There’s no shortage of technical buzzwords to keep today’s business, accounting, and finance professionals entertained and engaged when contemplating the future of their profession.
Cloud computing has captured the mind of mainstream accounting, and the concepts of big data and real-time analytics are not far behind.
One term that’s finding traction in business and accounting is machine learning (ML). Experts regard machine learning as a major branch of artificial intelligence, and for good reason.
Machine Learning From Experience
ML is just what the name implies: a computer system’s ability to learn from data over time, identify patterns, and make informed decisions, similar to human cognitive ability.
Machine learning uses algorithms—sophisticated mathematical formulas for programming instructions—to do its work. What sets ML apart from simpler robotic models is its ability to discern complex patterns and learn from experience.
In a simple, factory-floor application, for instance, an ML model might capture streaming data from production machinery and compare that data with contextual knowledge of a given machine: its repair history, normal performance range, and so on.
Based on that knowledge, ML might find that the machine is using a component that is likely to fail in the coming weeks. Through its predictive intelligence, ML could then recommend proactive replacement of the part.
The learning function of ML is typically broken down into four major types:
- Supervised. The ML model is trained, based on examples, to recognize patterns in labeled data. That is, it carries some clue as to what it represents. As it’s fed more labeled data, the model’s outputs become more precise.
- Unsupervised. The model is tasked with finding patterns that can be grouped by similarities in unlabeled data.
- Semi-supervised. A combination of the first two, where some data is labeled, but most is not.
- Reinforcement. The model tries to improve its scores based on feedback from initial results.
Adding Computing Power
Machine learning typically relies on massive data sets and powerful computing systems—often arranged as layers—or neural networks. Neural networks function similarly to the human brain: they are arranged in algorithmic layers, with each neuron-like layer feeding the results of its computations into the next for further processing.
Machine learning is widely used in Finance and other industries.
Banks and other financial services providers use ML to identify investment opportunities and prevent fraud. Large accounting and consulting firms use it to help auditors analyze legal contracts and deeds.
It’s also used to iron out complex billing problems for healthcare clients. Manufacturers, telecommunications companies, and many others are using it to drive sales and for a number of other applications.
The Human Element
In Accounting and Finance, expect to see ML functionality in a growing number of automated processes. Expect it to do its job quietly and well, without the spinning wheels and flashing lights of Hollywood’s early depictions of robot-like computers.
Future ML implementations will be far more similar to discernible human cognitive behavior than many people realize.
“Machine learning does not take place in a vacuum,” says Sapna Nagaraj, BlackLine’s Director of Machine Learning. “Instead, it is very closely tied to human behavior.
“For example, our ML solution for Transaction Matching applies the knowledge of how users have matched transactions manually in the past, analyzes their thought process and mimics the same, thereby building trust in how the solution performs.”
Read this blog to hear directly from our experts how machine learning is expected to add even more value to this BlackLine solution.