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).

Dataset Description This dataset contains details of various bank transactions. The data includes both debit and credit transactions made using different modes such as card, ATM, and UPI. Each transaction record provides comprehensive information, including the type of transaction, the mode of payment, the amount transacted, the current balance after the transaction, timestamps, and additional details such as narration and transaction ID.

Columns type: The type of transaction (DEBIT or CREDIT). mode: The mode of the transaction (e.g., CARD, ATM, UPI, OTHERS). amount: The amount involved in the transaction. currentBalance: The account balance after the transaction. transactionTimestamp: The timestamp of when the transaction occurred. valueDate: The date the transaction is valued. txnId: A unique identifier for the transaction. narration: A brief description of the transaction. reference: Additional reference information, if any.

Usage This dataset can be used for various analytical purposes, including but not limited to:

• Analyzing spending patterns.
• Studying the frequency and types of transactions.
• Developing predictive models for transaction behaviors.
• Financial and economic research.

Source The dataset is a synthetic creation for educational and analytical purposes. It provides a realistic representation of transaction data typically found in bank statements. It is generated using real data

Finance Statement

## Overview

Finance Statement is a dataset for object detection tasks - it contains Text Table annotations for 200 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

In synthetica synthetica synthetica synthetica synthetica dicta synthetica syntheticas syntheticas syntheticas enuntiationes factas artificiose generatas designabat ad simulata documenta realia nummaria. Varias transactiones tabulas, dies, summas et singulas rationes componit, quae ad formas rerum et contentorum reales mundi speculorum structas est. Haec dataset usus est ad formandum et aestimandum Documenti AI systemata in operibus sicut agnitio characteris optici (OCR), extractio notitiarum et analysis documenti, praebens ambitum moderatum sine intimis quaestionibus actualis notitiae nummariae.

Hospital financial data, yearly balance sheet and income statement

Scan software to extract transactions from PDF bank statements. AI-powered and API-ready, it converts PDFs and images into structured, usable data.

Comprehensive financial statement for OKX, including key performance indicators, market data, and ecosystem analytics.

Bank Statement Analyzer Market Outlook

The global bank statement analyzer market size was valued at USD 1.2 billion in 2023 and is projected to reach USD 3.8 billion by 2032, growing at a CAGR of 12.5% during the forecast period. The growth of the bank statement analyzer market is driven by the increasing need for automated financial analysis, which provides more accurate and quicker insights into financial transactions, enhancing the efficiency and decision-making processes for various financial institutions.

One of the primary growth factors fueling the bank statement analyzer market is the rapid technological advancement in artificial intelligence (AI) and machine learning (ML). These technologies enable more sophisticated data analysis, allowing bank statement analyzers to provide detailed and precise financial insights. Financial institutions are increasingly relying on these tools to reduce manual errors, mitigate risks, and streamline operations, thus driving the market's expansion. Additionally, the growing adoption of digital banking services has necessitated the use of advanced tools to manage the vast amounts of transaction data generated, further propelling the demand for bank statement analyzers.

Another significant growth driver is the rising regulatory compliance requirements across the globe. Financial institutions are under constant scrutiny to ensure adherence to stringent regulations and reporting standards. Bank statement analyzers help in maintaining compliance by providing accurate transaction records and facilitating easy audits and reports. This not only reduces the risk of non-compliance but also saves time and resources, making these analyzers an essential tool for banks and financial institutions.

The increasing demand for personalized banking solutions also contributes to the growth of the bank statement analyzer market. Customers today expect personalized services that cater to their specific financial needs and preferences. Bank statement analyzers leverage AI and ML to analyze customer transaction data and derive insights that can be used to offer tailored financial products and services. This enhances customer satisfaction and loyalty, driving banks and financial institutions to adopt these advanced analysis tools.

The integration of Smart Finance Technologies is revolutionizing the way financial institutions handle their data. These technologies encompass a range of digital tools and platforms designed to enhance financial management and analysis. By leveraging smart finance solutions, banks and financial institutions can automate complex tasks, improve accuracy, and reduce the time spent on manual processes. This not only boosts operational efficiency but also enables institutions to provide better services to their clients. As the demand for more intelligent and adaptive financial solutions grows, the role of Smart Finance Technologies becomes increasingly pivotal in shaping the future of banking and finance.

From a regional perspective, North America holds a significant share of the global bank statement analyzer market due to the early adoption of advanced technologies and the presence of major financial institutions. The region's strong regulatory framework and high focus on digital transformation in the banking sector further support market growth. Europe also offers substantial growth opportunities with its stringent compliance requirements and increasing investment in financial technology. Meanwhile, the Asia Pacific region is expected to witness rapid growth due to the rising number of digital banking users and increased adoption of AI-based financial tools.

Component Analysis

In the bank statement analyzer market, the component segment is divided into software and services. The software segment encompasses various platforms and applications that automate the analysis of bank statements. These software solutions utilize advanced algorithms and machine learning techniques to provide accurate insights and predictions. The increasing demand for efficient and effective financial analysis tools has led to significant advancements in bank statement analyzer software, contributing to the market's growth. Furthermore, the integration of AI and ML in these software solutions enables more accurate and quicker data processing, which is crucial for financial institutions to make informed decisions.

The services segment includes consulting, implemen

Annual-Financial-Statement

Get detailed Ford Motor Company Financial Statements 2020-2024. Find the income statements, balance sheet, cashflow, profitability, and other key ratios.

Financial Statements Dataset for FinGPT Fine-Tuning

  Overview

This dataset contains 3000 records, specifically designed for fine-tuning a FinGPT model to generate insights from financial statements. The dataset includes:

1000 Balance Sheets 1000 Income Statements 1000 Cash Flow Statements

Each record consists of:

Input: The text of a financial statement (Balance Sheet, Income Statement, or Cash Flow Statement) Output: Corresponding insights, analysis, or advice based on… See the full description on the dataset page: https://huggingface.co/datasets/MudassirFayaz/financial_statements.

apoidea/financial-statement-table-html dataset hosted on Hugging Face and contributed by the HF Datasets community

Get detailed Nasdaq Financial Statements 2020-2024. Find the income statements, balance sheet, cashflow, profitability, and other key ratios.

A Capability Statement documents a set of capabilities (behaviors) of an FHIR (Fast Healthcare Interoperability Resources) Server for a particular version of FHIR that may be used as a statement of actual server functionality or a statement of required or desired server implementation.

Banco do Brasil SA: Prudential: Income Statement: Net Income data was reported at 3,999,507.000 BRL th in Mar 2019. This records a decrease from the previous number of 6,919,199.000 BRL th for Dec 2018. Banco do Brasil SA: Prudential: Income Statement: Net Income data is updated quarterly, averaging 3,999,507.000 BRL th from Mar 2014 (Median) to Mar 2019, with 21 observations. The data reached an all-time high of 8,803,411.000 BRL th in Jun 2015 and a record low of 2,301,250.000 BRL th in Sep 2016. Banco do Brasil SA: Prudential: Income Statement: Net Income data remains active status in CEIC and is reported by Central Bank of Brazil. The data is categorized under Brazil Premium Database’s Banking Sector – Table BR.KBC077: Commercial Banks: Income Statement: Banco do Brasil SA.

Banco do Brasil SA: Income Statement: Earnings Before Tax & Profit Sharing data was reported at 4,742,426.000 BRL th in Mar 2019. This records a decrease from the previous number of 10,444,227.000 BRL th for Dec 2018. Banco do Brasil SA: Income Statement: Earnings Before Tax & Profit Sharing data is updated quarterly, averaging 3,414,540.000 BRL th from Mar 2000 (Median) to Mar 2019, with 77 observations. The data reached an all-time high of 15,498,989.000 BRL th in Jun 2013 and a record low of -4,184,839.000 BRL th in Sep 2015. Banco do Brasil SA: Income Statement: Earnings Before Tax & Profit Sharing data remains active status in CEIC and is reported by Central Bank of Brazil. The data is categorized under Brazil Premium Database’s Banking Sector – Table BR.KBB077: Commercial Banks: Income Statement: Banco do Brasil SA.

Get detailed Boeing Company Financial Statements 2020-2024. Find the income statements, balance sheet, cashflow, profitability, and other key ratios.

Banco do Brasil SA: Income Statement: Net Income data was reported at 3,972,064.000 BRL th in Mar 2019. This records a decrease from the previous number of 6,907,615.000 BRL th for Dec 2018. Banco do Brasil SA: Income Statement: Net Income data is updated quarterly, averaging 2,580,951.000 BRL th from Mar 2000 (Median) to Mar 2019, with 77 observations. The data reached an all-time high of 10,120,867.000 BRL th in Jun 2013 and a record low of 72,352.000 BRL th in Mar 2000. Banco do Brasil SA: Income Statement: Net Income data remains active status in CEIC and is reported by Central Bank of Brazil. The data is categorized under Brazil Premium Database’s Banking Sector – Table BR.KBB077: Commercial Banks: Income Statement: Banco do Brasil SA.

ES: Households: Assets: Financial: Financial Derivatives data was reported at 375.000 EUR mn in 2014. This records a decrease from the previous number of 430.000 EUR mn for 2013. ES: Households: Assets: Financial: Financial Derivatives data is updated yearly, averaging 441.000 EUR mn from Dec 2008 (Median) to 2014, with 7 observations. The data reached an all-time high of 517.000 EUR mn in 2011 and a record low of 375.000 EUR mn in 2014. ES: Households: Assets: Financial: Financial Derivatives data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Spain – Table ES.IMF.FSI: Sectoral Financial Statement: Balance Sheet: Annual.

This dataset is obtained from the MTA’s annual budgeting process and provides a detailed breakout of the MTA’s operating expenses and revenues. Data is available for each month of a fiscal year, on an accrual basis, and is categorized by budget scenario (actuals or budgeted plan), Agency, Financial Plan Year, Expense Type, and subcategories for types of expenses or revenues.