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RPA in Financial Services

Previously, we discussed where to start with robotic process automation (RPA) in financial services and defined the key processes where RPA could provide value for financial services firms. Now, we're going to explore more of the technology's potential and use cases with an emphasis on how to design, plan and execute a winning AI and RPA strategy.


Looking back, there was no way to predict the incredible pace of technological change that has gotten us to where we are today. As a case in point, over the past decade, we have seen quality improvements in speech recognition that have led to the creation of sophisticated digital assistants like Siri and Alexa. Now, it’s plausible to think that the keyboard could soon become obsolete, especially with a predicted 50% of all searches being voice searches by 2020. Much like natural language processing is transforming search, it is no longer a question that emerging technology like RPA and cognitive computing will transform the workforce of the future across the financial sector. For financial institutions, the key is to embrace the next wave of robotics technology to drive business outcomes and use these tools to automate a wide range of activities. Recent reports have highlighted that RPA will result in a more productive relationship between people and machines through deeper analytics and recommendation engines, which will in turn strengthen client services and product features. With robotics, the automation of recurring functions is possible in the front office and back office, freeing people to focus and work on more complex, high-value tasks. For example, applying AI to front-office client interactions like customer onboarding proceedings, such as documentation requirements verification, compliance, legal and credit checks, and the operational activation of accounts in trading and settlement systems, will significantly increase processing speed. Here’s a sample list of areas where RPA has enormous potential in banking and capital markets: BankingCapital Markets

  • New account entry across systems – moving data and doing multiple entries

  • Fraud detection

  • Account reconciliation – duplicating and moving data

  • Entitlement engines

  • Report generation across systems and generated

  • Reconciliation -- user-defined rules to generate alerts on chargeback and retrieval

  • eForm extraction

  • Automated scheduling of reconciliation activity

  • Accrual support -- making and updating entries

  • Multi-level reconciliation across international & domestic networks

  • Mortgage approval -- refinancing processes for initial entry and updating records

  • Receive/track dividend, interest and amortized principal payment information

  • Notification of delinquent loans – emails and letters to clients

  • KYC/AML authentication: collection and analysis of basic identity information

  • Credit card order processing

  • Audit support and validation

  • Fraud detection – tracking of activity

  • Account cleansing – inactive account purging


One of the great advantages of RPA is the speed and relatively low cost of implementation, but institutions must first select the process candidates for automation and ensure that the operating model for RPA can be supported. Having effective governance in place to establish standards and drive organizational buy-in from those who will be affected by automation is critical to maximize value. This emphasizes the need to focus on developing key metrics, execution strategies and change management plans. Financial institutions that can adjust their organization and culture to adopt intelligent automation as collaborators, rather than “people replacements," could achieve significant success. Organizations need to define clear roles for design and planning at the onset. In order to incorporate RPA as a long-term strategy firm-wide, its potential must be expressed broadly. Alignment across the organization is vital, as the benefits are not just confined to specific processes or departments within a firm. RPA deployment also requires effective change management and stakeholder engagement – these aspects dictate the need for executive oversight, active project management office (PMO) tracking, and the rollout of continuous improvements along the way. Proper planning should include a clear set of metrics and key performance indicators (KPIs) that can later be used to determine the efficiency of a new RPA task in terms of accuracy, improved productivity and enhanced end-user experience. Starting with areas that are labor intensive, identify and assess which of the existing processes and business characteristics are potential candidates for RPA. Consider the following:

  • How can RPA, including the introduction of straight-through processing, enhance the customer experience?

  • How can RPA help identify certain trends to inform the future direction of the business?

  • How can an RPA solution or cognitive automation help with risk monitoring and regulatory compliance when it comes to accuracy and streamlining headcount?

  • How can RPA help with high-volume processes that are repetitive and require a high-degree of accuracy?

The immediate step following the identification of areas requiring automation is to validate that the targeted processes can be automated and produce tangible benefits and value. Effective RPA execution should employ agile delivery methods to develop the plan and deploy an automated solution.


Always remember that people are key to the success of any organization, and that deploying the right team and resources is crucial to the planning and success of an RPA project. A talent acquisition strategy to meet the new technology demands will be necessary, and tough decisions will need to be made about whether to hire new or retrain existing staff. Hiring a third-party team of RPA implementation experts to determine whether existing products and tasks can be targeted is essential. Such a team should be able to help assess the business value and practicality of automating the selected processes. Key stakeholders should be dedicated for focused involvement and active participation in the functional design aspect, as well as the testing and go-live support. Since RPA will have a considerable effect on the roles and responsibilities in any organization, the changes that will transpire must be anticipated. With the proper team of experts in place, the negative impact of any changes can be mitigated over time to ensure a winning AI strategy.

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