By Volodymyr Marchuk,  Cloud and Solutions Architect and Yevhen Berko, Head of Big Data Office at ELEKS 

According to a recent Deloitte report, the pandemic has been “reshaping the global banking industry on a number of dimensions, ushering in a new competitive landscape…prompting a new wave of innovation, recasting the role of branches, and of course, accelerating digitization in almost every sphere of banking and capital markets.”

Digital process automation or “Hyperautomation”, a  modern term coined by Gartner will be a key part of this digitization. Gartner defines hyperautomation as “the idea that anything that can be automated in an organisation should be”. It’s driven by the need to streamline what is, for the majority of businesses, a disjointed hotchpotch of new and old systems and processes which aren’t efficient, agile or synchronised.

As its name suggests, hyperautomation describes a deep level of digital autonomy; the process of automating end-to-end business operations to unburden human workers, optimise efficiencies and reduce costs.

Main technological pillars

Hyperautomation isn’t a single entity. It comprises multiple different technologies, connected via the Internet of Things, which work in unison to enable end-to-end automation. Here are the key elements:

• Robotic process automation – RPA describes the process of harnessing technologies such as bots to takeover manual tasks that would normally be performed by humans. For example, the processing of payment lists through the accounting system or website.
• AI is the next step on from RPA, whereby computers are able to simulate human intelligence and this unlocks a far deeper level of automation than RPA alone. For example, providing a real-time analysis looking at the potential for fraud and money laundering risks
• Intelligent business process management approach combines business process management (BPM) software with the capabilities of artificial intelligence (AI). This is really the linchpin of hyperautomation. IBMP describes software which has the ability to manage the switch to a hyperautomated environment. It is a strategic tool which handles the processes, strategy and workflow involved in enabling end-to-end automation – and to monitor the results so that issues can be resolved. For example, automated investigation and resolution of any discrepancies between the finance teams and other departments via e-mail messaging.

How can banking and finance benefit from hyperautomation

One area that is likely to be automated, according to Deloitte, is the entire regulatory reporting process. Many banks that we are talking to are already using robotic process automation (RPA) and cognitive intelligence technologies. This means that manual tasks can be automated 24/7 with limited human supervision. We are seeing improvements in data quality and human workers are able to be redeployed to higher value tasks. However technologies such as RPA may not be the complete solution  for end to end regulatory reporting and that is where hyperautomation will come in but this may take time. Complete automation is often complex and can take years to implement requiring a transformation in the culture of a business.

In addition, hyperautomation can significantly reduce financial losses due to fraud, accidents, and errors. According to research from Crowe and the University of Portsmouth’s Centre for Counter Fraud Studies (CCFS), in 2018 global losses due to fraud were calculated to be  5 trillion USD (6% of global GDP). Hyperautomation, using RPA and Machine Learning, can solve some of these problems. Using hyperautomation for transaction processing is efficient and transparent and generated information (action logs) can be used by machine learning for recognition of predictive patters and trends. Additionally, blockchain can be used to identify the origin of funds used in a transaction.

Key challenges and what does the sector  need to think about before implementing hyperautomation

Hyperautomation and its implementation is not only a set of technical projects but one that will transform the entire business. The complexity of this means that for implementation to be successful comprehensive planning and a full understanding of current processes needs to be made.

• Compatibility with existing systems -Many companies that we are talking to have legacy systems that cause problems with the implementation of hyperautomation. Most of the banks we deal with have an extensive number of different applications and systems, like a core banking system, accounting, CRM, scoring and fraud detection, reporting. Some of these can be custom developed, others are legacy systems. For such technological landscapes, integration projects can be a real challenge that requires significant investment, planning, communication and interaction between multiple departments.
  

  Yevhen Berko

• Staff concerns – The potential threat to staff and their employment can cause resistance or even opposition. The solution is education and the availability of information. Despite the widespread bias, the goal of hyperautomation isn’t to replace humans as employees. It is to ease the burden of more tedious, simplistic tasks so that human staff can be put to more creative, strategic use. The view is that by combining comprehensive automation with human intelligence, businesses will be able to operate at a higher level and provide a better customer experience. For example, whilst speaking to a client, a support bank assistant may need to open five different applications. Opening these applications and looking for the name and surname may take up to 25% of a call. RPA can help to pre-open these applications onto the appropriate screen.
• Management disconnect – One of the most difficult challenges can be caused by senior executives disagreeing on what process and activities should be prioritised for automatisation. We have found that some may prioritise small and poorly visible opportunities and others may extend the scope too much. It is important to find the right balance between taking a slower approach and reducing risks of improvements with trying to automate as much as possible and quickly. Implementing complex projects requires discipline, project governance and communication across all departments of the organisation.
• Cost –Although hyperautomation technologies are developing rapidly, there is a lot of work to be done in order to break technical limitations. Currently hyperautomation is suitable for processing high-frequency and high-volume tasks for example:
• Collecting real-time data from various financial sources
• Analyzing market sentiment or predicting trends
• Analyzing and creating insights (for example – identifying unusual client transactions using geospatial analysis),
• Predicting and decision making (for example – identifying assets to be tracked, depreciated and amortized),
• Using language to read, speak, write and interact (for example – guiding bank clients in the onboarding process through the use of intelligent chatbots).
• Automation of knowledge work is one of the most complicated tasks so adoption of prediction-based machine decisions for high-volume and risky operations on a stock market is more of a challenge.
• Data- The success of any hyperautomation system will depend on the quality of the data it is automating. Having the correct and right data that is accessible and properly managed is already a challenge for many businesses and this will have a strong impact on the automated process.

Summary

In a digital landscape that’s moving at hyperspeed, hyperautomation offers businesses the right tools to optimise and future proof their operational processes. However,hyper automation is complex and is not an over night solution. The key for banks is to focus on activities that can be automated in the short-term while continuing to adapt to increasingly complex regulatory reporting requirements and implementing innovative new technologies.