Python is a fast, powerful, and easy-to-use programming language that can benefit accountants significantly. However, it’s essential to remember that any programming language is only functional when used correctly, and mistakes could slip in from time to time. I’ve noticed several mistakes that accountants tend to make when using Python. Let’s explore them a bit.
Accountants who use Python to automate their workflow often face pitfalls when they neglect data validation, overlook reconciliation processes, ignore scalability, fail to secure sensitive data, rely on manual processes too much, and try to over-automate processes without proper human oversight.
In many cases, a programming mistake simply causes errors. Though errors are frustrating, they don’t usually cause any severe problems. That’s not the case with accounting, where errors in tax preparation code could lead to significant penalties and fines. That’s why it’s so crucial to avoid these common mistakes.
Common Python Mistakes Accountants Make
As an accountant, you’re used to being thorough with your work. Everything must be calculated and completed with meticulous attention to detail to avoid giving clients inaccurate information.
This should be no different when you use Python. I’ve found that most of the mistakes accountants make when they start automating their workflow with Python happen because they fail to consider all the factors involved. They also don’t implement the same attention to detail they use in manual processes.
Neglecting Data Validation
One of the popular Python applications for accountants is data entry. Python makes it easy to import data from spreadsheets, PDF files, accounting software, or other documents. However, we can still manually enter and manipulate data using Python scripts, and mistakes could sneak in somewhere between the automatically captured and manually entered data.
Incorrect data means flawed calculations, which affects the results. For example, in 2022, the Equifax credit reporting authority issued inaccurate credit scores for millions of customers, influencing their interest rates and leading to rejected loans. This was caused by code that didn’t allocate data to appropriate variables, leading to errors and miscalculations.
It’s absolutely crucial to build verification systems into your code to check that data is valid (the correct type and format) and accurate to avoid these types of problems.
Overlooking Reconciliation Processes
The fact that our accounting systems automatically capture and process data doesn’t guarantee everything will always run smoothly. Let’s be honest—things don’t always run smoothly when we do things manually, either. That’s why we have account reconciliations. They are a way for us to double-check all data to guarantee accuracy.
Python automation doesn’t eliminate the need for account reconciliation. We can (and should) do this manually from time to time, but we can also build reconciliation processes into our code. It simply means writing code that compares one set of data with another and gives us a warning if it detects discrepancies. Adding the code only takes a few minutes but could save hours of trouble.
Ignoring Scalability
The goal of any business is to grow, and the same is probably true of your accounting firm. As the company grows and takes on more clients, we tend to bring in more employees to handle the increasing workload, but we often fail to consider that our software could also have difficulty keeping up.
We should look at this from two angles:
- Our code should be robust enough to handle large quantities of data. When the firm is small and only deals with a handful of customers, writing code that caters to that small number using limited variables could make sense. When we grow, we suddenly find that our Python scripts can’t cope with how much data we throw at them.
- Our computer systems may not be powerful enough to run more demanding scripts. As the amount of data increases, our servers and computers might struggle to keep up with the demands of the code we’ve written. It’s essential to upgrade our systems along with our code, but we should also keep efficient coding practices in mind as we write our Python scripts.
It’s better to start coding with the end goal in mind rather than upgrading or tweaking our systems occasionally. Take the time to ensure your code is efficient enough to use the least amount of system resources but powerful enough to cope with your company’s future growth.
Failing to Secure Sensitive Data
As an accountant, you are writing code that will deal with the highly sensitive data of several clients, and there are plenty of bad actors out there who would give anything to get their hands on that data.
However, the risk isn’t just hackers. In fact, around 74% of all data breaches happen due to human error! That’s why you should keep security in mind as you write your code, but on two separate levels:
- Write your code so that all data remains encrypted and out of public hands. Also, keep Python and all libraries and software updated to patch vulnerabilities.
- Build access controls into your code so only authorized people can view and access the data.
Over-Automating Without Human Oversight
The next mistake is going into automation to such an extent that you don’t need human oversight. It’s true that well-written code rarely makes mistakes, but is “rarely” good enough in an industry like accounting?
Build systems into your code that allow for human inspection and intervention to ensure everything works as it should.
Relying Solely on Manual Processes
The last common Python mistake I’ve seen many accountants make is not using Python at all. Maybe they don’t trust technology, or perhaps they don’t want to learn a new skill. Either way, they choose to keep on doing everything manually.
This is a big mistake. Python allows you to decrease your workload exponentially by taking over many repetitive processes, freeing up your time to focus on more business-critical tasks.
Conclusion
After automating their processes with Python, most accountants wonder how they ever managed without it. However, it’s easy to see why many are hesitant once you consider the potential horror stories that could emerge from the cracks of coding mistakes. Always be mindful of security, efficiency, and scalability as you code to reap the benefits without the risks.