RPA In Action In The Financial Sector
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Robotic Process Automation (RPA) is the automation of everything in a process lifecycle, which encompasses bringing in inputs, undertaking processing and generating required outputs. RPA is today no more the new kid on the block. As per Gartner, global RPA software revenue is projected to reach $1.89 billion in 2021.
Typically, to fulfill a process, there are two activities: 1) Rule-based processing requires a set of rules to be followed to make a decision. To cite an example: if a credit card dispute is less than or equal to $3, you approve it without any further check, as the cost of analysis is more than $3. 2) Cognitive processing requires human judgement using many inputs and complex analysis techniques. To cite an example: marking a credit card dispute as fraud based on location, IP Address, previous transactions, and other relevant parameters.
100% of rule-based processing can be automated today using RPA. Cognitive processing is facilitated using Machine Learning (ML), and today RPA tools can integrate with ML pipelines to enable cognitive processing. Once a process is fully or partially automated, one or more bots can run the process, depending on the transaction volume at hand. Bots are your new team members and perform tasks based on how they are automated.
RPA can be undertaken at individual desktops / laptops or using a server-based software with more robust controls. When done at a desktop / laptop it is generally called Robotic Desktop Automation (RDA). RDA is a great place to start automation, as it is extremely cost effective and typically has very good automation features.
RPA delivers two key benefits: 1) Financial benefit by freeing up human resources who can instead be leveraged for higher value roles, reducing penalties by meeting SLAs, and generating revenue by enabling financial transactions 2) Non-financial benefit with bots includes ensuring 100% quality, provided you automate them right. Trends show that bots are generally 200% faster and can scale up within seconds, especially when there is a spike in transactions e.g. festival shopping.
Back-office operations are generally an area where the first successes of RPA came in, due to the repetitive and manual nature of work. Processes such as Account Payables, Account Receivables, Customer / Merchant Onboarding (including KYC processing), Regulatory Filings and Dispute Management are examples of areas where RPA is leveraged.
“With digital acceleration at its peak, RPA has been at the heart of transformative strategies across industries”
There are other areas in the financial industry where RPA is used.
• Sometimes to fulfill an end-to-end process, multiple applications are used, which requires information to be manually fed from one application to the other. These are perfect use cases for RPA. Product companies generally also sell product bundles that are not connected with eachother. Pre-written RPA bots that connect these products together now come along these bundles to make life easier for customers.
• Legacy applications can be a tech-debt for any organization. Rewriting these legacy applications to enable modern technologies such as APIs, chat bots, and mobile apps requires budget and time. RPA can play an enabling role here, where modern frontends connect with RPA bots that in-turn connect with legacy applications to get the job done. Of course, the response time is comparatively higher, but it does get the job done. These are generally called RPA micro-services.
• As the capability of RPA tools is growing, another trend we are seeing is using RPA for test automation (post scripting) and test data generation. Manual test data generation can take 10-20% of testing effort, so RPA offer significant potential savings of time and effort.
• Agent Automation is another growing trend. Typically, agents in call centers need to look at a myriad of consumer data from various applications to understand the context and decide how to support a customer on a call. RPA is being used to quickly create a screen that consolidates all information required, including recommendations on how to help the customer (using cognitive recommendation engines).
• Quick integration of applications post mergers and acquisitions are being enabled using RPA.
The Intelligent Automation industry and its adoption are growing, providing a plethora of opportunities to automate. Some trends to look out for are:
• Intelligent automation will become increasingly effective when RPA is combined with multiple tools such as business process management systems (BPMS), low code application platforms (LCAPs), omni-channel conversational platforms (IVR, chat bots, SMS gateways), and artificial intelligence tools (machine learning, NLP, NLG), among others. This is also called hyper automation.
• Self-healing bots: RPA bots fail when graphical user interfaces (GUI) elements change due to upgrades, increasing the need for bots that can self-heal using artificial intelligence techniques
• Self-generated bots: RPA tools are being created that can auto-create bots by watching the work being done on a desktop / application(s). As this technology matures creating bots will become easier.
• Self-optimizing bots: Depending on how they are automated, RPA bots can use up a lot of memory / CPU performance / network bandwidth. Monitoring these aspects and recommending optimization mechanisms will go a long way as it will make bots automatically efficient.
With digital acceleration at its peak, RPA has been at the heart of transformative strategies across industries. Where there is a process pain point or bottle neck, RPA can establish clear workflows and enable continuous process improvement. What makes RPA stand out is its capability to automate end to end process lifecycles by integrating new front-end technologies with back office environments. Adding bots to manage repetitive and predicative tasks further speeds decision making, reduces process time, and influences positive outcomes and experiences.