Microsoft Excel has dominated the financial services industry for the last 20 years as the go-to tool for calculations, data analysis, and reporting. However, analyzing large and complex data sets on Excel can be frustrating, especially today, when firms have to comply with high quality control standards.
Based on our experience, Python – the programming language of the year in 2018 (according to TIOBE programming community index) – is the best alternative to Excel. Python can make research models faster, smoother, and more efficient (compared with Excel).
A key reason that explains Excel’s near domination, until recently, is that it is a multifunctional tool that can calculate, analyze, create, and share reports. However, these benefits come at the cost of quality control – a cost that the financial industry cannot afford, especially with tighter regulatory controls. Excel also lacks efficient error tracking, strong data controls, and version control, not to forget crashes and slowness when working with large datasets. According to MarketWatch, an astonishing 88% of Excel spreadsheets have errors. The consequences of these errors can be substantial, such as the infamous Reinhart-Rogoff error that wrongly influenced US austerity policies.
Evalueserve is technology agnostic when servicing clients. However, we have used Excel extensively over the past two decades to serve financial services clients. Therefore, we have in-depth understanding of the advantages and disadvantages of the tool. Given the emerging industry demand for stronger quality standards and large complex datasets, we have increased our use of Python to set up new processes and replace Excel-based ones. This has resulted in significantly more efficient and error-proof routines for our teams. Our experience tells us that widespread adoption of Python is inevitable in the near future.
In this post, we explore three cases in which Python can effectively replace Excel.
Python is a great tool for repetitive tasks, as it allows for automation of data collection from single or multiple data sources. The Python script is flexible enough to connect with any database, and allows users to check the last data point and efficiently scrape data from websites. Python libraries, such as NumPy, Panda, and SQLAlchemy, are ideal for data collection and storage, while Beautiful Soup and Selenium are designed for web scraping.
Financial institutions use thousands of Excel sheets, making it hard to integrate and analyze large volumes of data. Python provides the option of centralized calculation modules for effective processing of large data sets. Libraries, such as Scikit-learn and Scipy, are top choices for data processing.
Python can easily automate report generation, including the creation of charts and graphs. It can generate a PowerPoint or PDF output, thereby saving time spent on manually creating reports in Excel. For such uses, we recommend libraries such as Matplotlib, Seaborn, ReportLab, and Python-pptX.
Although Excel will remain relevant for single-use spreadsheets and for the creation of prototypes of recurrent models, Python is the way forward.
Access this CASE STUDY to see how Evalueserve leveraged Python to help a leading US investment bank achieve 90% efficiency gains. If you’d like to learn more about how Python automation and process re-engineering can help you, write to us.