For modern businesses, making decisions based on solid, reliable information is essential. In recent years the range of data available for analysis has expanded considerably: with the spread of digitalization and data warehousing technologies, large datasets covering many business activities have become accessible. At the same time, technological solutions capable of running computationally intensive analyses — sometimes in real time — have emerged. As a result, the use of data-driven decision-support models has grown, and machine learning algorithms are increasingly applied.
What is machine learning?
Machine learning is a collective term for algorithms designed to uncover relationships in data or to make decisions without human intervention. The learning process typically uses historical data and observed decision situations: based on these, the system makes forecasts or takes actions for future cases. Human roles are mainly preparing the databases, selecting the appropriate type of algorithm, and defining training and testing rules. Practically, the dataset is usually split into a development set and a test set: the former is used to tune model parameters, the latter to measure the model's effectiveness and stability.
Recently, more and more powerful and sophisticated machine learning algorithms have appeared, offering better opportunities to reveal information hidden in data. However, training these models increasingly requires more computational resources and expertise.
Applications in finance
Models based on machine learning can be applied wherever sufficient data are available, and finance is a particularly suitable domain. Machine learning can uncover patterns in financial data that traditional methods may miss.
Practical use cases include:
- Credit risk prediction: a bank can estimate at the time of application how likely a borrower is to repay loan installments.
- Marketing targeting: identifying which of a company's customers are worth approaching with a campaign.
- Bookkeeping automation: assigning incoming supplier invoices to general ledger accounts and VAT codes can be automated, provided several years of accounting and invoice data are available.
In the bookkeeping example, the algorithm learns continuously from entries approved by accountants: after an accountant corrects a booking, the system is more likely to classify a similar invoice correctly next time.
Keys to successful deployment
Successful application of machine learning in business depends on several key factors:
- High-quality data: the data used for analysis must be clean and reliable.
- Clear problem definition: the task to be solved must be well specified.
- Expert oversight during training: human supervision ensures the model's learning and behavior meet business expectations.
- Integration into business processes: model outputs need to be embedded into daily workflows and systems.
Without these elements, machine learning implementations are less likely to deliver the expected efficiency gains; when they are in place, however, they can reduce routine workload and improve decision support in financial and administrative processes.
