BlogKristen Jansone
Kristen is a seasoned professional with extensive expertise in new business development, IT consulting, and IT outsourcing. Her in-depth knowledge and strategic insights make her a trusted advisor in the industry, helping companies navigate complex challenges and capitalize on emerging trends.
8 min
RPA in finance: 12 Innovative Use Cases
Robotic process automation (RPA) is probably one of the most exciting technologies. Initially, RPA solutions were used in the manufacturing industry to assemble automobiles, household appliances, furniture, and other complex products. But nowadays they have begun infiltrating the back office as well.
By implementing RPA software, businesses automate manual labor-intensive processes, minimize errors associated with the human factor, save costs, increase operational efficiency, and improve security. No wonder more and more organizations start investing in this technology.
As an experienced fintech software development company, AnyforSoft would like to discuss RPA in finance in more detail. In this article, we dive deep into the topic, covering the following questions:
What is RPA in finance?
How are modern businesses using robotic process automation?
What are the benefits of RPA?
What are the challenges of implementing RPA in finance and accounting?
If you plan to implement RPA tools into your own business, don’t skip this blog post!
What is RPA in Finance?
The first question we should cover is, what is robotic process automation?
In short, robotic process automation is a technology that utilizes software robots or bots to mimic human actions and automate rule-based processes across applications, systems, and data sources. Just like humans, RPA bots are able to perform many actions, such as completing the right keystrokes, navigating systems, identifying and extracting data, and so on. However, they do it faster and more consistently than people, without coffee and smoke breaks.
In the financial industry, RPA has the potential to optimize business processes and improve operational efficiency. By automating mundane and repetitive tasks, it enables finance teams to reallocate their time and resources to more important activities, such as financial analysis, decision-making, and customer service.
RPA typically goes hand in hand with artificial intelligence and machine learning. These technologies boost the power of robotic process automation in many ways. More specifically, ML and AI algorithms:
Prevent RPA robots from encountering errors when underlying rules change.
Find patterns in historical data to identify the most relevant information for decision-making.
Analyze data and predict outcomes that help organizations make well-informed decisions.
Let’s make it clearer with an example. Say, you’re using RPA software to consolidate data from various sources about customer payments scheduled to come in, along with invoices that will need to be paid. Machine learning can predict the likelihood that each customer will make their payment on time. Thus, you will be able to use these predictions to make informed decisions and take appropriate actions in the following areas:
Reducing administrative costs. By accurately predicting which customers are likely to make timely payments, you will be able to focus your company’s resources and efforts on following up with those customers who may face payment issues. This targeted approach will lead to reduced administrative costs related to collections and accounts receivable management.
Extending supplier payment terms. If a business can predict that certain customers are highly likely to make their payments on time, it may have more confidence in its cash flow. This increased financial stability enables the company to negotiate longer payment terms with suppliers, improving its working capital position.
Investing in new equipment. Reliable predictions about customer payment behavior help you better assess your financial position and cash reserves. With the assurance of timely payments from certain customers, your business has the confidence to invest in new equipment or expansion initiatives, potentially leading to business growth and increased efficiency.
By the way, we have a comprehensive blog post explaining the benefits of AI in fintech—make sure to check it to learn more about how artificial intelligence helps organizations in the financial sector.
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