment analysis, stock prediction, and automated trading. Regulatory & Compliance Monitoring: Stay updated on financial regulations, policy changes, and corporate governance news. Economic Research & Forecasting: Analyze financial news trends to predict economic shifts and market movements.

Whether you're tracking stock market trends, analyzing financial sentiment, or training AI models, our Financial News Dataset provides the structured data you need. Get started today and customize your dataset to fit your business objectives.

The data sets provide the text and detailed numeric information in all financial statements and their notes extracted from exhibits to corporate financial reports filed with the Commission using eXtensible Business Reporting Language (XBRL).

Access Company Financial Data for banking and capital markets professionals in the Middle East with Success.ai. Gain verified profiles from 170M+ datasets, including email addresses, phone numbers, and decision-maker insights. Best price guaranteed.

Purpose and scope

This dataset evaluates LLM reasoning over structured financial knowledge. It tests an LLM’s ability to interpret and apply foundational concepts in corporate finance, based on the open textbook Introduction to Financial Analysis by Dr. Kenneth Bigel.

  Dataset Creation Method

The benchmark was created using RELAI’s data agent. For more details on the methodology and tools used, please visit relai.ai.

  Example Uses

The benchmark can be used to… See the full description on the dataset page: https://huggingface.co/datasets/relai-ai/financial-scenarios.

About the Dataset This dataset contains financial transaction records and risk management data for accounting systems. It includes a variety of transactional data, such as transaction IDs, amounts, categories, and payment methods, alongside associated risk incidents like fraud, errors, and misstatements. The dataset also captures system metadata, such as user activity, transaction processing time, login frequency, and geographical region of the IP. The data is designed to simulate real-world accounting system operations and risk events, enabling the development and testing of AI-driven risk prediction models. The dataset can be used for research in real-time financial risk management, fraud detection, and improving decision-making processes in accounting systems using artificial intelligence.

For a visual depiction of GSA's Balance Sheet and Statement of Net Cost, please use the interactive charts to view the financial results for fiscal years 2007-2013.

Success.ai offers a cutting-edge solution for businesses and organizations seeking Company Financial Data on private and public companies. Our comprehensive database is meticulously crafted to provide verified profiles, including contact details for financial decision-makers such as CFOs, financial analysts, corporate treasurers, and other key stakeholders. This robust dataset is continuously updated and validated using AI technology to ensure accuracy and relevance, empowering businesses to make informed decisions and optimize their financial strategies.

Key Features of Success.ai's Company Financial Data:

Global Coverage: Access data from over 70 million businesses worldwide, including public and private companies across all major industries and regions. Our datasets span 250+ countries, offering extensive reach for your financial analysis and market research.

Detailed Financial Profiles: Gain insights into company financials, including revenue, profit margins, funding rounds, and operational costs. Profiles are enriched with key contact details, including work emails, phone numbers, and physical addresses, ensuring direct access to decision-makers.

Industry-Specific Data: Tailored datasets for sectors such as financial services, manufacturing, technology, healthcare, and energy, among others. Each dataset is customized to meet the unique needs of industry professionals and analysts.

Real-Time Accuracy: With continuous updates powered by AI-driven validation, our financial data maintains a 99% accuracy rate, ensuring you have access to the most reliable and up-to-date information available.

Compliance and Security: All data is collected and processed in strict adherence to global compliance standards, including GDPR, ensuring ethical and lawful usage.

Why Choose Success.ai for Company Financial Data?

Best Price Guarantee: We pride ourselves on offering the most competitive pricing in the industry, ensuring you receive unparalleled value for comprehensive financial data.

AI-Validated Accuracy: Our advanced AI algorithms meticulously verify every data point to ensure precision and reliability, helping you avoid costly errors in your financial decision-making.

Customized Data Solutions: Whether you need data for a specific region, industry, or type of business, we tailor our datasets to align perfectly with your requirements.

Scalable Data Access: From small startups to global enterprises, our platform caters to businesses of all sizes, delivering scalable solutions to suit your operational needs.

Comprehensive Use Cases for Financial Data:

1. Strategic Financial Planning:

Leverage our detailed financial profiles to create accurate budgets, forecasts, and strategic plans. Gain insights into competitors’ financial health and market positions to make data-driven decisions.

1. Mergers and Acquisitions (M&A):

Access key financial details and contact information to streamline your M&A processes. Identify potential acquisition targets or partners with verified profiles and financial data.

1. Investment Analysis:

Evaluate the financial performance of public and private companies for informed investment decisions. Use our data to identify growth opportunities and assess risk factors.

1. Lead Generation and Sales:

Enhance your sales outreach by targeting CFOs, financial analysts, and other decision-makers with verified contact details. Utilize accurate email and phone data to increase conversion rates.

1. Market Research:

Understand market trends and financial benchmarks with our industry-specific datasets. Use the data for competitive analysis, benchmarking, and identifying market gaps.

APIs to Power Your Financial Strategies:

Enrichment API: Integrate real-time updates into your systems with our Enrichment API. Keep your financial data accurate and current to drive dynamic decision-making and maintain a competitive edge.

Lead Generation API: Supercharge your lead generation efforts with access to verified contact details for key financial decision-makers. Perfect for personalized outreach and targeted campaigns.

Tailored Solutions for Industry Professionals:

Financial Services Firms: Gain detailed insights into revenue streams, funding rounds, and operational costs for competitor analysis and client acquisition.

Corporate Finance Teams: Enhance decision-making with precise data on industry trends and benchmarks.

Consulting Firms: Deliver informed recommendations to clients with access to detailed financial datasets and key stakeholder profiles.

Investment Firms: Identify potential investment opportunities with verified data on financial performance and market positioning.

What Sets Success.ai Apart?

Extensive Database: Access detailed financial data for 70M+ companies worldwide, including small businesses, startups, and large corporations.

Ethical Practices: Our data collection and processing methods are fully comp...

Paper |Homepage |Github

  🛠️ Usage

Regarding the data, first of all, you should download the MMfin.tsv and MMfin_CN.tsv files, as well as the relevant financial images. The folder structure is shown as follows: ├─ datasets ├─ images ├─ MMfin ... ├─ MMfin_CN ... │ MMfin.tsv │ MMfin_CN.tsv

The following is the process of inference and evaluation (Qwen2-VL-2B-Instruct as an example): export LMUData="The path of the datasets" python… See the full description on the dataset page: https://huggingface.co/datasets/hithink-ai/MME-Finance.

On an annual basis (individual hospital fiscal year), individual hospitals and hospital systems report detailed facility-level data on services capacity, inpatient/outpatient utilization, patients, revenues and expenses by type and payer, balance sheet and income statement.

Due to the large size of the complete dataset, a selected set of data representing a wide range of commonly used data items, has been created that can be easily managed and downloaded. The selected data file includes general hospital information, utilization data by payer, revenue data by payer, expense data by natural expense category, financial ratios, and labor information.

There are two groups of data contained in this dataset: 1) Selected Data - Calendar Year: To make it easier to compare hospitals by year, hospital reports with report periods ending within a given calendar year are grouped together. The Pivot Tables for a specific calendar year are also found here. 2) Selected Data - Fiscal Year: Hospital reports with report periods ending within a given fiscal year (July-June) are grouped together.

The Yahoo Stocks Dataset is an invaluable resource for analysts, traders, and developers looking to enhance their financial data models or trading strategies. Sourced from Yahoo Finance, this dataset includes historical stock prices, market trends, and financial indicators. With its accurate and comprehensive data, it empowers users to analyze patterns, forecast trends, and build robust machine learning models.

Whether you're a seasoned stock market analyst or a beginner in financial data science, this dataset is tailored to meet diverse needs. It features details like stock prices, trading volume, and market capitalization, enabling a deep dive into investment opportunities and market dynamics.

For machine learning and AI enthusiasts, the Yahoo Stocks Dataset is a goldmine. It’s perfect for developing predictive models, such as stock price forecasting and sentiment analysis. The dataset's structured format ensures seamless integration into Python, R, and other analytics platforms, making data visualization and reporting effortless.

Additionally, this dataset supports long-term trend analysis, helping investors make informed decisions. It’s also an essential resource for those conducting research in algorithmic trading and portfolio management.

Key benefits include:

• Historical Stock Data: Access years of trading data to analyze market behaviors.
• Versatile Applications: Use it for financial modeling, data analytics, or academic research.
• SEO Benefits for Finance Websites: Boost your content with insights derived from this dataset.

Download the Yahoo Stocks Dataset today and harness the power of financial data for your projects. Whether for AI, financial reporting, or trend analysis, this dataset equips you with the tools to succeed in the dynamic world of stock markets.

At CompanyData.com (BoldData), we specialize in delivering high-quality company data sourced directly from official trade registers. Our extensive dataset includes historical financial records for over 230 million companies worldwide, enabling deeper insight into business performance over time. Whether you're benchmarking companies, training AI models, or building risk profiles, our financial data equips you with the long-term perspective you need.

Our financial database includes multi-year balance sheets, profit and loss statements, and key performance indicators such as revenue, net income, assets, liabilities, and equity. We provide standardized and structured data—backed by rigorous validation processes—to ensure consistency and accuracy across jurisdictions. Each financial profile can be enriched with hierarchical data, firmographics, contact details, and industry classifications to support complex analyses.

This historical financial data supports a wide range of use cases including KYC and AML compliance, credit risk assessment, M&A research, financial modeling, competitive benchmarking, AI/ML training, and market segmentation. Whether you’re building a predictive scoring model or assessing long-term financial health, our data gives you the clarity and depth required for smarter decisions.

Delivery is flexible to suit your needs: access files in Excel or CSV, browse through our self-service platform, integrate via real-time API, or enhance your existing datasets through custom enrichment services. With access to 380 million verified companies across all industries and geographies, CompanyData.com (BoldData) provides the scale, precision, and historical context to power your next move—globally.

Ainnotate’s proprietary dataset generation methodology based on large scale generative modelling and Domain randomization provides data that is well balanced with consistent sampling, accommodating rare events, so that it can enable superior simulation and training of your models.

Ainnotate currently provides synthetic datasets in the following domains and use cases.

Internal Services - Visa application, Passport validation, License validation, Birth certificates Financial Services - Bank checks, Bank statements, Pay slips, Invoices, Tax forms, Insurance claims and Mortgage/Loan forms Healthcare - Medical Id cards

The global AI in Fintech market is experiencing explosive growth, driven by the increasing adoption of artificial intelligence across financial services. From fraud detection and risk management to personalized customer service and algorithmic trading, AI is transforming every aspect of the industry. The market, estimated at $50 billion in 2025, is projected to maintain a robust Compound Annual Growth Rate (CAGR) of 25% between 2025 and 2033, reaching an impressive market value of approximately $250 billion by 2033. This significant expansion is fueled by several key factors: the increasing availability of large datasets for training AI models, advancements in machine learning algorithms, and the growing need for efficient and secure financial transactions. Major players like Microsoft, Amazon Web Services, and Google are heavily investing in AI solutions for the fintech sector, further accelerating market growth. The rising adoption of cloud-based AI solutions and the increasing demand for regulatory compliance are also contributing to this growth. However, the market is not without its challenges. Data privacy and security concerns remain a significant hurdle, along with the need for robust regulatory frameworks to govern the use of AI in finance. The high cost of implementation and the lack of skilled professionals capable of developing and deploying AI systems also present obstacles to widespread adoption. Despite these challenges, the long-term outlook for AI in Fintech remains exceptionally positive. The continuous innovation in AI technologies and the increasing digitalization of the financial sector will continue to drive demand, creating lucrative opportunities for both established players and emerging startups in the coming years. Segment-wise, fraud detection and risk management are currently leading the way, followed by algorithmic trading and customer service solutions.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Probabilistic AI: A New Approach to Artificial Intelligence

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Data Collection and Labeling Market Outlook 2032

The global data collection and labeling market size was USD 27.1 Billion in 2023 and is likely to reach USD 133.3 Billion by 2032, expanding at a CAGR of 22.4 % during 2024–2032. The market growth is attributed to the increasing demand for high-quality labeled datasets to train artificial intelligence and machine learning algorithms across various industries.



Growing adoption of AI in e-commerce is projected to drive the market in the assessment year. E-commerce platforms rely on high-quality images to showcase products effectively and improve the online shopping experience for customers. Accurately labeled images enable better product categorization and search optimization, driving higher conversion rates and customer engagement.



Rising adoption of AI in the financial sector is a significant factor boosting the need for data collection and labeling services for tasks such as fraud detection, risk assessment, and algorithmic trading. Financial institutions leverage labeled datasets to train AI models to analyze vast amounts of transactional data, identify patterns, and detect anomalies indicative of fraudulent activity.

Impact of Artificial Intelligence (AI) in Data Collection and Labeling Market

The use of artificial intelligence is revolutionizing the way labeled datasets are created and utilized. With the advancements in AI technologies, such as computer vision and natural language processing, the demand for accurately labeled datasets has surged across various industries.



AI algorithms are increasingly being leveraged to automate and streamline the data labeling process, reducing the manual effort required and improving efficiency. For instance,

• 
  

  In April 2022, Encord, a startup, introduced its beta version of CordVision, an AI-assisted labeling application that inten

This dataset consists of sentiment information extracted from image and text data of financial subreddit posts. Posts from different financial subreddits are processed using AI tools to create sentiment variables.
The data consists of time series data and is fully anonymized. Financial tickers are included to allow financial forecasting.

AI Agent Marketplace and Directory of Finance AI Agent

This dataset contains meta information of AI Agent Marketplace's category of "Finance AI Agent". And the list will keep updated.

  Finance AI Agent

To navigate the directory of AI Agent please visit AI Agent Marketplace, including AUTONOMOUS AGENT, GUI AGENT, SALES AGENT, Multi Agent, etc. You can also search the AI Agent Marketplace using DeepNLP AI Agent Search Portal.

  Schema

The meta information of… See the full description on the dataset page: https://huggingface.co/datasets/DeepNLP/AI-Agent-Marketplace-Finance-Agent.

Quantum-AI Financial Modeling Market Outlook

According to our latest research, the Quantum-AI Financial Modeling market size reached USD 2.35 billion in 2024 globally, demonstrating robust momentum driven by the convergence of quantum computing and artificial intelligence in financial analytics. The market is projected to grow at a CAGR of 28.7% from 2025 to 2033, reaching a forecasted market size of USD 19.61 billion by 2033. This extraordinary growth is fueled by escalating demand for advanced predictive analytics, real-time risk assessment, and optimization solutions across the financial sector, as organizations increasingly leverage quantum-AI to gain a competitive edge in complex and volatile markets.

The primary growth factor driving the Quantum-AI Financial Modeling market is the pressing need for enhanced computational power and accuracy in financial modeling. Traditional computational techniques often fall short in handling the vast and complex datasets inherent in modern finance, especially for tasks such as risk analysis, portfolio optimization, and fraud detection. Quantum computing, when combined with AI, allows for the processing of multidimensional data at speeds unattainable by classical systems. This capability enables financial institutions to simulate intricate market scenarios, optimize asset allocations, and detect anomalies in real-time, thereby reducing operational risks and improving decision-making. As financial markets become more interconnected and data-driven, the adoption of quantum-AI solutions is expected to surge, further propelling market growth.

Another significant growth driver is the increasing regulatory scrutiny and the demand for transparency in financial operations. Regulatory bodies worldwide are mandating more rigorous risk management and reporting standards, compelling financial institutions to adopt advanced modeling tools that can ensure compliance and adaptability. Quantum-AI financial modeling platforms offer the ability to automate compliance checks, perform scenario-based stress testing, and provide granular audit trails, which not only streamline regulatory adherence but also enhance overall operational efficiency. This regulatory impetus is particularly pronounced in sectors such as banking, insurance, and investment management, where the cost of non-compliance can be substantial.

The accelerating pace of digital transformation in the financial services industry is also a critical factor fueling the expansion of the Quantum-AI Financial Modeling market. As financial institutions migrate toward cloud-based infrastructures and embrace digital-first strategies, the integration of quantum-AI capabilities becomes more feasible and scalable. Cloud deployment models lower the barrier to entry for smaller enterprises, enabling them to access cutting-edge financial modeling tools without significant upfront investments in hardware. Furthermore, the growing ecosystem of quantum software development kits, APIs, and partnerships between fintech firms and quantum computing vendors is fostering innovation and expanding the addressable market for Quantum-AI financial modeling solutions.

From a regional perspective, North America currently dominates the Quantum-AI Financial Modeling market, accounting for the largest share due to the presence of leading financial institutions, advanced technological infrastructure, and substantial investments in quantum research and development. The Asia Pacific region, however, is expected to witness the fastest growth rate during the forecast period, driven by rapid digitalization, increasing adoption of AI and quantum technologies, and supportive government initiatives in countries such as China, Japan, and Singapore. Europe is also emerging as a significant market, with regulatory harmonization and cross-border financial activities stimulating demand for sophisticated modeling tools. As these regions continue to invest in quantum-AI research and forge strategic collaborations, the global landscape of the Quantum-AI Financial Modeling market is poised for dynamic evolution.

Component Analysis

The Component segment of the Quantum-AI Financial Modeling market is categorized into software, hardware, and services, each playing a pivotal role in the ecosystem. Software solutions are at the forefront, encompassing quantum-AI algorithms, modeling platforms, and analytics suites designed to process massive datasets and gener

Analysis of ‘Financial Sentiment Analysis’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sbhatti/financial-sentiment-analysis on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Data

The following data is intended for advancing financial sentiment analysis research. It's two datasets (FiQA, Financial PhraseBank) combined into one easy-to-use CSV file. It provides financial sentences with sentiment labels.

Citations

Malo, Pekka, et al. "Good debt or bad debt: Detecting semantic orientations in economic texts." Journal of the Association for Information Science and Technology 65.4 (2014): 782-796.

--- Original source retains full ownership of the source dataset ---

Access 170M+ verified company financial data with Success.ai’s B2B Contact Data for European financial professionals. Includes work emails, phone numbers, and continuously updated datasets. Best price guaranteed.