The article discusses the aspects related to the implementation of RPA in the banking industry. We will put forward 4 simple use cases of RPA in banking operations and will try to address widespread concerns leading to doubts on RPA implementation.
Cold splash of banking reality
While pondering the place of RPA in banking, we cannot ignore one of the most precious things in the business world. Time. To be more precise, the time of your clients. Have you ever put yourself in customers’ shoes when it comes to time limitations in banking services? If not, we’ll walk you through the whole process.
Just a simple example from a loan application procedure:
A customer calls the bank to apply for a loan. After a standard conversation with the customer service representative, he/she provides all the required details for the loan application, and the call is directed to the loan department. The loan department, in its turn, takes some time to verify all the data, check the customer’s credit rating, and then leave him in limbo, having promised to be back with the application status. The call usually takes 5 to 15 minutes with the additional 15-30 minutes of an excruciating wait.
A considerable amount of time, isn’t it? The truth is that time-consuming processes are a standard situation in banking. Yet, it does not take away the fact that in the era of “I want it now!” society customer experience issues come to the forefront and cannot be overlooked.
Now let’s suppose that this process can be reduced twice, triple, multiple times. In fact, that’s where Robotic Process Automation comes in handy.
RPA: What is it and what does it offer to the banking industry?
Robotic Process Automation, or simply RPA, is a term used to describe a technology that partially or fully automates manual human tasks. RPA is associated with rule-based, mundane, usually time-consuming activities.
Besides the case above there are a plethora of situations in banking where robots can enhance your customer experience, reducing precious time of your employees on repetitive and mundane tasks.
It can be demonstrated by 4 simple use cases of RPA deployment in various banking processes:
1. Use Case: Daily reporting
A loan officer spends up to 2 hours a day generating reports in the Encompass system (a software environment for working with mortgage loans).
An RPA-bot performs report generation in Encompass. It logs into the designated accounts in Encompass, generates a report and uploads data in an Excel spreadsheet.
Time saved: 2500 hours within the department per year
Bonus: Elimination of errors in reports
2. Use Case: Analytics and market research
An analytical department specialist spends, on average, 1 hour a day analyzing competitors’ prices and inputting it into designated spreadsheets
The robot opens predetermined competitors’ websites and makes a pricing information search. It then downloads the related open source data to the pivot table
Time saved: 1200 hours within the department per year
Bonus: Comprehensive and error-free analytical data, pricing enhanced
3. Account data management
Typically an accounting specialist spends on average 1 hour per day manually digitizing invoices through an OCR (Optical Character Recognition) software and processing the invoices data using various accounting systems.
The bot automates the accountant activities, digitizes various format invoices, processes the data into the designated databases, and reconciles and validates it for the further automated process of crediting the payment on the vendor’s account.
Time saved: 1200 hours within the department per year
Bonus: Accuracy of account management
4. Mortgage processing
A mortgage agent must go through several checks, i.e. credit check, inspection, and employment verification, and filling various sets of documents with duplicated information. Any error leads to delays and complications of the loan set-up process.
The particular algorithms and rules set up for the bot allowed to accelerate the validation processes and minimize the possible errors in documents.
Time saved: loan processing brought forward by 7 days
Bonus: Seamless mortgage loan processing
The list of RPA use cases in banking may be extended to many other areas, such as, for instance, compliance procedures, ticketing and customer support services, financial and operational planning, customer onboarding and monitoring.
The bottomline here is that simple-task automation use cases emphasize the top virtues of RPA implementation in banking, i.e.:
- Providing with 100% accuracy of data management and processing
- Enhancing customer satisfaction level
- Saving time on labor-intensive activities
- Tangible results and opportunities to scale it to other processes.
4 concerns of implementing RPA in banking and how to address them
While discussing the pros of RPA we cannot fail to refer to possible risks related to RPA implementation in the banking industry.
The point is that innovations and especially RPA cause a vast number of concerns for the banking industry. According to Harvey Nash/KPMG CIO Survey among 300 IT leaders on banking digitization, only 24% of banking enterprises invest in Robotic Process Automation to enhance their current workflow. The low figures here reflect the existing concerns of the banking industry about the challenges associated with RPA deployment.
The primary concerns around RPA in banking imply 4 key considerations that need to be addressed.
1. Technologies applied
Question: Is RPA technology compatible with the existing infrastructure and architecture?
Simple repetitive tasks automation does not require emerging technologies, NLP, and machine learning to be involved.
RPA implementation is seamless and it can be activated on top of your current applications. You do not need to make changes in the existing infrastructure and architecture.
2. Privacy and security issues
Question: Is RPA technology secure with regards to vulnerabilities, data storage, and transfer and privacy control?
Answer: Any RPA technology is built, taking into account the security issues on every level of automation.
Such tools as credentials vault, privileged access, multi-tenancy give you a hundred percent security and privacy control.
However, additional security actions in your organization might be helpful in building a safe ecosystem around robots. Basically, to reach this ultimate goal, the organization should take reasonable measures in the 3 key areas:
- Establish a sound strategy for RPA security in the existing organization security policies
- Implement R&Rs governance framework to mitigate possible risks
- Conduct security analysis and scanning within all the robotization processes: bot creation, running, authorization and authentication, analysis of the back end code which may produce vulnerabilities
Data access and credentials management:
- Use sophisticated schemes to manage access privileges for specific bots to perform only specific tasks
- Prevent credentials leakage using credential managers encryption
3. Workforce reluctance to changes
Question: Will I need to train the current staff members or hire a more technically skilled team to implement RPA?
Answer: RPA does not require coding skills or technical knowledge to be implemented within your ecosystem. Though it is vital to organize sound communication among team members and reduce the cultural aspect of implementing automation and possible overwhelming and anxiety around automation. The team should have a clear vision of the automation goals and the future opportunities it brings to the enterprise both on a general and an individual level
4. ROI estimation
Question: Is RPA a cost-effective solution?
Answer: According to Gartner research the average amount of avoidable rework in accounting departments can save 25,000 hours per year at a cost of $878,000 for an organization with 40 full-time accounting staff.
Mundane tasks automation may produce a rapid return on investment, possibly due to 2 facts:
Firstly, RPA requires low initial expenditures and licensing investments, the costs for staff training is also significantly lower than, for instance, in such related fields as machine learning
As for the pricing, in Electroneek, e.g., the pricing starts from $6 per month per user in addition to a free trial available for 14 days.
Secondly, comfortable RPA implementation timeline allows you to see tangible within less than a month.
We have put forward several simple examples of RPA use cases in banking and tried to address the most popular concerns regarding the possible risks on RPA implementation for the industry.
To summarise, despite the gloomy statistics on the slow-pace digitization in the banking industry there is no denying that automation is paving the way for the gradual raising of the role of the innovations in such a regulated and rigid field as finance.
In case of a sound approach to implementation in banking organizations, RPA could play an indispensable role not only for the digital transformation of such enterprises but, what is more important, in building better customer experience through an acceleration of typically time-consuming processes and elimination of excessive bureaucracy.