How RPA Benefits The Finance Industry

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8th January 2023
4 Minute Read

Why is RPA important in the Finance Industry?


Recent research by Gartner concluded that at least 80% of finance leaders have already implemented or are planning to implement RPA in finance. This wake-up is a far cry from the years of meager innovation in the finance industry, where it seemed there was no imminent solution to improving the industry workflows.

The finance industry is highly complex and competitive, with a seemingly ever-growing burden of regulation to accommodate. Continual innovation is required to stay ahead of competitors, as new banking models extend the traditional roles, while springing up thick and fast.

Customer loyalty has also come under scrutiny. Previously, the complexity of transferring an account to a new bank meant customers were loyal through inertia. However, automated transfers have now left banks scrambling to retain their customers.

As more and more financial institutions gravitate to RPA in finance solutions, so customer expectations accordingly develop. Rapid transactions, phone banking, and instant credit approvals have become the norm, as the demise of hundreds of branches marks out this transfer as permanent.

Finance departments are under increasing pressure from the demands of different departments in the organization. Directors need accurate financial data which has been intelligently analyzed to make the best business decisions. While at the same time housekeeping the many business processes, such as cash flow forecasts, automatic payments, and business reports requires considerable input.

Fortunately, the information handled by finance departments is well-suited to execution by RPA software. The data is high volume, time-sensitive, and repetitive to deal with which makes it an ideal candidate. Once implemented RPA can significantly improve operational efficiency, return a fast ROI, improve accuracy and adhere to compliance regulations. RPA software is additionally much more responsive in adapting to unforeseen market changes, where rapid upscaling is required.


The Benefits of RPA in Finance


1.    Increased Productivity and Efficiency

Often RPA software can be installed alongside existing finance systems, which makes the installation far less intrusive. In operation, RPA completes tasks much more quickly and accurately than any manual operation. Timesaving can be usefully employed where members of staff are redeployed to work on higher-value tasks, which wouldn’t have been possible prior to automation.

The role of RPA software is diverse, and it can be applied to numerous applications in finance including training, fraud detection, audit validation, and credit card processing among others.


2.    Cost Savings

The significant cost savings of implementing an RPA program have been well-documented by industry commentators. Installing any radical software solution will naturally have its attendant upfront costs but it has been shown that RPA is one of the most effective office streamlining systems that provides a prompt and substantial ROI. RPA enables companies to stand out from the competition by improving productivity and efficiency, whilst at the same time creating significant savings in cost efficiency

By 2024 technological research consultant, Gartner, estimates firms will reduce operational costs by 30% by restructuring operational processes and implementing automation technologies.


3.    Improved Compliance and Audit Reporting which results in less Risk

For any financial institution trying to keep abreast of compliance and the documentation that goes along with it is a never-ending task of catch up. Fines and court cases to address non-compliance are frequent and expensive. Remedial action to correct non-compliance is time-consuming and frustrating.

The only way to stay on top of the latest regulations is by devoting money and manpower to carry out the required analysis and audits. These need to be converted into reports, which are then verified by the legal department and fed back to management.

An RPA implementation can encompass the entire compliance and reporting process. It can also track regulatory updates to keep on top of changes, as well as generate compliance reports automatically creating substantial timesaving.


4.    Improved Customer Experience

As customers come to expect mobile accessibility and customer service every day of the week, financial institutions need to up their game to offer the required level of service at any time. RPA software can be installed to create instant access to customer information, thereby speeding up inquiries and resolutions, as well as pinpointing any other service needs.


5.    Scalability

Before deciding on any automation implementation, it is always wise to check on its scalability, so that with increased demand the automation can be increased to cope with the new requirements.

If the capability for scaling either doesn’t exist or is inadequate, then a sudden increase in demand will leave the RPA unable to cope, creating a bottleneck. Similarly, if the RPA software works well in one department of the company but doesn’t extend to other departments then workflows will quickly become frustrated.

In planning and implementation, the full capability of the RPA software needs to be matched with the projected highs and lows experienced during scaling.


6.    Better Accuracy and Reliability 

The weaknesses of manually inputted data are well documented. Humans are skilled at many tasks but when asked to enter unstimulating data repeatedly, concentration slips and errors start to creep in.

RPA software doesn’t need to concentrate; it is always aware and able to carry out tasks precisely and without error 24 hours a day. Some applications now combine RPA with AI and ML, where very large datasets need to be processed effectively.


7.    Extraction of Information with Accuracy

The amount and complexity of the data a financial institution retains can present huge problems when it comes to searching for it and retrieval. Some organizations have turned to extending the capability of RPA with AI and ML. A complex new query can be checked against the learned history created from previous inquiries to quickly find the best solution. A manual attempt to do the same thing would take forever.

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