It is well-established that accounting is a data-intensive stream. It is filled with numerical, non-numerical, statistical and non-statistical data. There is data involved in invoices, purchase orders, employee salaries, daily expenses and a load of other things. Such enormous amounts of data can get overwhelming after a point. Accounting professionals have to undergo a tedious process to collect and analyze such enormous amounts of data. Moreover, the data is scattered at various sources.

Recent research from Accuity indicates that banks struggle with the time required for staff to perform manual repetitive compliance tasks. Labor still accounts for the largest cost of compliance functions. Management tends to view compliance functions as cost centers, rather than profit generators, while regulatory changes drive bank behavior. Most people today understand the power of a data-driven approach when it comes to their recent online shopping behavior: we purchase goods online and our preferred vendor will offer us previously bought goods that might complement our purchase. The more data this algorithm has, the easier it can make predictions and recommendations for users.

One of the key applications of machine learning for small businesses is by using it for tracking their customers at various stages of the sales cycle.