Financial Leadership in the AI Era

AI in Finance: Separating Hype from Reality This is the first installment in my series “Financial Leadership in the AI Era.” If you’re new here, check out the introduction post to learn what this series is all about. The Current State of AI in Finance Departments Three weeks into my new role as a finance manager, I’ve been cataloging every vendor pitch that mentions AI, machine learning, or automation. The result? A spreadsheet with 23 different solutions, all promising to revolutionize our finance function through the power of artificial intelligence. ...

Building Your Finance Team’s AI Literacy This is the second installment in my series “Financial Leadership in the AI Era.” If you missed it, check out the previous post where we explored separating AI hype from reality in finance departments. The Literacy Gap in Finance Six weeks into my role as a finance manager, I’ve identified a significant challenge: the varying levels of AI literacy within our team. During a recent discussion about potentially implementing an AI-assisted forecasting tool, I noticed reactions ranging from unrealistic enthusiasm (“This will solve all our forecasting problems!”) to deep skepticism (“I don’t trust any black-box system”) to anxiety (“Will this replace my job?”). ...

Ethical Considerations in Financial AI This is the third installment in my series “Financial Leadership in the AI Era.” If you’re just joining, check out the first post on separating AI hype from reality and the second post on building your team’s AI literacy. When Algorithms Make Financial Decisions Three months into my role as a finance manager, my team faced our first significant ethical dilemma with AI implementation. We were evaluating a vendor’s AI solution for credit analysis that promised to increase approval rates while reducing default risk. The system showed impressive results in the demo, but when we dug deeper into how it made decisions, we discovered it was using postal codes as a significant factor—potentially serving as a proxy for demographic information that could lead to discriminatory outcomes. ...

Financial Leadership in the AI Era: A Journey of Discovery Welcome to my new blog series where I’ll be documenting my learning journey as a finance manager navigating the evolving landscape of AI in finance. Why This Series? I recently stepped into a new role as a finance manager. Like many professionals in finance today, I’m facing a rapidly changing environment where artificial intelligence is no longer just a buzzword but an increasingly important part of our toolkit. Rather than pretending to have all the answers, I’ve decided to learn in public and bring you along for the journey. ...

Next Steps & Advanced Python Topics Welcome to the final post in our Python beginner series! If you’ve made it this far, congratulations—you’ve built a solid foundation in Python programming. In this post, we’ll explore some advanced topics and provide guidance on where to go next in your Python journey, with a particular focus on financial applications. Concurrency & Asynchronous Programming As your Python programs grow more complex, especially when dealing with financial data processing or automation, you’ll need to understand how to make your code run faster and more efficiently. ...

Building Simple GUI Applications with Tkinter Part 16 of my Learning Python in Public series As a finance professional diving into programming, I’ve realized that data analysis is only half the battle. Sometimes you need to package your calculations into user-friendly applications that colleagues can use without knowing Python. That’s where GUI (Graphical User Interface) programming comes in, and Python makes this surprisingly accessible with Tkinter. What is Tkinter? Tkinter (pronounced “tee-kay-inter”) is Python’s standard GUI framework that comes bundled with your Python installation. No extra pip installs needed! It’s based on the Tk toolkit, which has been around since the 1990s. While not the most modern-looking framework, it’s: ...

Python Learning Series: Web Development Foundations Part 15 of my 17-part series on learning Python as a finance professional After exploring all the fundamentals of Python programming, it’s time to put those skills to practical use by diving into web development. As a finance manager, I’ve found that building simple web applications can help automate reports, create interactive dashboards, and share financial data across teams. In this post, I’ll cover three popular approaches to web development with Python: ...

Introduction to Object-Oriented Programming (OOP) in Python: A Finance Perspective As a finance professional learning Python, I’ve discovered that Object-Oriented Programming (OOP) is one of those concepts that initially seems abstract but becomes incredibly powerful once you understand it. In this post, I’ll break down what OOP is, why it matters, and how we can use it to model financial concepts in our code. What is Object-Oriented Programming? In the simplest terms, OOP is a programming paradigm that organizes code around “objects” rather than functions and logic. An object bundles related data (attributes) and behaviors (methods) together. ...

Data Visualization Basics in Python Welcome to the thirteenth post in our Python learning series! In this article, we’ll explore how to create effective data visualizations in Python - an essential skill for analyzing and communicating financial data. If you’ve been following along, you’ve already learned how to work with data using NumPy and pandas. Now it’s time to bring that data to life through visualization. Whether you’re analyzing budget trends, tracking investment performance, or presenting financial reports, the right visualization can transform raw numbers into actionable insights. ...

Data Analysis with pandas for Finance and Accounting Welcome to post #12 in our Python journey! In the previous post, we explored NumPy and its powerful numerical capabilities. Now we’re taking a step up to pandas, which builds on NumPy’s foundation to provide specialised tools for working with tabular and time series data—exactly the kind of data we deal with daily in finance and accounting. Why pandas for Finance? pandas is specifically designed for data analysis and manipulation, with particular strengths in: ...