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Machine learning is a subset of artificial intelligence in which programs improve their results
independently, without explicit manipulation by a person. While humans play an essential role in
setting goals for and training artificial intelligence, machine learning learns from data
inputted into algorithms and finds meaningful relationships and patterns in that data on its
own. It then improves as more clean data is provided.
AI, machine learning and automation are changing finance jobs, not replacing them. Most commonly,
their implementation helps to free up finance professionals from mundane, repetitive tasks to
focus on more managerial and strategic activities, such as supervising the technology and
disseminating AI insights throughout the business.
AI in accounting and finance plays two fundamental roles: It serves as a tool to
improve efficiency and a strategic guide to identify complex patterns, perform
granular analysis and generate insights. According to our latest industry survey, the top use cases among AI
adopters include predictive analytics and forecasting, process automation for internal workflows
and customer personalization. For a deep dive into the benefits of AI, check out our Finance Transformation
AI, whether generative AI or a customized machine learning model, has a
multitude of use cases in finance, such as:
Automating data import, export and visualization by pulling large data volumes from multiple
Automating financial reporting, flash reporting and data summarization
Flexible budgeting, forecasting or scenario planning based on data insights
Generating, scanning, executing invoices or building expense reports and contracts per
Improving fraud detection by identifying transaction anomalies
Automating month-end close tasks like error detection, transaction matching or
The key is to start with strategic business objectives and unique challenges, then evaluate how
AI can solve those issues, rather than leading with the technology.
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