Europe Fintech

Robots and Retail Banking – What’s Next?

Robotic Process Automation (RPA) replicates human actions by using software. RPA creates a virtual ‘worker’ or ‘bot’ to perform actions within and between applications, removing the need for human interaction. 

As companies and organisations grow, their processes are becoming more complex. This is when RPA can help – using software automation techniques, RPA can cut organisational costs and streamline resource-heavy operations. 

Consistency, speed, cost, and scalability are all benefits of Robotic Process Automation. While this progressive technology is applicable across many industries in various ways, retail banking (and the financial services sector in general) can benefit greatly from its application given the heavy regulatory requirements of the industry and its emphasis on process-based transactions. 

RPA’s impact so far

RPA has had a significant impact on retail banking in recent years. One example of this is customer onboarding. 

This can be demonstrated by the initial contact form generated for each product. The customer’s profile must then be created across multiple internal systems, traditionally accomplished through manual entry by an employee, who would then set up an individual account for the customer across various platforms. 

This is a time-heavy process, and is prone to error. RPA can instead create a virtual bot to carry out these tasks, cutting out the need for the employee and eliminating the chance of human error.

RPA has also proven useful in fighting fraud and malicious attacks. In 2018, the Financial Times reported that bank customers had lost half a billion pounds to scams. RPA can help fight against fraudulent scams by sifting through large volumes of data such as transactions, customer profiles, and public databases of stolen information, making fraud identification easier and faster. 

The limitations of RPA

While RPA offers several benefits to retail banks, it is not without its limitations. Importantly, banks must keep in mind that RPA software is incapable of dealing with tasks requiring comprehension or judgement. 

For an RPA bot to work effectively, it should be applied to processes that are well-understood and documented but most importantly are rules-based. This is most applicable in transactional, high in volume products which are limited in complexity.

What next for RPA and retail banking?

According to Felicity Duncan, Financial Researcher and Writer at Intuition Publishing, an eLearning provider focusing on the financial services industry, Robotic Process Automation is becoming a hot topic in the world of finance. Duncan says many banks are becoming more serious about their RPA rollouts and as a result, interest is spiking across the financial world. 

Evidence of this can be found in the rise in the number of financial services professionals learning about RPA. At Intuition, RPA was among the most popular courses in the first quarter of 2020 and has retained its popularity since. 

She said, “While RPA has clear value for those working in retail banking, we must be mindful of its rigidity. Unlike dynamic organisations, RPA is bound to specific rules and requirements. This lack of flexibility makes it incapable of keeping up with the ever-changing retail banking landscape.”

Two key technologies will play a significant role in the development of RPA over the coming years – artificial intelligence (AI) and machine learning (ML).

AI is considered the true endgame of RPA. A powerful vision, it imagines a time when machines will be as – if not more – intelligent as a human, creating suitable (and possibly superior) substitutes for any task or role within retail banking. 

ML refers to the ability of algorithms to improve their performance over time using a set of computational techniques. One of RPA’s obvious limitations is the requirement for human direction to change and improve. 

ML addresses this limitation, cutting out the need for human input as the computational system steadily optimises its own parameter as it iterates and learns. 

Through the use of RPA and AI, organisations can address issues such as fraud more effectively. 

While these innovative technologies have historically benefitted businesses through their back-end application to issues such as those mentioned above, it’s interesting to note that customers also have something to gain. Advancements in RPA may transform the customer experience.

It is already apparent how voice assistants are becoming increasingly common in the banking industry. Used as a means of meeting customer needs, voice assistants can give basic guidance and improve satisfaction levels within a customer base through the elimination of frustrating call centre experiences, long wait times, and high phone fees.

One customer-facing application of these technologies is robo-advisors. These automated bots can help customers make financial decisions without the need to consult an expensive human financial advisor. A customer can supply basic information through an online questionnaire touching on relevant areas and the robo-advisor will then run their data through an algorithm and generate specific recommendations. 

As these customer-facing developments are likely to continue, from a business and consumer standpoint there could be an interesting shift in the world of retail banking over the next few years.


  • Gina is a fintech journalist (BA, MA) who works across broadcast and print. She has written for most national newspapers and started her career in BBC local radio.

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