Machine learning feeds on an enormous amount of data. It uses intelligent algorithms and statistical models to produce in-depth insights and make predictions, and constantly improves in the process.
Machine learning “learns” from the datasets it receives, repeatedly, and makes connections between datasets in more ways than humans can. It also considers the variables that manipulate the data in past events.
Financial operations can benefit the most from machine learning, compared to other industries, simply because the industry produces volumes of data from a multitude of bookkeeping records, ledgers, and numerical history.
So, it makes sense that machine learning is leveraged since critical and numerical analysis, as well as economic predictions, are key functions of finance operations.
The technology is not only able to automate the analytical processes of past and present data in financial operations, but will also continuously learn about new data entry.
Not to mention, it will add intelligence to available data insights, and identify patterns in the processes involved, to learn from the data and improve itself.
There are many analytic processes that can be critically enhanced by machine learning, so CPAs must start with the processes that involve repetitive human effort.
This is a crucial first step in the process because choosing the wrong processes that can simply be automated by other forms of technology will not add any value to the operation.
Among processes that can be leveraged using machine learning are risk assessment, asset management, and loan approval.
Once a decision is made, a strong case proposing all the considerations involved in choosing to adopt the solution must be produced. This would include perceptible benefits such as increased productivity, efficiency, accuracy, consistency, and optimization of the talent pool as well as a quicker decision-making process.
Other attributes range from production cost, talent requirement, volume of data, and employability of technology in a fool-proof timeline.
CPAs must also realize that the automation of these processes will rid algorithms of human bias, human error, unlawful data regulation as well as strictly reduce the possibilities of corruption.
Finally, a clear understanding of how machine learning operates and how it advances with time and data must be achieved. The most important material in building a machine learning solution is the right amount of data and a good data science team.
Needless to say, it is pivotal to have a clear plan of what other features of the technology will be needed for it to produce tangible predictions, insights, and analytical intelligence to best complement the operation’s business model.
A key concept to bear in mind: Enhancing financial operations with machine learning is not merely for innovative purposes. Instead, it is a means to find operational solutions and to shape a ‘smart’ economy.
Source: techwireasia
