Financial institutions are very cautious when it comes to integrating robotic process automation in the operations. That does not mean that they have rejected the technology entirely, RPA has been used in tightly articulated pilot programs with clear objectives. Quite often, the first obstacle in implementing RPA begins with the qualification of the correct processes to automate.
Let’s look at five key segments of finance that can be enhanced and easily automated using RPA platforms.
1. Accounts Payable
There has been a sustainable amount of apprehension when it comes to the inconsistencies and lack of structure when dealing with invoices. RPA would be an ideal solution to tackling these problems. This stems from the reliance of key RPA tools on OCR(Optical Character Recognition) technology which, in the past decade, were quite inefficient. However, there have been significant advancements in making the software more accurate and mature as well as added features like automated approval/exception with make RPA tools are an enticing option for businesses.
Currently, the benefits of RPA tools far outweigh its limitations. Businesses can save both time and money by using automation to significantly reduce error rates. When applied to payroll, it ensures quick and systematic invoice generation and end to end payment processing. Additionally, the ability to easily replicate RPA processes into various financial and business operations means that it is easier to scale with business growth.
2. Tax Accounting
Global financial institutions invest huge sums every year in outsourcing their tax accounting activities. These activities are usually done manually and repetitively involving acquiring accurate information regarding trial balance, fixed assets, calculating deferred taxes and reviewing tax returns.
They also have to be in compliance with ERP platforms, financial management and payroll processing software. RPA applications are able to achieve quick results when it comes to tax automation and saves huge on offshore accounting. Another salient feature is that RPA tools are non-invasive meaning they do not require adjusting or customizing existing processes to integrate into the business’s technology ecosystem.
3. Bank Reconciliation
Businesses spend a lot of energy manually reviewing and validating online transactions. Although some solutions have proven beneficial, especially when it comes to journal entries, they are still incapable of handling large transaction volumes, irregular processes and seemingly limitless sources of data.
One of the early operations identified for automation by experts was reconciliation. It led to businesses implementing cost-effective solutions while reducing the workload of employees, thus allowing them to make more valued contributions.
Using RPA tools, businesses can develop applications for reconciliations that offer complex data comparison, automated journal entries and long-term archiving.
4. Account Opening and Closure
In today’s fast and dynamic environment, bank relationship managers have quite a demanding job. They are responsible for hundreds of accounts allocated to them at different stages in their relationship with the bank. This gives them the unenviable task of managing these accounts while simultaneously ensuring the best customer experience for all of them. An uphill battle if there ever was one.
As a result, automation has been an incredible asset for relationship managers. It has been key in changing the way people open accounts through eKYC. RPA applications can simplify customer onboarding for relationship account managers allowing them to easily monitor credit ratings, analyze default risks and process eKYC through automation.
Likewise, RPA applications can also handle account closures upon request generated by the bank and accordingly update the banking system, generate a transaction ID and input MIS with its appropriate status. The accuracy afforded by RPA systems would be critical in reducing errors in deferred recovery charges or closure rates.
5. Fraud Detection
One of the most complex and costly issues for financial institutions is fraud and identity theft. Many organizations have systems in place to proactively handle fraud detection. Utilizing RPA techniques allows banks to monitor and eliminate threats before they cause any damage. Automated alerts and notifications signal to the involved parties on any attack and they can also be used to trigger the recovery process if there has been any damage.
Automation coupled with machine learning and predictive analytics applications can assess and analyze existing data and compare them with previous threats to develop a defense mechanism that is capable of identifying and distinguishing between real and fraudulent transactions.