How To Implement RPA In Banking

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2nd January 2023
7 Minute Read

The changing face of the banking sector has been transformed in recent years by new trends and innovations that now appear to be permanent features. The rapid growth of the Fintech sector and the proliferation of online banking, where customers are learning to constantly up their requirements, have been a solid force for change.

In addition, Covid-19 resulted in a profound shakedown to many businesses with extensive disruption. However, to more astute decision-makers Covid 19 did offer something of a silver lining, in making more time available to initiate automation projects. 

Prior to Covid 19, there were fewer external threats to the banking sector, so, at that time, it was easy for RPA to fall prey to inertia. However, with the sudden threat to all businesses, the pandemic acted as a natural catalyst, spurring a new wave of RPA in banking across the board.

Historically, many of the routine tasks in banking are carried out manually by humans. This work is often repetitive and mundane, and consequently prone to a higher level of errors. It’s with RPA that these plentiful, more basic, and rule-based tasks can be transformed into faster, more accurate, and more effective calculations. Robotic process automation (RPA) simply mimics human input but much more quickly and accurately.

 

Why are financial institutions using RPA?

 

RPA is a useful step toward digital transformation. In the implementation of an RPA program each of the steps is clearly documented and standardized. It’s this standardization process that is a fundamental part of digital transformation, where all company processes become standardized.

By their nature, a considerable part of the work of financial institutions is made up of selecting and transferring small amounts of data between sources. Software companies such as UiPath and Blue Prism have created RPA bots, which can be programmed without the need for understanding computer code.

Some financial institutions have gone one step further and use more advanced setups with RPA, where it is combined with artificial intelligence (AI) to execute more complex tasks. For example, AI has the capacity to scan semi-structured and unstructured data from text and web pages and convert it into a structured format that an RPA bot can interpret.

 

How to implement RPA in banking

 

It’s a fact that face-to-face banking in branches is rapidly becoming a thing of the past. Solely on the savings of staff wages, it is a no-brainer. As customers became more confident with the reliability and security of online banking its convenience naturally became its best-selling point. Covid 19 was to become the driving force that only accelerated this trend.

Research proves the point: Lloyds, by the end of 2022, was completing the closure of 60 branches in the UK. Over the past two years, the bank had seen a 12% increase in online banking users and a 27% increase in mobile app users.

So, customer requirements are changing fast, the market is changing fast, and business solutions are showing rapid progress at the same time. RPA solutions alone are quite limited in the tasks they can execute. For example, an RPA bot can be programmed to log into a database, move files about and then log out.

However, with the advent of artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) combined with RPA, suddenly the complexity of tasks that can be undertaken is greatly increased. For example, the combined functions can easily interpret human language and adapt to real-time data.

In implementing any RPA program it’s vital that adequate research and planning are applied to create the bedrock that will support the processes over the years. At the same time, the program needs to be designed with enough flexibility, to accommodate changing business, customer, and market conditions.

Digital transformation is more relevant than ever in the banking sector and one of the cornerstones of its success is applying a holistic viewpoint. The transformation needs to take place and connect with all the systems. A piecemeal approach where some systems are automated, and others are not, will only lead to inefficiencies later.

Recruiting an agency to help with the design and implementation of an RPA program is an important part of the process. The consultants will have built up extensive knowledge and experience in software, logistics, and planning which will reap rewards way beyond a project that is designed in-house.

There are several steps to the implementation process:

  1.   Assessment

Analyze the existing operations and decide which would be the best candidates for RPA. Note regular issues associated with the operations and how RPA could streamline them.

  1.   Create a business case

Calculate the cost of RPA investment required (time and hardware) and the real financial gains RPA will offer during operation.

  1.   Implementation strategy

Using the collated information create a strategy, which includes the operating model and the staff involved.

  1.   Implementation

Ensure all stakeholders are fully involved in the implementation. They need to learn about its operation, so they can be the eyes and ears of the system. Conduct regular follow-ups after implementation to ensure the RPA is functioning to desired standards.

 

The Benefits of RPA in Banking

 

Low infrastructure costs

 

In an implementation, RPA doesn’t need any changes to the existing infrastructure. When the project is cloud-based RPA the hardware and maintenance costs are significantly reduced.

 

Fast implementation

Many modern RPA solutions now have drag-and-drop capability. This enables trained non-IT staff to implement and maintain workflows without the need for coding skills.

 

Compatibility with legacy data

With the standardization that RPA brings financial institutions can finally integrate new data and legacy systems into one. The insights from the redundant legacy systems help to create more comprehensive reports for better business decisions.

 

Enhanced security

With the removal of manual processing RPA immediately reduces the risk of errors. Any RPA implementation needs to be the subject of regular audits and risk assessments to ensure it is operating effectively against the financial institution’s governance framework.

 

Use cases of RPA in Banking

 

The role of RPA in fraud detection

Anti-money laundering compliance is a multibillion-dollar requirement for the financial institutions of many countries. However, when implemented manually this compliance is often highly inefficient and particularly labor-intensive.

Analysts often spend 75% of their time collecting data from different sources and another 15% inputting the data for verification. These two tasks, data collection and inputting, are ideal candidates for RPA, which can significantly improve speed and accuracy.

RPA can also be a vital tool in detecting fraud by highlighting suspicious patterns. If an account is suddenly subjected to numerous transactions in a short space of time RPA can immediately trigger an alert and notify the best department.

 

The Role of RPA in Mortgage Processing

The use of RPA in mortgage processing is a success story and well documented.

The complexity of a mortgage loan often means the application can take anywhere up to 60 days to complete. The different aspects of employment, credit, bill, and identity checks are routine but lengthy, although fortunately ideal for transfer to RPA.

RPA in mortgage processing can often lead to reductions in the speed of loan applications of up to 80% and a reduction in application processing of 70%, hence the popularity of RPA.

 

The Role of RPA in Accounts Payable

To process accounts payable manually, employees need to digitize vendor invoices and validate the fields to enable payment processing. When RPA is combined with optical character recognition (OCR) the overall payment process is significantly speeded up.

The OCR is designed to extract the invoice details and transfer them to the RPA, where they are validated and the payment is made directly if verified. Any accounts which don’t pass the verification process are forwarded to staff members for investigation.

 

The Role of RPA in Report Generation

As part of their responsibilities for compliance, the bank, and financial sector have a requirement to regularly produce a number of reports, such as monthly closing, management reports, and numerous reconciliations.

The volume and complexity of the data these reports contain cannot be minimized, which creates a continual challenge. The accuracy of the reports is vital, as well, as they are usually presented to the Board of Directors.

RPA has shown itself to be a proven alternative to the manual generation of these reports. RPA bots can be easily programmed to collect complex data from different sources, convert it into a standardized format, and even generate different reports.

In a similar application, RPA has also been used to generate suspicious activity reports (SAR). Under financial law financial institutions have a duty to report suspicious activity, which might involve money laundering or the financing of terrorists to the National Crime Agency.

RPA linked with NLP is a powerful tool, which can extract the required information from the correspondence and transfer it onto the standardized SAR form.

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