Module 5 Practical Applications and Cases

Practical Applications and Cases in Financial Analysis and Predictive Analytics 

In this module, we'll explore real-world applications of data handling and predictive analytics within the accounting and finance sector, emphasizing their relevance for Robotic Process Automation (RPA), compliance and anomaly detection.

The Link Between RPA and Data Analytics

Robotic Process Automation (RPA) is all about automating repetitive, rule-based tasks, such as data entry, data extraction, and report generation. However, RPA's true potential is unlocked when it's combined with data analytics. By automating data-driven tasks and then applying analytical techniques to the processed data, RPA becomes a powerful tool for extracting actionable insights and improving decision-making processes.

Predictive Analytics in Accounting and Finance

We'll also explore predictive analytics in finance. Predictive models can help organizations anticipate financial trends, identify early warning signs of financial distress, and make proactive decisions to mitigate risks. We'll work with real-world financial datasets, including accounting ratios from the Taiwan Economic Journal, to build predictive models using machine learning techniques.

As you progress through this module, you'll gain practical skills in data analysis and predictive analytics that are not only valuable for financial decision-making but also for enhancing RPA capabilities. By combining RPA with data analysis and predictive modeling, you'll be better equipped to automate financial processes, improve compliance, and make data-driven decisions that drive success in the finance industry. Let's embark on this journey of practical application and analytics in finance.

The Financial Times Bank Fines Dataset (2007 - 2015) 

The Financial Times Bank Fines dataset chronicles fines imposed on major banks from 2007 to 2015. It provides a comprehensive view of regulatory actions and financial penalties in response to various infractions, offering an excellent used case for demonstrating Python automation. This dataset allows us to: 

Automate Data Retrieval: Python automation can be used to scrape data from Wikipedia, Federal Reserve, and other sources using Beautiful Soup, Requests, and other libraries. 

Efficient Data Transformation: Pandas can structure and manipulate the data, facilitating in-depth analysis and visualization. Time Series Analysis: With libraries like yfinance and YahooFinancials, automated retrieval of historical stock prices and financial data is possible. 

Automated Ratio Analysis: Python's automation capabilities enable quick calculation of financial ratios using extracted data. Robotic Process Automation (RPA): The dataset is an ideal candidate for introducing RPA concepts, showcasing how Python can automate repetitive tasks in financial analysis.

The playlist below demonstrates in detail how to retrieve Financial Statements:

Benefits of Python Automation

Automating data extraction with pandas, Beautiful Soup, and Requests offers several advantages: 

Efficiency: Automation eliminates the need for manual data extraction, saving time and effort. 

Consistency: The process ensures consistent data retrieval, reducing human error. 

Scalability: The same script can be used to scrape multiple webpages, enabling scalability. 

Repeatability: Automating the process allows for repeatable and reproducible results. 

Automating Bankruptcy Prediction using Financial Accounting Ratios in Google Colab 

Here we set up the process to automate bankruptcy prediction using financial ratios! In this tutorial, we use financial data analysis and machine learning (logistic regression) to predict bankruptcy risk in companies. Dataset Source: We'll be using a real-world dataset sourced from Taiwan This is also available for download on Kaggle This dataset contains a wealth of financial accounting ratios (FRs) and corporate governance indicators (CGIs) used for bankruptcy prediction. You can easily access this dataset on Kaggle to follow along and experiment with the code artifacts we provide. 

Below we will show you how to utilize Python's powerful Pandas library to identify the factors that drive bankruptcy among these 95 accounting ratios. 

You'll learn how to expand this dataset to apply machine learning approaches to process larger datasets automatically. 

We'll deploy logistic regression, a potent classification technique, and demonstrate its application for bankruptcy prediction. 

The video instruction below maps out key steps in operationalizing machine learning which can supercharge robotic process automation (RPA) to discern factors that influence bankruptcy, paving the way for autonomous financial decision-making. Key insights into risk assessment can be revealed. Guidance will be provided on how to assess the credit risk of companies by identifying key bankruptcy-contributing factors. Confusion matrices are generated and these can be mobilized as a crucial tool to evaluate the accuracy of bankruptcy prediction models. These matrices provide a granular breakdown of true positives, true negatives, false positives, and false negatives, empowering analysts to understand an anomaly detection model's performance. 

Kaggle and Google Colab: Throughout the video, we will implement various code artifacts both on Kaggle and Google Colab. Kaggle, in particular, is a treasure trove of datasets and code snippets contributed by the data science and python community.