How Does Machine Learning Work in Finance Automation?
Discover why automating financial services is quite different and challenging from other business domains due to a high level of caution, concerns, and risks.
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Automation in finance, or any other sector for that matter, is inevitable. However, automating financial services is quite different and challenging from other business domains due to a high level of caution, concerns, and risks.
According to the survey used for the above infographic, 73% of finance experts believe that automation boosts their function efficiency, and hence, the finance team can focus on other value-added activities. At the same time, 87% of CFOs believe that they need to be more agile to analyze their financial services and meet their targets.
Looking back at the traditional systems, financial operations were supported by the conventional on-premise ERPs. Hence, the financial procedures were slow and prone to errors due to manual work. Thanks to the emergence of technology like deep learning and machine learning, the automation rates in finance operations are rising like never before.
What Is Finance Automation?
Finance automation simply means automating financial functions or completing financial processes with little to no human intervention. It involves a series of jobs that can be singly handled by technology using predefined steps. There are different levels to automation, but the end goal is to minimize dependency on humans for the tasks.
Different organizations are adopting automation differently, whether in the form of robotic process automation for basic rote-task automation or artificial intelligence and machine learning automating intelligent decisions.
It is pretty interesting to note how automation in finance is easing up jobs and, at the same time, helping accomplish more by increasing agility and efficiency. Currently, the RPA industry is expected to be worth $2.9 billion by the end of the year 2021. Automation in finance is not just a mere possibility, but a reality.
How Does Machine Learning Work in Finance Automation?
Machine learning algorithms can work with large amounts of raw data and draw meaningful insights. These results are later used to solve complex problems of financial domains.
There are various applications of machine learning in finance like algorithmic trading, financial monitoring, process automation, risk management, making investment predictions, financial advisory, securing transactions, and many more.
As every business has different objectives, procedures, and needs, having tailor-made solutions is the key to a successful implementation. Machine learning in finance can lead to better productivity, saved costs, better compliance, and improved user experience.
Benefits of Finance Automation
There are multiple reasons why companies - startups and leading enterprises alike - are adopting finance automation using different technologies such as machine learning, RPA implementation services, chatbots, etc.
Here are some of the benefits of finance automation:
Increased operational efficiency of the system
Reduced manual errors
Consistency of the system
Increased employee satisfaction
Reduced risks and frauds
Time for more strategic work
3 Steps To Implement Finance Automation
Automating a core function at an organizational scale is no small task. Planning and research before getting started are essential to a successful implementation. Here, we have discussed some of the critical steps to guide you through the finance automation process:
1. Analyze Your Financial Processes
Not every task is suitable for automation. Therefore, the first step is to identify which process you wish to automate. The best way is to draw the workflow on a whiteboard and identify the streamlined tasks and where you need to make changes.
Consider the below two points to ease your task:
Identify the repetitive and frequent functions which are performed manually.
Identify the jobs that do not require decision-making or strategic thinking.
Once you filter your tasks that meet these criteria, do not attempt to automate all of them at once. Arrange them according to your priority list and assign them to respective team members.
You must be wondering: why assign a task to a member if it is to be automated? Even though the system is running automatically, it is wise to maintain the workflow and streamline operations under the guidance of a person. The finance sector always comes with lots of complex tasks and risks, and therefore, having someone who ensures the system runs smoothly without any error is a good call.
2. Create Digital Workflow
As it is said, “A goal without a plan is just a wish.” Similarly, setting up the workflow is vital during automation as it guides how your technology will work and move forward.
Building a perfect workflow that captures all the essential information is recommended, considering the conditions, sequence, and risks you might face while automating your financial tasks dynamically.
Every workflow consists of two parts: a trigger and an action. You can consider triggers as the predefined task which helps to kickstart your automation system. At the same time, actions are the responses to the triggers generated. For instance, the trigger will suggest to the system that a particular payment is past due, and as the resultant action, an email will be sent to clear those payments.
3. Testing the Workflow
Creating the workflow is not enough. You have to test your workflow once you are done setting up everything. It will ensure that your system works efficiently when your finance team is busy working on more valuable tasks.
What’s in It for You?
Technologies today are quite capable of doing everything you ever imagine. Machine learning algorithms help enhance the productivity of financial businesses and improve their customer’s user experience.
Financial companies face low operational costs and better conformity by automating their systems with the right set of service partners experienced with machine learning models to implement.
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