Intelligent automation and hyperautomation will drive the future of finance image credit: shutterstock

Today’s financial services companies operate in an increasingly complex and rapidly changing landscape. They navigate constantly evolving customer expectations, geopolitical shifts, and heightened regulatory demands—all in a particularly challenging economic environment.

Many financial organisations already have a strategic transformation programme as part of their operational and technology infrastructure to respond to these challenges. They know that new technologies, such as generative AI, will bring profound change to the banking industry, triggering a cognitive leap that can fuel productivity and innovation at an unprecedented scale.

According to recent McKinsey data, generative AI, if integrated well into business operations, can deliver between $200bn and $340bn in annual operational savings for the financial services industry globally while improving customer satisfaction and employee experience. In addition to increased revenues, generative AI could help financial institutions generate additional value from higher productivity of between 2.8% and 4.7% of the industry’s annual revenues. Unsurprisingly, banking is projected to be one of the two industries spending the most on AI solutions by 2024.

To optimise what AI can do, financial organisations need to ensure AI is fully operationalised and securely embedded into their workflows. The most effective and secure way to achieve this is through intelligent automation (IA) hosted on a private AI platform.

Intelligent automation and hyperautomation as competitive advantages

There are three levels of automation. Each level builds on the complexity and power of the previous one.

Simple automation involves one automation used to solve a simple task. For example, robotic process automation (RPA) extracts and collects data from websites or applications to automatically fill in forms and data fields.

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Intelligent automation (IA) refers to automation technologies that enable rapid end-to-end decision-making, creating workflows that can evolve without human intervention. Intelligent automation uses AI and RPA to create a dynamic framework where processes adapt in real time.

Hyperautomation is a holistic strategy that connects people, systems, bots, AI, and business rules into automated end-to-end workflows. It draws on AI and RPA combined with other tools like decision rules, workflow orchestration and intelligent business process management (iBPM).

In an era of frequent digital disruption, financial institutions can leverage intelligent automation and hyperautomation to position themselves as pioneers of digital transformation. With these technologies and strategies, firms are better equipped to handle complex activities, achieve efficiency, boost revenue and deliver exceptional client service.

Saving time, a precious commodity

Time is one of the most valuable assets for any organisation. Financial processes often include repetitive and time-consuming tasks, such as data entry, identity verification and transaction monitoring for fraud prevention. These tasks must be handled with absolute accuracy, as the smallest error can result in costly consequences. It is in these areas that intelligent automation thrives.

Automation not only streamlines these activities and reduces the chance of errors but also enables financial organisations to manage complex workflows and regulatory requirements more effectively. This boost in efficiency frees up employees to think critically about higher-value tasks, enabling financial organisations to invest more resources in strategic initiatives while fostering innovation and future-oriented projects.

Intelligent automation and hyperautomation can significantly reduce operating costs and help firms excel in a competitive business landscape. Time-saving benefits are not just limited to internal processes; they translate to client satisfaction. For instance, as customer expectations evolve, financial companies can speed up customer onboarding through data extraction, automated fraud check and identity verification. They can deploy chatbots, which are available 24/7 to resolve customer queries quickly and enhance personalisation to deliver a more enjoyable banking experience.

Process mining to guide successful implementation

The journey towards successful automation implementation requires careful planning and strategic execution. Haphazard deployment can dilute the value and even lead to adverse outcomes. Companies should not aim to automate everything; they need to identify where automation can make a difference and be realistic regarding what is achievable. Process mining can help with this. Process mining tools identify problem areas requiring attention and present the findings in a visualisation resembling a flow chart. With automated intelligence, process mining proactively identifies bottlenecks, process non-conformances, and the root causes of issues inhibiting digital transformation goals.

Another key step is integrating data from multiple sources. A notable advantage of the intelligent automation and hyperautomation approach lies in its potential to dismantle silos — isolated pockets of information that hinder efficient collaboration. Employees across financial institutions must navigate multiple, contrasting systems to bring together relevant data. To bridge this gap, low-code solutions with accessible APIs can help seamlessly integrate with existing technologies to unify systems and expand automation capabilities, bringing data from several technologies into one single repository that forms the backbone of streamlined processes.

Looking ahead

Changing regulations are a catalyst for transformation. Announced in December 2022, the Financial Services Market Act (FSMA) 2023 sets out plans for regulatory sandboxes to encourage the testing and development of new technologies, incentivising competition and innovation.

As the pressure on financial organisations to innovate faster and do more with less intensifies, there will be a greater need for technology-driven transformation. As the generative AI era is in its infancy, there is an opportunity for all industry players to drive productivity and boost customer satisfaction through AI-driven intelligent automation and hyperautomation. With a smart implementation strategy, firms that adopt these tools can accelerate innovation and unlock new revenue streams, contributing to the success of the industry and the economy as a whole.