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    The History of RPA, Its Data & Analytics

    Robotics Process Automation (RPA) is defined by the Institute for Robotic Process Automation as ‘the application of technology allowing employees in a company to configure computer software or a ‘robot’ to capture and interpret existing applications for processing a transaction, manipulating data, triggering responses and communicating with other digital systems.’

    The Institute continues by saying companies who use large-scale labor forces, such as banking and financial institutions, for exorbitant amounts of knowledge process work will benefit from RPA as it will boost their capabilities and save money and time with its software.

    The premise behind robotics automation is that it gives any work process that is definable, repeatable, and rules-based the ability to map out a business process and assign a software robot to manage the execution of that process, just as a human would. RPA technology is not a part of a company’s information technology infrastructure, but rather sits on top of it.

    According to the Robotics Process Automation Handbook this allows a company to implement the technology quickly and efficiently without altering existing infrastructure and systems. Just as robots have and continue to revolutionize the manufacturing industry by creating higher production rates and improved quality, RPA is overhauling the way we think about administrative business processes, support processes, workflow processes, remote infrastructure and back-office work.

    In the early 1990s, AI was taking over many industries especially manufacturing,” said Dave Sufrinko, Director of DISYS’ Testing Practice. “But its implementation within the financial sector repeatedly fell short because of the technology costs and implementation problems.”

    One of the debates surrounding RPA revolves around the question of whether this technology is truly revolutionary or simply the product of the evolution of other similar technologies. Many technologies, including artificial intelligence, expert systems, and other methods of process automation have served as predecessors to RPA.

    That being said, RPA takes artificial intelligence and expert systems to an elevated level. Among leaders in the automation industry, robotic process automation is perceived as offering unique capabilities and advantages over previous technologies such as artificial intelligence which largely fell flat in the financial industry during the 1990s.

    Experts agree the financial sector is at a crossroads: It is now required to take a look at RPA technology and what it wants it to accomplish in the short and long term. In recent years, several things have changed as the cost of computing has declined and the power of it has improved tremendously.

    AI applications, which tend to produce astronomical amounts of data that requires storage, were not scalable to the past years – but currently the use of AI is more practical and its data more manageable due to big data and cloud solutions.

    “The fact AI produces so much data that could not be mined properly, previously made implementing it a daunting task,” said Ray Goodwin, Testing and Automation Expert at DISYS. “But with today’s AI solutions, data can be mined in such a way that it is useful and applicable to the daily processes within financial institutions.”

    The ability to transform data into language turns what machines only previously understood into information that banking leadership can easily understand. The financial sector now has access to years of data related to sales, products, divisions and branch activities as well as customer opinions which improves leadership’s ability to make informed decisions.

    RPA is a game-changer since it allows organizations to continually monitor business or IT processes and the behavior of personnel and software applications as part of those processes.

    That monitoring of patterns and events is performed by virtual engineers (robots) that can actually learn by observing process-based activities undertaken by human engineers. The subsequent knowledge gathered through machine observation can then be incorporated into future computer inferences made during operations.

    Not only can RPA be used to identify an anomaly, thereby turning human workers into problem solvers, it can also initiate a set of action items to respond to the occurrence. In the wealth advisory space, for instance, advisors can access on demand, up-to-date tailored performance summaries for their clients, giving them knowledge they need to make better investment recommendations.

    This understanding of the data AI technology mines through big data analytics and its accurate and valuable interpretation is what experts say is the key to AI’s runaway success within the financial sector.

    “Using statistical analysis or machine learning, data can now be used to discover relationships between separate data points such as customer engagement, churn, transactions, sales and success likelihood,” said Paul Francis, Director of DISYS’ Business Intelligence Practice.

    Artificial Intelligence can then transform the discoveries of those correlations into actual explanations of identified relationships. Think of it this way: When financial institutions release AI software into systems to automate business processes, that robot can in essence, bring back identifiable information on how a process performed.

    When an entire ‘army’ of RPAs are deployed, it adds up to a significant amount of data – known as big data. “When information, brought back in the form of big data, is processed and analyzed, it puts the finance industry in a position to discover bottlenecks and optimize processes,” Francis said.

    Enter, analytics. Mounds of data used to mean nothing to the naked eye and systems could not comprehend or break the data down. But now, the use of big data analytics through proper RPA installations can pinpoint actionable tasks for improvement and optimization.

    Turning large amounts of raw data into understandable patterns for institution decision-making is where the true beauty of RPA is discovered.

    NOTE: Find out more about what RPA has to offer the Banking & Financial Services industry in the DISYS Whitepaper, “Robotics Process Automation: Optimizing Today’s Banking Workforce.”

    DISYS’ Financial Services offerings combines a proven automation and optimization approach with repeatable assets to offer increased business value to our partners. Our financial services clients have benefitted from our catalog of intellectual property and our experience gathered on previous engagements.

    This means we can not only apply lessons learned in other engagements to your project risk but we can more proactively recommend solutions that are the best mix of tools, methodology and team structure to accelerate productivity in your environment.

    Financial institutions continue to partner with DISYS to solve critical infrastructure and compliance problems while reducing operating expenses – all amidst a rapidly-changing business landscape.