- Financial Datasets for Machine Learning: The Essential Points
- What Are the Best Financial Datasets in the Market?
The global financial market is evolving at breakneck speed, taking it to the next digital and virtual level. However, there are many challenges along the way, including high competition, volatile market dynamics, and rapid advancement of information and communication technologies.
Most importantly, financial companies are dealing with a vast amount of complex data that help them perform financial analysis. In this case, retrieving useful information from financial datasets and then using them to train machine learning models has become both a challenge and a huge economic imperative for the global financial landscape. But what exactly is financial data, and why are financial datasets important?
In a nutshell, financial data refers to information about the organization’s financial performance and health. It is primarily used by internal management to analyze the business performance and identify the strategies to be revised. The external use of financial datasets entails the assessment of creditworthiness, reliability of investment, and compliance with regulatory requirements of the business reporting financial data.
Let’s examine the core of the matter and understand how financial datasets are used for machine learning in finance, banking, and insurance. We’ll discuss the different types of financial and banking datasets, as well as the large financial database infrastructure!
Financial Datasets for Machine Learning: The Essential Points
A financial dataset, as the name suggests, is a collection of financial data that can be both real-world or artificial (synthetic), aka generated by machines. Either way, it requires the subject-matter specialists (financial experts) to further label this data and put it into use.
Financial data is usually divided into four main categories: fundamental data, market data, analytics, and alternative data. And as we already know, it takes high-quality training datasets to teach machine learning algorithms how to make accurate predictions according to the AI project’s goals. Basically, this means extracting the value of data for complex ML systems. Moreover, progressive machine learning methods are crucial for addressing the drawbacks of modern econometrics.
As the classic approach to data proposes, one needs to deal with unstructured data first and turn it into a structured dataset using machine learning. Therefore, only a structured financial dataset is amenable to ML algorithms and can be used for various economic purposes, like automating trading activities or providing financial advice to potential investors. However, financial activities are associated with non-linear data structure and large, complex financial datasets. Also, it’s a lot of work with noisy financial data, as a privacy-enhancing technique, and a human element, which currently go beyond the standard machine learning methods.
Dealing with financial data becomes an essential skill and condition for economic entities wishing to strengthen their market position and get the best use out of machine learning and data itself. To help them achieve this, we’ve prepared a list of open, publicly available sources with the most comprehensive and reliable financial and banking datasets.
What Are the Best Financial Datasets in the Market?
Despite many publicly available datasets today, there’s an evident lack of datasets coming from the banking and financial industries. This is especially true for the datasets created for credit and fraud risk management activities. The main reason is that presenting this data as publicly available is considered a violation of customers’ privacy and trust.
However, it’s not as bad as it sounds. Some easily accessible financial datasets for machine learning are based on pre-treated data (i.e., reduced data to principal components or only a small sample of data). Still, this is not something one can rely on when building ML models for financial tasks. Machine learning models can provide accurate predictions only when the training dataset is large, clean enough, and well-balanced (approximately equal number of samples in each class).
We’ve gathered the most popular free economic and financial datasets for machine learning at the full disposal of modern financial institutions:
- Data.gov. A US government website that provides access to high-quality, machine-readable datasets created by the Executive Branch of the Federal Government in a variety of fields.
- International Monetary Fund (IMF Data). IMF's financial datasets provide information about international finances, foreign exchange reserves, commodity prices, debt rates, and investments. They give clear insights on the global economic forecast, financial stability, fiscal monitoring, etc.
- Financial Times/Markets Data. A global database that contains data on stock price indexes, commodities, foreign exchange, and is regularly updated. It’s one of the most authoritative news organizations worldwide and the most reliable open-access data source for global markets.
- World Bank Open Data. A unique financial dataset that includes global population demographics as well as a wide range of economic and development metrics that may be used in predictive modeling. One of the most complete open data sources.
- Google Trends. A plethora of global data, including financial datasets and search trends.
- Quandl. One of the best spots to get credible, accurate, and valuable financial datasets for predictive modeling.
- EU Open Data Portal. A data repository originated by EU institutions and agencies, which offers datasets collected from areas like environment, employment, science, and education.
- American Economic Association (AEA). The AEA has a vast collection of economic data and financial datasets, particularly the US macroeconomic data.
- Global Financial Data (GDF). GDF generates the most comprehensive historical economic and financial datasets used for major global markets and economies’ analysis.
and other sources, including:
- School System Finances, US Stock Data, CBOE Volatility Index (VIX), Dow Jones Weekly Returns, EconData, Simfin, AssetMarco, Eurostat Comext, CIA World Factbook, Global Financial Development, etc.
Overview of the Current Financial Datasets for ML by Data Type and Financial Service
Financial data is an umbrella term for a comprehensive data infrastructure involving various institutions that perform different financial services. As such, there’s a wide gamut of financial data repositories, depending on the economic service: finance and banking, insurance, trading, financial crime, fraud detection, or financial news, to name a few.
Financial datasets can be used for data mining, regression analysis, financial reports and statement, and monitoring financial transactions. Let’s explore the features of financial datasets, among which the most crucial attributes are:
- Financial datasets are large, high-dimensional, and complex;
- They can be unstructured and non-numerical;
- The number of variables usually outnumbers the number of observations;
- Financial datasets are often sparse, containing NaNs (not-a-numbers) and many; ‘outliers’ values that have to be cleaned;
- They might indirectly include information about networks of agents, timestamps, etc.
Fortunately, the data-oriented nature of machine learning helps address the complex structure of large financial datasets. ML algorithms can accurately create a good approximation or interpolation of some base financial model (trained on the dataset). They usually provide valuable insights into an overly complicated set of numerical reasoning questions, linking structured tables and unstructured texts.
It’s fairly hard for humans to deal with an excessive amount of financial data, which is why machine learning techniques are needed to automate the process of analyzing the business’s financials. So, where are financial datasets stored, and where can they be downloaded? Here’s a short overview of each provided by Label Your Data.
Banking Datasets
The banking sector is producing an overwhelming amount of data through millions of daily transactions. Banks are dealing with big data that helps them better understand their clients. Banking datasets contain stats on banks’ profitability, balance sheets, asset quality, liquidity, funding, capital adequacy, and solvency of banks. Machine learning models built on top of banking datasets can be used for loan portfolios (customer targeting), credit (customer decisions analysis), or discovering top performers in the team. Some of them can be found on Kaggle.
Financial Crime Datasets
Preventing financial crime has never been more challenging, given modern compliance standards and growing financial data. Financial crime datasets are collected from financial, criminal, and other open data records. They are used to identify connections, patterns, and risks of financial crime activities. The ML models built on such data help filter out false positives, improve current investigative processes, and effectively detect the inherent risks of money laundering.
Fraud Detection Datasets
The skilled combination of supervised and unsupervised methods of ML and data science can be a viable strategy towards mitigating financial crime, particularly fraud. Financial datasets for fraud detection are created by extracting data from online sources and enterprise systems. These are the large financial datasets used to spot and detect fraudulent transactions and are applied for real-time checks on transactions and batch analysis.
Financial News Datasets
Advancements in ML have been driving the growth of financial data analysis. One such example is sentiment analysis of financial news for trading decisions, stock picking, fund and asset management, and risk control in the market. Financial news dataset is the collection of unstructured, qualitative data (public information) from corporate disclosures and filings, news media, and user-generated content such as social media. It’s mainly used in the decision-making process for investments. Financial news datasets can be found on Financial Times/Markets Data, Kaggle, or GitHub.
Concluding Thoughts on Financial Services Datasets
Every day, the world’s top financial organizations operate on and produce a considerable amount of financial data. No other industry deals with this amount of data, which puts higher expectations on machine learning in the economic sector.
Large datasets are commonplace in finance today, an ideal scenario for machine learning. Companies must, therefore, learn how to use financial datasets for building effective ML models and deploy more sophisticated, data-driven tactics to enhance their market success.
Considering the tricky nature of financial data, its annotation becomes quite challenging. Looking for a reliable partner for collecting or labeling financial documentation? Contact our team and get your quote now, so our team can find the best option for your unique ML project!
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