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Hyperautomation in Banking: Use Cases & Best Practices [2024]

The use of emerging technologies such as artificial intelligence and RPA is already prevalent among businesses in the financial services industry:

  • By 2024, banking will be one of the top industries spending on artificial intelligence.
  • About 80% of finance leaders have implemented or are planning to implement RPA.

The next step in enterprise automation is hyperautomation, one of the top technology trends of 2023.

In this article, we’ll explore why the banking industry needs hyperautomation, its use cases, and how banks can get started with their hyperautomation journey.

Why is hyperautomation important for the financial services and banking industry?

Hyperautomation is a digital transformation strategy that involves automating as many business processes as possible while digitally augmenting the processes that require human input. Hyperautomation is inevitable and is quickly becoming a matter of survival rather than an option for businesses, according to Gartner.

There are several challenges specific to the financial services industry that make adopting hyperautomation necessary:

  • Consumers increasingly prefer digital channels such as mobile and online banking and expect a more personalized banking experience. The COVID-19 pandemic has accelerated the shift to digital preference (Figure 1). Moreover, nearly half of customers want their bank to provide personalized offers and information.

Figure 1. COVID-19’s impact on the use of digital channels in banking

  • As JPMorgan Chase CEO Jamie Dimon puts it, banks face an “enormous competitive threat” from tech giants such as Amazon, Apple, and Google. Since these companies have access to large amounts of customer data and have the tools to make use of it, their entrance into the financial service industry poses a threat to traditional financial institutions.
  • Lower regulatory barriers facilitate increased competition from fintech startups. A fintech company has lower capital requirements, no liquidity requirements, low cost for compliance, and fewer privacy restrictions compared to a regulated bank. These enable fintech startups to adapt to user needs faster.

Hyperautomation can help financial institutions deal with these pressures by reducing costs, increasing productivity, enabling a better customer experience, and ensuring regulatory compliance.

What are the use cases of hyperautomation in banking?

We’ve explored the use cases of individual hyperautomation technologies in banking and financial services, such as:

Combining these technologies enables hyperautomation to achieve end-to-end process automation. Use cases include:

Back-office operations

On average, retail banks have between 300 to 800 back-office processes to manage and monitor, such as:

These processes often involve time-consuming and redundant manual tasks.

Leveraging process mining and digital twins can help banks to gain process intelligence and identify back-office processes to automate. AI and NLP-enabled intelligent bots can automate these back-office processes involving unstructured data and legacy systems with minimal human intervention.


IBM’s case study1 shows how global specialty finance company Credigy leveraged RPA to automate over 25 business processes and was able to:

  • Process account-based documents by email, media download, or other types of automation and automatically load documents into an internal system that users can easily access by account.
  • Forward suspicious emails for URL scanning and alert users if URLs appear malicious
  • Support compliance by automatically downloading updates from a variety of sites and loading data into specific tables for review.
  • Automate IT audits including password strength tests. If a user’s password fails a strength test performed by the robot, the user receives an email requesting a password update that complies with password rules.

Automating repetitive tasks enabled Credigy to continue growing its business at a 15%+ compound annual growth rate. Check our article on back-office automation for a more comprehensive account.

Customer service

Hyperautomation can help financial services organizations improve customer experience in various ways:

  • Streamlining back-office tasks will reduce friction in customer service operations. For instance, banks can accelerate their Know Your Customer (KYC) processes using intelligent document processing to extract relevant data from customer documents and identify risks with ML models. This will help banks achieve rapid customer onboarding.
  • By reducing the need for manual labor and using process intelligence, hyperautomation improves employee productivity and reduces operational costs, as we discussed in our article on hyperautomation benefits. This will enable banks to spend more time on developing customer-centric products and services.
  • Banks can also leverage bots with conversational AI capabilities that offer 7/24 customer service, answer customer queries and FAQs, and help human customer service reps during their interactions with customers.

How to get started?

As we discussed in the introduction, financial services companies extensively use AI and RPA, so chances are your business already has several automation initiatives. However, hyperautomation requires a coordinated and company-wide effort to achieve scalability and agility. Therefore, businesses should:

Establish an automation center of excellence

An automation CoE is a business unit consisting of technical and business experts to guide and oversee the hyperautomation initiative in your organization. An automation CoE will:

  • Bridge the gap between business decision-making and automation implementation,
  • Identify processes to automate,
  • Create an implementation roadmap for the hyperautomation initiative,
  • Create a unified vision and standardized practices within the organization.

You can also check our articles on AI CoE, RPA CoE, and digital CoE.

Develop a change management strategy

A strong company culture for hyperautomation is as important as selecting the right tools and technologies: the cultural deficit is one of the top reasons why digital transformation initiatives fail. Organizations should:

  • Create opportunities for reskilling and upskilling for employees. 33% of job skills present in an average job posting in 2017 were not needed in 2021. As hyperautomation becomes prevalent in an organization, this trend is expected to continue. Providing digital training programs can help mitigate this problem.
  • Improve top-down communication about why hyperautomation is needed, what will change, and what employees should expect.

For more, check out our article on the importance of organizational culture for digital transformation.

Collaborate with third parties

Collaborating with third parties such as:

  • Fintech companies can help financial service organizations to be able to develop innovative products and services. Fintech companies often leverage big data, AI, and automation to develop solutions that fulfill the niche needs of customers. Partnering with fintech companies can help traditional banks discover new ways of using hyperautomation technologies to improve customer experience.
  • Non-banking organizations create a data ecosystem can help financial services companies gain a deeper understanding of their customers and improve their analytical capabilities, which are crucial to hyperautomation.

Feel free to check our article on intelligent automation strategy for more.

Further reading

If you have other questions about hyperautomation and its applications in the banking industry, we can help:

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Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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