Robotic Process Automation is to Become a Growth Strategy for the Financial Services Industry

Robotic Process Automation is to Become a Growth Strategy for the Financial Industry
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With the expansion and maturity of computer processing capabilities, information technology has already reached the point where it can deliver human-like tasks. Although some individuals — including our clients and partners — think of robots as humanoids that act like humans, droids similar to ones in Star Wars, or mechanical arms like the ones used in Toyota factories, it’s not the case.

When we speak about robotics in the service sector, we predominantly talk about non-physical robots — like chatbots or ad hoc software — that allow companies to automate activities as though an individual was executing them across apps. Banking, financial services, and insurance (BFSI) organizations are actively introducing this kind of automation to advance process efficiency, all while reducing operational expenditures. The experience shows that when intelligent automation (RPA) is deployed properly throughout a financial business, stakeholders witness a 10-25 percent cost reduction. However, if you decide to proceed and bring in your employees to teach bots (cognitive automation), your return on investment (ROI) can reach 50 percent. Moreover, the revenue from robotic process automation in financial services and banking is expected to exceed $1 billion by 2023.

What is Robotic Process Automation in Banking and How it Works?

In such tertiary sectors as computer services, healthcare, insurance, and fintech, robotics can have multiple names, and some of them are ‘Rapid Automation’ (RA), ‘Intelligent Process Automation’ (IPA), ‘Robotic Process Automation’ (RPA) and Artificial Intelligence. Nevertheless, all these terms are inherently intended to describe the same concept and are applied to realize vital business objectives.

RPA in the banking industry means the use of specialized software and tools for carrying out recurring, rule-based, and high-volume tasks. Opting for intelligent automation services organizations can extend their problem-solving potential, increase productivity and accuracy of employees, and so demonstrate superior business outcomes.

RPA in the fintech and banking sector works by running through a determined workflow that provides robots with detailed instructions on what should be done at each step. Once robotic process automation software gains a roadmap, it can autonomously run necessary programs and perform tasks as specified by the requirements. These days many banks and financial institutions utilize RPA automation tools in account opening, KYC processes, anti-money laundering strategies, client request processing, and other recurrent tasks.

Four Robot Archetypes to Transform Your Operations and Fuel Banking Automation

As we’ve already clarified ‘what is intelligent automation’ and how it works, we can state that these virtual assistants can take over iterative human duties and so enable individuals to perform more complicated and mission-critical tasks. Consequently, employees can spend more time practicing their knowledge and skills to build better user experiences, encourage innovations, and offset risks. The four types of fintech automation robots that can become your next notable hire are:

#1 Verification & validation robots

Working closely with industry-leading banks and fintechs on financial software development activities, we know that your employees spend a tremendous amount of time reviewing and approving data provided by consumers, employees, providers, and partners. Besides that, each individual performs a manual checking routine in their own way and is likely to make ten mistakes out of every one hundred delivered tasks. Overall, there is no consistent and reliable flow, thus, compliance procedures can become a nightmare for your team.

Validation RPA tools in banking make real-time requests to external and internal systems to authenticate information during the KYC, Customer Due Diligence, and other processes. They are able to transform an error-prone operation into a smoothly-running banking automation project. Whenever a confirmation issue arises, the virtual employee notifies its human colleague, who is experienced enough to solve it.

#2 System interoperability robots

Today industry game-changers tend to opt for fintech development outsourcing and build their software on agile future-proof platforms. The sad truth is that most businesses operate on obsolete systems that have been massively modified over the years. When firms are acquired or merged, their basic systems continue carrying out duties in silos, adding much manual work.

Integrating (RPA) robotic process automation tools are utilized to bridge communication gaps between systems that are too cumbersome and complicated to be united.

#3 Scheduled robots

We all have pre-planned activities or events that take place on a regular basis. For instance, we buy birthday, Christmas, and anniversary presents for our beloved ones every year. Car maintenance should be done in a timely manner. We renew licenses or subscriptions at fixed intervals. Of course, we know that and have these tasks in the back of mind. Unfortunately, we all have busy periods and can unpremeditatedly miss the memo.

Triggered automation solutions for banking take on simple duties, but that cannot be completed until a particular event occurs. They are literally hardcoded to deliver on the mission when required, so you won’t miss deadlines or have to solve urgent issues in a big rush.

#4 Data input robots

The entry of data is often seen as a home stretch of business process automation in the banking industry. The procedure of copying and pasting readings from one app to another is time-consuming, unreliable, and costly when done by humans. Since most financial institutions are powered by legacy systems, integration can be too expensive or may require confusing logic chains.

This type of process automation in the financial services sector allows automating the synchronization of data without laborious integrations or solid investment. Bots do the job as if it was performed by humans, executing all the typical steps, like logging into the system, copying, or inserting records. Consequently, with such data tools used in robotic process automation, companies can not only avoid high costs but also boost employees’ productivity.

RPA Financial Services Use Cases and How You Can Capitalize on Them

In the grand scheme of things, up to 80 percent of tasks can be characterized as rule-driven, and thereby, they can be operated automatically through the use of automation RPA tools. Since the BFSI industry is strictly regulated and has high data quality and security standards, RPA software can be applied to multiple banking automation system projects:

  • With robotic process automation in the financial industry, mainly burdensome account opening activity becomes more precise and straightforward. It removes data conversion mistakes that used to exist between the central banking system and new requests for bank accounts, therefore improving the overall quality of information within the organization. Consequently, RPA in fintech can eliminate unreliable and prolonged data input sequence, significantly reduce the turnaround time, all while ensuring high accuracy and lowered costs.
  • Client onboarding is often a drawn-out and confusing procedure since there is a number of documents that need manual confirmation. RPA and AI in banking can streamline this process by picking up the necessary information from the KYC papers and later compare it against the data in the form through the application of optical character recognition technology (OCR). Such use of RPA in financial services can not only mitigate human errors but also help your employees save much time and effort.
  • KYC and AML procedures are very data-intensive, which makes them ideal for the application of intelligent automation framework. No matter if we’re talking about computerization of manual tasks or detection of abnormal banking activities, RPA in financial services has proved its efficiency in terms of cost- and time-saving in comparison with conventional solutions.
  • Creating suspicious activity reports (SARs) for fraudulent deals is a standard at financial services companies and banks globally. Routinely, compliance experts should read the papers and write in all the essential details in the SAR blank manually. Automated report creation is one of the opportunities for robotic process automation software since it can scan long compliance statements before retrieving the readings and filling out an SAR. Moreover, it can be educated by the officers to improve accuracy and finally reduce operational expenses and overall cycle time.
  • Mortgage lending is a crucial focus area for any bank, but this service is rather cumbersome and process-based. With RPA, banking processes related to lending can be performed automatically, including credit arrangement, quality audit, and document processing. Subsequently, loan requests are handled faster, resulting in greater customer satisfaction.
  • Loan underwriting has always been regarded as an exhaustively slow procedure, even though most banks have already computerized some duties. Thuswise, it is one of the possible RPA use cases in banking since virtual assistants can continue streamlining and bring the turnaround time down to nearly 15 minutes.

Pioneering the Industry: Examples of RPA in Banking

The use of robotic process automation and artificial intelligence in financial services to accelerate processes, reduce costs, and enhance user experiences is not new. Furthermore, considering the diversity of RPA in banking use cases mentioned above, the technology will only keep the momentum going. While discussing organizations that are ahead of the curve in automation, we cannot but mention:

  • Bank of America and its Pega Robotic Automation solution — a non-invasive software with AI, OCR, and machine learning (ML) technologies that have made it possible for the bank to connect its obsolete systems and patch data integration holes without updating the core technology. Robots are already put in place to execute global payments, service clients for card and mortgage disputes, manipulate data, and process exceptions. But more importantly, the ‘Bank of America-RPA’ program is promised to live on and grow.
  • Deutsche Bank-RPA’ alliance, combined with cognitive capabilities, has also brought positive changes and helped the institution to enhance the quality of its services. The bank applies automation in such areas as lending operations, tax, trade financing, cash payments, employee training, and others.
  • Nordea Bank is another advocate of AI and automation in the financial services sector. They utilize the AI-driven solution to analyze and sort customer inquiries, as well as automatically forward them to the appropriate areas for processing. The software can process hundreds of queries per second, resulting in better customer service and faster response time.
  • At OCBC, RPA-powered software programs (Bob and Zac) are integrated into lending and finance areas, and currently, they take on time-consuming and recurring duties that don’t require decision-making. Zac helps to generate daily sales efficiency reports. Bob works with housing loans: verifies consumer’s eligibility for home loan repricing, suggests options, and even makes a recommendation draft. The Singaporean bank states that now it takes one minute to reprice a housing loan, instead of the usual 45.
  • For insurers, it’s critical to identify individuals who are liable to cause a heavy-damage road traffic accident, and when such an incident could happen. AXA insurance company combined a neural network model and machine learning (ML) techniques to create a solution that foretells such events with 78 percent accuracy. Moreover, it’s UK division has also employed 13 robots to deal with routine admin tasks, which allowed them to save 18,000 staff-hours or around £140,000 in six months.

This is only a partial list. Among other insurers, fintechs, and banks using RPA, there are Danske Bank, Union Bank, JPMorgan Chase, Axis Bank, Sumitomo Mitsui, DBS Bank, BNY Mellon, Allstate, and many others. If you’d like to know how Dashdevs can help you with banking app development or RPA integration, please reach out to us, and we’ll get back to you shortly.

Automation in the banking industry is undoubtedly one of the most rewarding technologies since it not only helps companies boost their digital transformation and bring down operating costs but also enhance ROI from their previous IT investments.

Just like in the case of any other innovation used for fintech product development, success with automation is highly dependent on the underlying software that you choose. New robotic process automation companies are regularly coming into the market, while reputed providers are adding functionality and making improvements. So far, among the most efficient, powerful, and leading RPA tools are:

#1 UiPath

UiPath tops the list of robotic process automation tools and holds a sustainable position in the marketplace. It comes with all vital capabilities, offers Citrix support, and suits for any business size. It can be integrated with your existing infrastructure without making modifications to the legacy software. Being compatible with various frameworks (.Net, SAP, Java, and others), offering automated data input, and supporting both desktop and web apps, this tool often becomes clients’ number-one pick for implementing automation in banking and financial services.

Several apps together create a comprehensive system, and they are Platform, Explorer, Orchestrator, Studio, Connect Enterprise, Robots, and Insight. Moreover, the company offers training and tutorials for non-technical and technical people, thus making IT process automation accessible to everyone.

UiPath works with such BFSI companies as Heritage Bank, PZU, Federal Bank, eurobank, Lombard International, Maitland Group, American Fidelity, Swiss Re, and many others.

#2 Automation Anywhere

Automation Anywhere fairly takes second place in the RPA tools list. It is a functional and highly-reputed competitor that offers cloud and on-premise services, advanced security measures, real-time analytics, and platform independence. All these things make it a good option for large and medium-sized financial institutions.

It is one of the best RPA tools since it mixes intelligent components with conventional automation to enable natural language processing and work with unstructured data. Its product line consists of Bot Store, IQ Bot, Bot Insight, and Automation Anywhere Enterprise platform. Additionally, the company has an online training center to facilitate your RPA onboarding.

Automation Anywhere platform is used by PGGM, St. James’s Place, TreasuryONE, Bancolombia, Genworth Financial, Dai-ich Life Insurance, and other BFSI firms.

#3 Blue Prism

Blue Prism ranks third in the list of top RPA tools, though it is often called the best option for large and medium-sized financial organizations. It utilizes machine learning (ML) and artificial intelligence (AI) to continually educate bots and make them more adept at anticipating end-user expectations, emulating humans, and executing instructions.

This platform-agnostic RPA system includes Control Room, Object Studio, and Digital Workforce components. It offers sophisticated safety features, prompt integration that takes from four to six weeks, multi-faceted analytics, and load balancing capabilities.

Among Blue Prism clients in the financial industry, you can find Barclays Bank, ATB Financial, Ageas UK, The Co-operative Bank, and others.

RPA and AI in Financial Institutions: A Step-by-Step Guide on How to Get Started

To embrace all the advantages of automation in the banking sector, businesses need to draw up a preliminary roadmap sequencing their robotics journey.

  1. Establish a clear vision and gain leadership backing to acknowledge that intelligent automation is a mission-critical goal with objective performance indicators. What are the business issues that you’re striving to solve? What are you trying to accomplish with automation ― growth, lower operating costs, or better data quality?
  2. Create a use case that will evaluate the impact on the processing time, the scope of transactions, and the amount of time saved by the employees. Initially, focus your efforts on straightforward processes, like payments reconciliation, analysis and classification of consumer inquiries, or account servicing. It’s important to set realistic expectations in terms of your cost savings and ROI (return on investment) rates to prevent frustration.
  3. Analyze the results and scale. Automation in fintech or banking is not just an IT project since it transforms the entire business. Start with communicating the strategy, benefits of RPA in banking, and how the technology can improve employees’ roles. Expand to more complex business cases. At this stage, it is critical to have a reliable implementation partner or form an automation center of excellence (CoE) that will guide, regulate, maintain, grow, and optimize all the processes.

Conclusion

Within the past few years, financial organizations in the United States, Europe, and Asia have been seeking to prune away operating expenses and add value by streamlining, centralizing, and outsourcing a myriad of processes. Currently, RPA is the next stepping stone to cost-saving, better accuracy, and control.

Combined with the next suite of disruptive technologies, like artificial intelligence (AI) and machine learning (ML), it can provide companies with a remarkable opportunity to reimagine their operating model, boost efficiency, and unleash value associated with customer excellence and employee productivity. The future of RPA in banking is contingent on business leaders and their ability to substantiate intelligent automation, orchestrate it as a transformation program, and address challenges on-the-fly, from ecosystem to strategy to talent.

We believe that fully-fledged adoption of robotics is inevitable, and it will only gain traction as more auspicious cases are published.

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