Robotic process automation (RPA), or robotics as it is commonly referred to, is the automation of business processes using software to replicate human interaction with various systems and applications. Generally, RPA allows financial institutions to achieve cost and time savings by automating high-frequency and repetitive tasks, while eliminating the possibility of human error while improving accuracy.
In a recent Global Banking Outlook study conducted by EY, more than half of the banks surveyed indicated plans to invest in new technologies and integrate robotics into their current interfaces to reduce costs of existing, high-frequency manual operations by 40% or more over the next 12 months.
Indeed, the financial services industry have been quick to adopt RPA and artificial intelligence (AI). In wealth and investment management functions, there has been a steady adoption of RPA in the form of self-serve robo-advisory; partly attributable to the rising digital-savviness of the high net worth (HNW) population globally.
Robo-advisory incorporates big data and AI to reduce the need for face-to-face interaction with customers. Not only do robo-advisors support the provision of automated advice and more detailed portfolio reviews, while also enhancing the overall client experience and service delivery, they also significantly reduce the time that a typical wealth advisor would spend on administrative and back-office activities.
In back-end functions, robotics is making headway in risk and compliance areas, such as know-your-client (KYC) and anti-money laundering (AML) prevention processes. An EY survey found that 87% of the participating financial institutions thought that their KYC operations were not cost-efficient, and the majority were considering technology investments to address this concern.
Additionally, time and costs spent on onshore or offshore services – involving manual data entry, fact-checking, status monitoring or liquidity ratio aggregation – can be channeled to more strategic roles requiring human intervention. These include planning, defining frameworks, governance, evaluation, implementation, training and operational management of the robotics process and robots.
RPA also gives financial institutions the option for data to remain in-country, to address concerns around data governance and control. This, in turn, creates opportunities for financial institutions to potentially bring some of these previously offshore activities back onshore, providing a higher degree of compliance and alignment with their internal controls framework. In the long term, RPA can better enable compliance and operations to be more agile and responsive to regulatory and market changes.
RPA in corporate treasury
The corporate treasury and finance function is known to involve a lot of routine, repetitive and manual tasks where robotics can make a positive difference through automation; particularly in rules-based work such as processing of transactions and structured data. The areas of application include cash management reconciliation and simple cash accounting.
We are also seeing more scenarios where robotics is combined with machine learning, which can help to automate certain complex decisions that cannot be performed using straightforward rules-based automation. Such decisions revolve around self-learning detection of cash forecasting, payment fraud detection and mitigation of cash-flow related risks.
In addition, cognitive RPA is used to extract and combine data from various sources, including external data providers, social networks and shared drives. Cognitive RPA can help institutions to provide an initial analysis and conclusion based on pre-determined boundaries.
Notwithstanding the potential capabilities of robotics to substitute or augment the role of humans, human intervention remains important given the nature and complexities of the corporate treasury function where robotics alone cannot rise to the task. This includes managing sanctions, responding to changing regulations and making strategic decisions in areas that are less predictive and require more than just historical data to make the right deductions.
That said, it is imperative that corporate finance and treasury talent upskill themselves to understand how RPA works in order to stay relevant and agile in the automated environment.
Implementing RPA successfully
Even as the benefits of RPA is clear, implementation is key to fully reaping its potential.
One of the common implementation pitfalls is in seeing an RPA programme as an IT-led initiative, rather than a business-led one. Just as critical is the failure to involve other functions such as cyber, security, risk, human resource and other enterprise units.
Another issue that can lead to failed implementation is treating RPA as a series of automations, rather than an end-to-end change programme, and underestimating what happens after processes have been automated. Considering issues such as who manages the robot workforce and subsequent processes after the go-live is just as – if not more – crucial than getting an RPA programme mobilised, targeted and delivered at pace.
Ironically, over-automating can also potentially backfire. Sometimes, organisations totally eliminate the human input in a process, which results in a significant automation effort and cost but little additional benefit. Yet, we equally often see no effort in changing existing processes to allow RPA to work across as much of a process as possible, therefore reducing the possible savings.
Lastly, companies may risk neglecting their IT infrastructure when implementing RPA. Most RPA tools work best on a virtual desktop environment, with appropriate scaling up and a business continuity setup. Yet, RPA processes can be delivered so quickly that IT has not had the time to create a production infrastructure for successful automation.
In many RPA projects, more than one of the above issues are present or linked, creating a significant multiplier effect.
No doubt, RPA shows great promise in transforming the financial services industry and corporate treasury functions. Yet, its full benefits can only be realised if the various robotics processes co-exist and communicate with one another effectively across different platforms, and that any RPA programme is considered holistically from the business, IT, people and change management perspectives.
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