Banking and finance are two industries where automation is playing a significant role. Hyperautomation in banking and finance industry improves efficiency and productivity, reduces costs, and creates new opportunities. It is also helping to deliver a higher level of customer service.

The financial industry has been one of the first to embrace this technology. Banks use it for processing payments, managing accounts, and automating certain back-office functions such as risk management and credit scoring. 

Everything from contact-less payments to QR scanning and the usage of RPA in insurance sectors has made several financial institutions realize the true potential of hyper-automation. 

In this article, we will take a look at how banks and other financial sectors leverage the power of use automation in their operations so you can understand what it means for you as an end-user or customer. But first, let's know about hyperautomation.

History of Hyperautomation

Hyper-automation has been a trend in the business world for several years now. But, the term ‘Hyperautomation’ was first coined by Michael Abrash, an expert on virtual reality and game design. 

He uses this term to describe the use of these technologies to automate tasks that humans traditionally do with complex instructions. 

What is Hyperautomation?

Gartner defines hyperautomation as:

“Hyperautomation is a business-driven, disciplined approach that organizations use to rapidly identify, vet and automate as many business and IT processes as possible.”

To put it simply, Hyperautomation is a term used to describe the use of automation, robotics, and AI technologies to improve the efficiency of industrial processes. 

Hyperautomation involves the use of multiples tools, technologies and platforms such as:

  • Artificial intelligence (AI)
  • Machine learning
  • Robotic process automation (RPA)
  • Business process management (BPM) & intelligent business process management suites (iBPMS)
  • Integration platform as a service (iPaaS)
  • Low-code platforms
  • Event-driven architecture
  • And, several other tools & technologies

Hyper-automation can be used for various purposes, such as customer service, product manufacturing, and inventory management. Here we will discuss the role of hyperautomation in banking and what it means for you as an end-user: 

The Role Of Hyperautomation In Banking And Finance

The banking and finance services industry has been one of the most rapidly growing industries in the world. With this growth, it has become imperative for banks to deliver these services efficiently, reducing the risk of loss or delay.

Automation has become a fundamental part of every business today. It has revolutionized work processes and made them more efficient. The use of automation software has become an essential part of everything from software development services to test automation services

Automation helps us save time, money, and effort so that we can focus on other tasks that are more valuable than repetitive tasks, such as data entry or accounting tasks.

The role of hyper-automation is to increase the speed at which banks can process transactions, manage their assets, and provide products and services to customers. In addition, it helps them achieve their goals by providing increased accuracy and visibility into the processes they need to run.

Banks have been using hyper-automation for a long time now. However, with the advent of blockchain technology, we are now seeing a new era of hyper-automation in banking and finance where blockchain will play an essential role in making it happen!

Here are some benefits of using hyperautomation in banking and financial sector:

1. Saving time: 

Time is one of the most valuable resources that organizations have at their disposal. Automating your data entry process with auto-document generation software, hyper-automation can help banking sectors save time by eliminating tedious manual operations such as typing data into databases manually or copying information from one place to another. 

2. Reduced Error

Hyperautomation helps banks reduce system errors, ensuring that customers are always served properly. Banks can use hyper-automation to ensure they have the right technologies and software solutions assigned to detect and solve errors. With the help of image recognition solutions and artificial intelligence, errors in big data security systems can be validated in a matter of seconds and corrected. This result is error reduction for banking services and better customer service for customers.

3. Increased Efficiency

Hyperautomation allows banks to run efficiently, effectively, and profitably. Hyperautomation enables banks to operate at maximum capacity with minimum human intervention. This means that the banking and financial sectors can focus on their core competencies while leaving the mundane tasks to machines or other software systems.

4. Reduces Expenses

Another benefit of hyper-automation is that it can save both time and money. With systems like IBM Watson, businesses can generate new revenue streams by offering big data analytics services. For example, a bank can provide an AI solution for customers who want to make more informed decisions about their finances or investments. The bank could then use this data to generate revenue from selling these insights to other customers – a win-win situation for everyone involved!

5. Increased Productivity 

Since there is no need for supervision or human intervention, manual employees can spend more time doing creative work and less time doing mundane tasks. The RPA agents or automation software will handle all the repetitive tasks, thus enhancing the productivity of the business and the employees. 

The Role Of Artificial Intelligence And Machine Learning In Hyperautomation

Artificial intelligence (AI) and machine learning are two buzzwords often used interchangeably, but they are different. AI is the study of how to make machines think like humans, while machine learning is the use of statistical techniques to build systems that can learn from data.

Hyper-automation is a new manufacturing paradigm that combines these two technologies to revolutionize how products are made.

Hyperautomation uses AI to analyze data from a customer’s transactions history and then uses machine learning algorithms to produce predictive models about how likely a customer will buy a product or service offered by the bank. 

Artificial intelligence (AI) and machine learning (ML) are two of the most exciting technologies changing the world. They are also highly disruptive and impactful. Companies are using AI to increase productivity and lower costs, while ML is being used to automate processes, reduce errors, improve accuracy, and boost productivity.

Hyperautomations combine the best of both worlds: they use AI to make complex decisions about what should be done next based on historical data; then they use ML to automate those actions. This means that Hyperautomations can be faster, more accurate, and more efficient than traditional automation methods.

With the rapid adoption of AI and machine learning, there is an increasing trend for businesses to automate their processes. This goes beyond simple tasks such as data mining, information processing, and decision making. Sometimes, it can mean automating complex human-in-the-loop processes and activities (such as maintenance, repair, or even production).

Challenges Associated with Hyperautomation in Banking & Finance Industry

While automation is a great tool to increase efficiency and productivity in many industries, it comes with its challenges. These include:

Specialization: The specialization of machines used for automation is a major challenge for companies. 

Let’s take two banks, bank A and bank B for example. Bank A uses AI-powered tools to predict the creditworthiness of customers, While bank B uses the manual approach of going through transaction history to predict the creditworthiness of customers. Bank A also uses different software to provide real-time customer support. Bank A also uses AI marketing tools to boost its online presence.

As you can see, with the adoption of hyperautomation in the finance industry, the specialization of machines/tools has increased. 

Data management: Another challenge involves managing data across all systems involved in automated processes. 

Reconsidering our previous example, Bank A has to manage tens and thousands of logs, traces, metrics and datasets generated by individual tools which is a major challenge in today’s world where there’s a data breach every 32 seconds!


Hyperautomation has left its mark in the finance sector and several other industries, including product development, industrial parks, warehouses, hospitals, and much more. 

To meet the growing customer demands and to change market trends, it is essential that businesses must start adopting some form of automation into their existing workflow. 

With the help of automation, companies can meet the growing demands and improve employee productivity, work efficiency, and, most importantly, customer satisfaction.