Financial Data Analytics Tools – Accounting is more than just balancing books or submitting a tax return. Over the years, software has helped to automate many manual accounting processes, allowing professionals to take on a more analytical role.
However, the lack of skilled professionals is a major challenge for accounting to become a more analytical role. Businesses need accountants who are skilled in business administration as well as data science. Gardener
Financial Data Analytics Tools
Says lack of computer skills in finance can cost a business as much as 1% of total revenue.
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If you’re an accountant or auditor stuck with backend spreadsheets and calculations, it’s time to take a deeper look at your numbers. To become a next-generation accounting professional, you need to learn how to use data analytics to discover business insights and make recommendations—that is, complement your financial skills with knowledge of analytics.
Are you ready to upgrade your skills? In this article, we discuss how data analytics is changing the accounting profession and how you can adapt to this in-demand industry skill.
Simply put, data analytics is the practice of taking a 360-degree view of a problem or situation. This is done by collecting, examining and organizing all relevant data to extract meaningful information.
Accounting professionals handle tons of data—cash receipts, checks, bank statements, invoices, and more—to prepare financial statements every day. Analytics tools help them analyze financial statements and other non-financial data to make predictions and recommend corrective actions.
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Entry-level office jobs, such as collecting and logging records of business transactions, have already been partially or fully automated. Analytics now help accountants perform more complex tasks such as checking financial records for fraud and analyzing financial statements. Here are some of the accounting functions that data analysis replaces:
Accounting analysis tools increase the auditor’s ability to examine large amounts of data. Auditors are now able to analyze entire financial records instead of sampling small data. This leads to easier identification of outliers, a lower margin of error and more corrective recommendations.
With robust predictive models that analyze a region’s regulatory environment, market sentiment and financial risks, tax accountants can more accurately predict market movements and create useful prescriptions, resulting in tax-saving and profitable investment recommendations.
By improving cost estimates, analysis of accounting data makes budgets more accurate and relevant. Through real-time data analysis, accounting experts can also make frequent revisions of budgets.
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By increasing the speed of data processing, analytics allow accountants to crunch data on demand to produce financial statements, which summarize business transactions into results and other such reports. Usually these statements are prepared once every three, six or 12 months, but then they lose their relevance for many stakeholders (business units, investors, etc.).
Data analysis helps accountants predict future risks such as bankruptcy, fraud and misstatements in financial reports. Accounting firms can use predictive analytics to analyze areas of risk, better measure the likelihood of a particular risk and take proactive measures.
Before the rise of data analytics, financial accounting was limited to the descriptive interpretation of financial statements with summaries of daily business transactions. Few, if any, practitioners will delve deeper to find the “why” of the data – say why revenue was down this year. But it was a very manual process and success depended on internal relationships and the ability to obtain information from other departments.
In the analysis domain, there is a clear shift in the accountants’ responsibilities from administrative tasks to logic. They range from stacking and storing data to filtering relevant insights (descriptive and diagnostic analytics) and interpreting results to achieve larger business goals (predictive and prescriptive analytics).
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Working with data analysis comes easier to people with strong quantitative skills and business acumen. And accountants are ready for both.
Their numbers and inherent knowledge of business fundamentals and the needs of internal (like the C-suite) as well as external (like investors) stakeholders make them perfect candidates for thinking holistically about data.
By itself, data is of little use. To explore and understand large volumes of data, use data analysis software. Data analysis tools help organize large data sets, identify relationships between complex data points, and generate reports to enable trend analysis. They allow you to spot patterns and outliers, require little or no coding skills, and also offer an easy-to-use drag-and-drop interface.
Amita Jain is a writer who has covered the branding and accounting markets with a focus on new digitally enabled tools and techniques. She has a degree in public policy from King’s College London, and has worked as a journalist for an academic magazine. Her work has been featured by Gartner and Career360, among other publications. Swimming, doodling and reading fiction are her happy distractions outside of work. The popularity of the Python programming language is due, at least in part, to the versatility it offers. In addition to a large number of use cases in web and app development, Python provides the tools to create and implement any type of scientific or mathematical model, regardless of the origin or type of data. This versatility is made possible by an extensive standard library that provides many facilities aimed at increasing the functionality and portability of the language. For more specific applications, the Python Package Index (PyPI) provides additional packages that extend Python’s capabilities to meet the needs of each domain.
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For these reasons, Python has proven to be a powerful tool for developing new financial technologies. From raw number crunching to creating aesthetically pleasing yet intuitive graphical user interfaces (GUIs), countless packages exist to help users build their own financial models. In this article I will highlight my top 10 packages for finance and economic modeling with some basic examples. All of these packages (except Quantlib) are available on the platform to include in your runtime environment.
The field of financial technology is large and covers everything from insurance, lending and trading, to e-banking and other payment services. This article focuses on specific quantitative finance applications, which require programming tasks such as data import and transformation, time series and risk analysis, trading and backtesting, Excel integration, and data visualization. I try some of the best packages to accomplish each task.
Basically, all financial models rely on number crunching. The first couple of packages I’ve listed provide a framework for doing that. The first is NumPy. NumPy is the most essential package for scientific and mathematical computing in Python. It not only introduces n-dimensional arrays and matrices to Python, but also covers some basic mathematical operations for manipulating these data structures. Most of the high-level Python packages for finance mentioned later in this list rely on NumPy.
The NumPy package provides a basic mathematical structure for manipulating and storing data. But building sophisticated models based on this data requires a repertoire of more advanced statistical tools and operations. Enter SciPy. This package contains the necessary functions and algorithms for advanced scientific calculations necessary to build any statistical model. It includes algorithms for interpolation, optimization, clustering, transformation and integration of data. These operations are essential when doing any kind of data analysis or building any kind of predictive model.
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To demonstrate interpolation, I first use NumPy to generate some data points with an arbitrary function, and then compare different interpolation methods:
NumPy and SciPy lay the foundation for mathematics. Panda’s package, on the other hand, establishes an intuitive and user-friendly data structure, a data framework, specifically designed for analysis and model building. It is based on the matrix introduced by NumPy and is optimized for tabular, multidimensional and heterogeneous data. The most common manipulations, such as groupby, add, merge or fill, replace and use null values, can be performed on a single line. In addition, the package provides functions for importing data from various standard formats and other functions for quick plotting, retrieving basic statistics or output data.
SciPy provides a library of statistical tools that allow users to build models, and Pandas makes it easy to implement. The Statsmodels package builds on these packages by implementing more advanced testing of various statistical models. A comprehensive list of performance statistics and diagnostics for each estimator is available for a given model, with the goal of providing the user with a complete picture of the model’s performance. The results are tested against existing statistical packages to ensure they are correct.
So far, the packages I’ve listed are fair for the type of data being considered. When we consider financial models, we of course need financial data. This is where Quandle comes to the rescue. The Quandl Python module gives users access to vast collections of economic, financial and market data collected from central banks, governments, multinational organizations and many other sources. Most raw datasets are free to access after registration (you need an API key), with more advanced and deeper datasets available for a price.
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Zipline is a package that binds statistics, data structures and data sources together. This makes it clear that this is a great algorithmic trading library for Python
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