Sun, 05 Dec 2021

Data Science in Finance - Beginners Guide

22 Oct 2021, 22:51 GMT+10


Data science has found inroads into every industry. Virtually every organized industry has realized the benefits of data science techniques to derive useful and game-changing information from historical and live data streams. Finance is no exception and has many applications of data science already in use. The sections below look at how data science is being used to plug gaps and overcome challenges in the fiancé sector.

Data Science in Finance

Finance is usually about compliance, typically cumbersome, time-consuming, and resource-intensive. There are many aspects of finance that need repetitive tasks, while some tasks require niche skills in finance. For example, Order to Cash is a repetitive task, while risk analysis is a more advanced and nuanced skill. Data science has found a place in almost all fronts in finance, be it small, low-value tasks or high precision analytical skills.

Data Science in Finance - Order to Cash

With repetitive tasks comes automation. This is very true of Order to Cash, one of the processes in finance. Data science techniques like image processing and natural language processing help perform tasks that require a human. These tasks were prone to errors and delays. With the advent of data science, these tasks take place in the blink of an eye. Analysts or finance practitioners can then focus their energies on high-value tasks.

Data Science in Finance - Risk Analytics

Finance is usually involved in assessing risk in fresh contracts, investing in new assets, or buying out another business. This involved intensive work by analysts, carefully analyzing all risks involved, like collaterals, existing debt, ability to repay the credit, non-performing assets, value of all assets, and more. With data science, risk analytics is a holistic experience. All possible risks are analyzed and highlighted even with data, not in the standard form of spreadsheets or documents. Many artifacts in finance have scanned copies with no readable data item attached. This is where data science helps with its Natural Language Processing and Image Processing techniques to extract data from artifacts where the process would have needed a human resource. This improves the processing time and accuracy of risk analysis with deeper and valuable insights.

Data Science in Financial Fraud detection

Financial fraud is not something that happens overnight; the perpetrator leaves behind many signals and marks. Some frauds are long-term, where the extent of the fraud is detected very late. Others are quickly committed and leave the scene. Many of these frauds violate some financial business rules and regulations in one way or another. These usually happen with the help of an accomplice, and thus these violations never get reported. Sometimes these violations are never picked up by the business in any of their reviews. Data Science changes this and brings in the much-needed eye on every detail. When financial rules are violated, it is immediately available for analysis, and data science techniques and processes can bring the fraud to light as soon as it starts. This avoids large-scale financial losses and loss of reputation in the marketplace.


Data Science is a holistic approach where all data involved is captured and analyzed, even non-textual or numerical. You virtually secure your financial system against any fraud. Data science will be a dominant technology of the future and will find use in virtually all aspects of your life. If this stirs interest in you for data science, you can learn data science, its various techniques like Machine Learning, Deep Learning, Natural Language Processing from experts in the industry at Greatlearning. These data science courses and programs are designed in collaboration with Great Lakes and Texas McCombs UT. Greatlearning also offers data science certificate online courses that are industry-recognized certifications.


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