All financial transactions made by the Intellectual Property Office as part of the Government’s commitment to transparency in expenditure

The "yahoo_finance_dataset(2018-2023)" dataset is a financial dataset containing daily stock market data for multiple assets such as equities, ETFs, and indexes. It spans from April 1, 2018 to March 31, 2023, and contains 1257 rows and 7 columns. The data was sourced from Yahoo Finance, and the purpose of the dataset is to provide researchers, analysts, and investors with a comprehensive dataset that they can use to analyze stock market trends, identify patterns, and develop investment strategies. The dataset can be used for various tasks, including stock price prediction, trend analysis, portfolio optimization, and risk management. The dataset is provided in XLSX format, which makes it easy to import into various data analysis tools, including Python, R, and Excel.

The dataset includes the following columns:

Date: The date on which the stock market data was recorded. Open: The opening price of the asset on the given date. High: The highest price of the asset on the given date. Low: The lowest price of the asset on the given date. Close*: The closing price of the asset on the given date. Note that this price does not take into account any after-hours trading that may have occurred after the market officially closed. Adj Close**: The adjusted closing price of the asset on the given date. This price takes into account any dividends, stock splits, or other corporate actions that may have occurred, which can affect the stock price. Volume: The total number of shares of the asset that were traded on the given date.

The data sets below provide selected information extracted from exhibits to corporate financial reports filed with the Commission using eXtensible Business Reporting Language (XBRL).

AdiOO7/llama-2-finance dataset hosted on Hugging Face and contributed by the HF Datasets community

All financial transactions made by Companies House as part of the Government’s commitment to transparency in expenditure

All financial transactions made by SLC as part of its functions, including payments to/on behalf of customers and payments to suppliers.

The COFI database includes power-generation projects in Belt and Road Initiative (BRI) countries financed by Chinese corporations and banks that reached financial closure from 2000 to 2023. Types of financing include debt and equity investment, with the latter including greenfield foreign direct investments (FDI) and cross-border mergers and acquisitions (M&As). COFI is consolidated using nine source databases using both automated join method in R Studio, and manual joining by analysts. The database includes power plant characteristics data and investment detail data. It captures 575 power plants in 87 BRI countries, including 314 equity investment transactions and 341 debt investment transactions made by Chinese investors. Key data points for financial transactions in COFI include the financial instrument (equity or debt), investor name, amount, and financial close year. Key technical characteristics tracked for projects in COFI include name, installed capacity, commissioning year, country, and primary fuel type. This project is a collaboration among the Boston University Global Development Policy Center, the Inter-American Dialogue, the China-Africa Research Initiative at the Johns Hopkins University (CARI), and the World Resources Institute (WRI). The detailed methodology is given in the World Resources Institute publication “China Overseas Finance Inventory”. Cautions When analyzing debt investment amounts, users should be aware of the difference between loan commitment and actual disbursement. Our database records the loan commitment for a certain year and not actual disbursement. The investment amount should only provide a rough picture of where Chinese companies are investing and not how much their exact portion is. In this version of the database, all equity investment amounts are missing. This is because the equity amount is either missing or estimated in the source databases. Citation

This release provides information on the capital structure of businesses in New Zealand, the sources of finance they use, and their recent financing experience. The Business Finance Survey collected information on recent debt and equity finance requests for businesses with between 1 and 500 employees. Information was also collected on the current financial structure of the businesses, and other characteristics that may have influenced the businesses' ability to obtain finance.

This dictionary gathers different disciplines and topics such as: finance, economy, trade, business, stock-exchange, banking, firms, negotiation, mailing, telephone conversation, values, etc. It also includes many phrases relevant for business, impersonal expressions, conjugated sentences, relevant sentences, standard sentences, synonyms, abbreviations. The DISCIPLINE field gives a subdivision into sectors : stock exchange, trade, export, business, values, economy, banking, etc. Single words are associated with the meaning or event which they apply to.Languages : French - English (GB, US), English (GB, US) - FrenchNumber of entries: 91,300. Number of terms per language: about -10% with respect to the number of entries (i.e. ca. 82,000 terms)Disciplines: about 105Format: .DBF files, sorted alphabetically in French and EnglishA viewer is also available upon demand. This software enables a spontaneous search French => English and English => French in the database according to different criteria:- by beginning of term, - by included word,- by discipline,- by abbreviation.Terms, phrases and conjugated sentences are sorted alphabetically.Examples : phrases beginning with "à" : à terme, à titre gracieux, à titre onéreux, à vue...; "en" : en compte, en vigueur..., "prix" : prix abordable, prix choc, prix exorbitant...Viewing format: .FIC (Windev)Please note that the prices indicated here are dependent from the number of entries available which is growing constantly. Please contact us for further details.

This data contains the latest State and Local Government Finance data from the U.S. Census. A detailed description of the project can be found in: Pierson K., Hand M., and Thompson F. (2015). The Government Finance Database: A Common Resource for Quantitative Research in Public Financial Analysis. PLoS ONE doi: 10.1371/journal.pone.0130119

The dataset captures 20,985 projects across 165 low- and middle-income countries supported by loans and grants from official sector institutions in China worth $1.34 trillion. It tracks projects over 22 commitment years (2000-2021) and provides details on the timing of project implementation over a 24-year period (2000-2023).

This dataset tracks the known universe of overseas Chinese official finance between 2000-2014, capturing 4,373 records totaling $354.4 billion. The data includes both Chinese aid and non-concessional official financing. For geolocated data on Chinese project locations, see AidData's Geocoded Global Chinese Official Finance Dataset, Version 1.1.1.

The Global Financial Development Database is an extensive dataset of financial system characteristics for 203 economies. The database includes measures of (1) size of financial institutions and markets (financial depth), (2) degree to which individuals can and do use financial services (access), (3) efficiency of financial intermediaries and markets in intermediating resources and facilitating financial transactions (efficiency), and (4) stability of financial institutions and markets (stability).

For a complete description of the dataset and a discussion of the underlying literature, see: Martin Čihák, Aslı Demirgüç-Kunt, Erik Feyen, and Ross Levine, 2012. "Benchmarking Financial Systems Around the World." World Bank Policy Research Working Paper 6175, World Bank, Washington, D.C.

This dataset contains campaign finance summary records for candidate and committee campaigns for the last 10 years. The data present a summary with one record per candidate or committee for an election year that summarized the campaign contributions ,expenditures, debts, etc up to the point in time the summary was generated. For candidates campaigns, the number of years is determined by the year of the election, not necessarily the year the data was reported. This dataset is a best-effort by the PDC to provide a complete set of records as described herewith and may contain incomplete or incorrect information. The PDC provides access to the original reports for the purpose of record verification. Descriptions attached to this dataset do not constitute legal definitions; please consult RCW 42.17A and WAC Title 390 for legal definitions and additional information regarding political finance disclosure requirements. CONDITION OF RELEASE: This publication and or referenced documents constitutes a list of individuals prepared by the Washington State Public Disclosure Commission and may not be used for commercial purposes. This list is provided on the condition and with the understanding that the persons receiving it agree to this statutorily imposed limitation on its use. See RCW 42.56.070(9) and AGO 1975 No. 15.

Using a sample of former colonies, this paper assesses two theories regarding the historical determinants of financial development. The law and finance theory holds that legal traditions, brought by colonizers, differ in terms of protecting the rights of private investors vis-à-vis the state, with important implications for financial development. The endowment theory argues that the disease environment encountered by colonizers influences the formation of long-lasting institutions. The empirical results provide evidence for both theories. However, initial endowments are more robustly associated with financial intermediary development and explain more of the cross-country variation in financial intermediary and stock market development.

Finance Co: Assets: Account Receivables: Business data was reported at 391.370 USD bn in Jun 2018. This records an increase from the previous number of 387.073 USD bn for Mar 2018. Finance Co: Assets: Account Receivables: Business data is updated quarterly, averaging 361.262 USD bn from Jun 1980 to Jun 2018, with 153 observations. The data reached an all-time high of 563.927 USD bn in Jun 2008 and a record low of 42.185 USD bn in Sep 1980. Finance Co: Assets: Account Receivables: Business data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s United States – Table US.KB037: Finance Companies: Balance Sheet.

Finance Co: Liabilities: Debt: All Other Liabilities data was reported at 156.248 USD bn in Mar 2018. This records a decrease from the previous number of 173.619 USD bn for Dec 2017. Finance Co: Liabilities: Debt: All Other Liabilities data is updated quarterly, averaging 176.480 USD bn from Mar 1984 to Mar 2018, with 137 observations. The data reached an all-time high of 497.168 USD bn in Sep 2005 and a record low of 33.242 USD bn in Jun 1984. Finance Co: Liabilities: Debt: All Other Liabilities data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s USA – Table US.KB010: Finance Companies: Balance Sheet.

• Report Overview
• Key Takeaways
• Driving factors
• Restraining Factors
• By Deployment Model Analysis
• By Application Analysis
• By Technology Analysis
• Key Market Segments
• Growth Opportunity
• Latest Trends
• Regional Analysis
• Key Players Analysis
• Recent Development
• Report Scope

Report Overview

The Generative AI in Finance Market size is expected to be worth around USD 27,430.7 Mn by 2033 from USD 1,397.9 Mn in 2023, growing at a CAGR of 35.7% during the forecast period from 2024 to 2033.The surge in demand for new advanced technologies and the adoption of digitalization are some of the main key driving factors for generative artificial intelligence in the finance market. GenAI and ML in finance comprise everything starting from virtual assistants like chatbots to fraud detection and task automation. An article published by Imagovation Insider in August 2023, highlights that GenAI has changed the system of the financial sector. Introducing AI in finance has led to cost reduction and increased productivity. This article also highlights the Gartner study that more than 80% of CFOs surveyed in 2022 expected to invest more in AI in the coming two years.Generative AI for finance helps companies to fasten their path to high efficacy, precision, and adaptability. It helps in forecasting and budgets according to an report published by KPMG highlights that as per the survey conducted by them more than 83% of respondents utilized AI for financial planning which comprises predictive models, situational development, and budget insights. GenAI also helps in producing financial reports and consumes time. It helps in collecting market intelligence by using a large language model and also generates tactic insights from the information gathered. By using GenAI, many financial companies can get contracts that aim at non-standard terms and reporting accounting treatments. This technology also helps in identifying irregularities and fraud detection. There are several advantages of using GenAI in the finance market such as it helps to automate workflow and processes. It also helps in maintaining precision, efficacy, speed, new ideas, and availability to handle and regulate all types of finances.The implementation of GenAI in finance will help the banking sector to grow tremendously. Many financial firms have digitalized their services and have implemented innovative ways to sell, add efficacy, and aim to generate more data. GenAI plays an important role by driving personalized customer responses that make more secure and accountable service suggestions. By use of GenAI and human engagement, financial institutes can develop seamless experiences that can address customers requirements. The demand for GenAI in finance will increase due to its requirement in various verticals of the finance sector that will help in market expansion in the coming years.

Key Takeaways

• Market Growth: The Generative AI in Finance Market size is expected to be worth around USD 27,430.7 Mn by 2033 from USD 1,397.9 Mn in 2023, growing at a CAGR of 35.7% during the forecast period from 2024 to 2033.
• By Deployment Model: In 2023, cloud deployment dominated the Generative AI in Finance Market due to its scalability and cost-efficiency.
• By Application: Generative AI dominates risk management in finance, enhancing predictive analytics and compliance efficiency in 2023.
• By Technology: Deep Learning Technology dominates Generative AI in Finance, shaping AI deployment with predictive analytics and risk management.
• Regional Dominance: North America leads the generative AI finance market, setting global benchmarks with a 40% share.
• Growth Opportunity: Generative AI in finance boosts productivity and market reach, revolutionizing the sector in 2024.

Driving factors

Fraud Detection and Prevention: Enhancing Security and Trust in Financial Services

The application of generative AI in fraud detection and prevention is pivotal for the growth of the finance market, primarily by enhancing the security of transactions and maintaining consumer trust. Generative AI technologies, such as deep learning and neural networks, have revolutionized the ability to analyze transaction patterns and identify anomalies that may indicate fraudulent activity. By learning from vast datasets of historical transactions, these systems can generate models that predict and detect suspicious behaviors with high accuracy.The financial impact of fraud on institutions is significant, costing billions annually. The use of AI in combating this can not only reduce losses but also optimize the allocation of resources towards more productive activities rather than fraud management. A report suggested that AI-driven fraud detection systems could save businesses approximately $12 billion annually by 2023. This considerable saving underscores the vital role of generative AI in financial services, making it a key driver in the adoption and market expansion of AI technologies in finance.

Personalized Financial Advice: Tailoring Solutions to Enhance Customer Engagement

Generative AI contributes significantly to the personalization of financial advice, a factor that drives customer satisfaction and retention in financial services. By leveraging data on individual spending habits, investment history, and financial goals, AI models can generate personalized insights and recommendations. This tailored approach not only improves customer engagement but also empowers consumers with better financial decision-making tools, aligning with their specific financial contexts and aspirations.The ability to provide customized advice at scale can transform customer interactions from generic to personal, thereby increasing the perceived value of financial services institutions. Institutions that adopt AI for personalized advice are likely to see higher engagement rates, as noted in the analysis, where banks employing advanced analytics and AI reported an increase in customer satisfaction scores by up to 10%. This shift not only drives growth in the generative AI market but also enhances the competitive edge of financial institutions that implement these solutions.

Portfolio Management Optimization: Increasing Efficiency and Returns

Generative AI plays a transformative role in optimizing portfolio management, another key growth driver in the finance market. AI technologies enable more sophisticated analysis of market data and trends, allowing for the generation of optimized investment strategies that can adapt to changing market conditions. This capability leads to better risk management and potentially higher returns on investments, which are critical value propositions for both individual investors and institutional clients.AI-driven portfolio management systems can process complex scenarios and vast datasets far more efficiently than traditional models. This efficiency not only reduces operational costs but also enhances the scalability of financial services Firms. For example, advisors, which are largely driven by AI, are projected to manage significantly large assets under management (AUM) in the coming years. According to a report, the advisors are expected to manage $2.5 trillion by 2023. This statistic highlights the substantial impact of generative AI on market dynamics within the financial sector.

Restraining Factors

Data Privacy and Security Concerns: A Major Hindrance to Market Adoption and Trust

In the realm of generative AI in finance, data privacy and security concerns are paramount. Financial institutions handle sensitive personal and business data, making them prime targets for cyber-attacks. The deployment of generative AI technologies

Yahoo Finance dataset provides information on top traded companies. It contains financial information on each company including stock ticker and risk scores and general company information such as company location and industry. Each record in the dataset is a unique stock, where multiple stocks can be related to the same company. Yahoo Finance dataset attributes include: company name, company ID, entity type, summary, stock ticker, currency, earnings, exchange, closing price, previous close, open, bid, ask, day range, week range, volume, and much more.