How Robotic Process Automation Is Transforming Financial Services

If you haven’t automated your business processes yet, this post will provide the basics on how robotic process automation (RPA) works, how much this tech costs, and what tools to use for its implementation. Improve the efficiency of your ongoing processes, cut expenditures, minimize daily routines, and make your procedures auditable with RPA in just a few weeks.
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By Oleg Komissarov
Principal Consultant, Finance Practice
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By Alex Bronstein
Principal Consultant
How Robotic Process Automation Is Transforming Financial Services

Robotics seem to be everywhere these days, and the financial services sector is no exception. Robotic process automation (RPA) is now the tool of choice for 50% of financial market players seeking to automate manual processes. With profit margins thinning, increasing regulation, and interest rates stagnant, using RPAs to enhance competitive edge is one way to keep up with the slew of FinTech start-ups crowding the market. 

But what exactly is RPA? Here we’ll take a look at what it is, how it works, how it can be used, and the various RPA options currently available.

What Is RPA? What Are Its Benefits?

RPA is a software-based tool that relies on bots to emulate human work, specifically in labor-intensive areas of a business’s process. Essentially, the ‘robots’ perform the same work that humans would, using the same interface and following similar steps. It differs from traditional automation in that, instead of relying on APIs to integrate several systems into one platform and perform set routines, RPA notes a user’s actions in a GUI and then repeats those actions in the same GUI. This allows human-like automation of repetitive tasks. 

Implementation of RPA into the routine processes offers significant benefits to asset managers, as it can be applied to a number of the specific business areas, including:

  • Trade processing and email generation based on exception criteria
  • Trade support, enrichment, validation and reconciliation
  • Reconciliation, especially between disconnected systems (WSO-like issues)
  • Information and message delivery between operational teams
  • Data entry and extraction automation and reconciliations – for example with credit ratings, loan contracts, financial statements, etc.
  • Fund admin areas

Carefully picking a business area to integrate RPA into is essential for it to produce quick and noticeable benefits. Let’s have a closer look at the main benefits:

Improved efficiency. RPA can significantly reduce human error. It also affords a 20% reduction in the number of helpdesk queries, as well as enhanced customer satisfaction. Another obvious advantage is the higher speed of processing, as it works on average about four times faster than a person. In addition, it can execute multiple processes simultaneously and formulate feedback faster. Last but not least: robots are available for work 24/7, and are not prone to absenteeism.

Savings in cost and effort. Typically, robot license costs are much lower than the average cost of a human workforce. Moreover, RPA can generate up to a 67% reduction in the number of full-time equivalent employees needed for the process. In terms of needed effort, RPA automation can save about 20-25%, as it can be developed in a matter of weeks.

Seamless usability. RPA is a lightweight enterprise automation technology that can sit alongside any existing tech. Business process management (BPM) and RPA platforms are built for change and agility, so they are highly flexible. In addition, a robust audit trail can be created for all activities robots perform, which makes it easy to monitor the process and take corrective action if required.

The State of RPA in Financial Services

With such a possibility for success, it’s no surprise that the adoption of RPA in the financial sector is soaring. Here are some quick facts:

RPA can lead to 25-60% in cost savings. 

Global RPA market revenues exceeded $1.5bn in 2019.

RPA software and services are expected to grow to over $3bn by 2023.


Lower margins due to increasing regulations and stricter capital requirements are forcing companies to do more with less. A rapidly evolving marketplace and competition from myriad fast and nimble FinTech start-ups are pushing companies to reinvent how they deliver their products and service to their customers, forcing them to upgrade and automate their operational processes. 

These automation efforts have been supported by a wide range of technologies. Some of these technologies, such as Workload Automation and BPM, have been used to automate labor-intensive processes for decades. Other technologies, such as RPA, are relative newcomers but are spreading rapidly across firms in all industries.

Let’s explore how exactly financial companies can adapt RPA technology in their business processes.

RPA Tools on the Market

How can businesses adapt software robots to automate repetitive work and enhance their business processes? Is it possible to create “bots” for cross-application maneuvers that mimic human activity, including application logging in and out, data copy-pasting, opening attachments and emails, and filling out forms? RPA is not screen-scraping or macros software technology, but something far more sophisticated.

Let’s consider an example. Macros can repeat only linear commands. In turn, bots intuitively respond to stimuli and are able to increase their intelligence over time. Moreover, RPA can interact with multiple applications at once.

RPA does not require large upfront investment in technology or expensive, specialized technical resources. The costs of RPA scales with its use, as the licensing is based mainly on the number of “robots” used. Each robot is responsible for a single automated process, and therefore the cost of investment can easily be linked to the savings from automation. RPA implementation does not require expensive developers either. Many RPA tools are designed to be “business-friendly”, and don’t require support from IT. In many cases, technically-minded business users or business analysts can quickly get up to speed and become productive with these tools.

Let’s look at some of the options:

Ready-Made RPA Solutions

There are a number of ready-made RPA solutions. These do not need RPA developers to implement; most are designed to be simple enough that any user who’s familiar with how the process that’s being automated works can apply them. No additional coding is required. The benefits of a ready-made RPA solution include:

  • Typically lower cost than custom solutions
  • Easy ability to measure savings from implementation, as each robot is linked to one process alone
  • Quick implementation
  • Ability to access and modify code to individual needs

However, there are some drawbacks to using off-the-shelf RPA solutions. For example, their limited functionality may mean that some businesses need multiple RPAs to address specific processes, which spirals costs. And they require in-house developers in the case that ready-made options need some modification.


Free Solutions

There are open-source RPA options available that can offer some level of free automation for any business. Most cannot be adapted easily to an individual company’s needs (at least, not without using developers) and are instead intended for non-enterprise organizations. Many of these free options are pared-down versions of paid RPA solutions, intended to give businesses the chance at a “trial run.”

Here’s a brief overview of the top three open-source options:

  • Automation Anywhere combines standard RPA with more intelligent elements, which means it can process both structured and unstructured data while performing complex tasks across multiple computers.
  • UiPath offers desktop, GUI, web, mainframe and SAP automation, screen scraping, and a macro recorder. Although available as a free tool, UiPath’s paid option is far more popular thanks to its increased functionality.
  • Blue Prism focuses on rule-based back-office operational automation and is extremely user-friendly. As with the above, Blue Prism’s paid solution has more advanced options and is typically chosen over the more limited free tool.

Paid Solutions

It should come as no surprise that greater functionality and possibility comes with paid RPA software. Some of the RPA-as-a-service and RPA enterprise vendors have free trials or test periods available but then come at a cost. These include:

  • BluePrism prices range from $15,000-$18,000 annually per bot. The benefit of using BluePrism’s paid service rather than its free tool is that the paid option comes with a robust analytics suite and an improved oversight tool to access real-time feedback.
  • KOFAX scales from $3,500 per year and the paid version offers intelligent robots that can monitor and optimize processes.
  • UiPath’s studio license is $2,000 annually, and the paid version betters its open-source counterpart by coming with auto-login features and enhanced security measures, as well as offering the ability to automate any task regardless of complexity.
  • Pega Robotic Automation is a BPM tool that costs as low as $200 per month, and it offers predictive analytic decision-making as well as the ability to consolidate across architectures. However, it is cloud-based only. 

Custom RPA Solutions

Technological developments described above have reduced but not completely eliminated the role of custom software solutions for business process automation. Many companies have unique business challenges that cannot be met by in their entirety by any single, or even combination of, off-the-shelf solutions and some custom development may be required to meet the needs of the business. Some companies may find that the total operating cost of multiple vendor products exceeds the cost of building a custom solution. But even custom software solutions can be developed much cheaper than ever before.

Cloud computing has eliminated the need for expensive on-premises infrastructure and its ongoing maintenance. The elastic and scalable nature of many cloud-based services optimizes the use of computational and data resources. Many of the “ities” that make enterprise computing so expensive – scalability, extensibility, maintainability, reliability – come at a much lower cost in the cloud. Built-in support for continuous deployment and integration eliminates much of the software release overhead. 

General-purpose REST APIs make it much easier to integrate third-party products and those legacy systems offering such APIs into custom software solutions. Micro-services are becoming the preferred approach for architecting applications, which will result in practically all business logic and services within an enterprise becoming available to other systems.

RPA Use Cases

Companies across sectors using RPAs include WalMart, Walgreens, AT&T, Vanguard, American Express, and many others. However, it is worth noting that while 43% of financial services jobs are automatable, research shows that only 3% of organizations have managed to scale RPA beyond 50 robots.

Here’s an example of smart RPA use within financial services: a leading international financial services company needed help efficiently and accurately extracting data from financial documents for the purpose of generating ratings. DataArt was able to help by designing and implementing an automation application that recognized, digitalized, extracted, validated, and processed data from PDF statements, using PDFBox, Java, OSS, and Tabula. This allowed the client to automate what was a time-intensive manual process, quickly and cost-effectively.

What Is Behind RPA?

Intelligent Process Automation is an approach that combines RPA, Business Process Mining and Orchestration, Machine Learning and Natural Language Processing in one solution to achieve better business outcomes compared to using an RPA Platform only.

Business Process Mining helps to analyze and optimize processes based on event logs generated by systems.

Business Process Management is an essential tool for orchestrating, controlling, monitoring and continually improving complex processes.

Machine Learning means systems can learn via handling variations that are not anticipated upfront. These systems are trained on the go by assimilating lessons from the data and decisions backed by algorithms. Examples include processing image-based POs, identifying molecules from an image, and triggering a relevant process.

Natural Language Processing uses statistical methods and learning algorithms to analyze text and unstructured information to understand the meaning, sentiment, and intent. A sample use case is in customer service, where a customer raises a support ticket in the form of free text, which is then analyzed to determine the next step, and a process is subsequently triggered.



RPA adoption may be on the rise, but that doesn’t mean the RPA landscape isn’t changing as it grows. There are currently several main areas in which RPA solutions are expected to grow, and these rely on integration with AI. The ability to read unstructured documents is a key hurdle in automation, so self-learning and cognitive RPAs that utilize NLP and ML will offer enhanced functionality, as well as the ability to adapt to changing environments. As such they will become more useful and cheaper to maintain. Automation that can perform cognitive functions may sound like the future, but it’s closer than you think.

Discover how your company can use RPA and AI to grow. Contact DataArt today for a chat!

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