As technology advances at a breakneck speed, financial institutions worldwide are going under strong pressure to increase efficiency, decrease costs and increase output. Indeed, there is a growing worldwide need for the services industry to fundamentally move away from established, age-old business models.
As the population of users increases, the need for financial software development bespoke services also rises that can smoothly integrate with numerous third-party systems in a constrained environment while increasing efficiency, regardless of the different financial sectors.
As we see the fintech industry ever-growing, the main reason behind it is automation. Automation has accounted for a sizable portion of the change that we see today. It is believed, robotic process automation (RPA) is going to play a critical role in job execution inside financial institutions over the next few years.
Let’s discuss the reasons behind it and the significance of RPA in this evolution!
What is Robotic Process Automation (RPA)?
RPA is a method that combines robotic automation and artificial intelligence to automate repetitive processes that were previously handled by people across applications and systems. It is occasionally referred to as "smart automation" or "intelligent automation" and therefore refers to any software system that may be configured to successfully fulfil activities that previously needed human intellect.
To be precise, it is at the cutting edge of human-computer interaction technology, providing participants in the financial services sector with a virtual workforce that is rule-based and configured to interact with your organization's systems in the same way as existing users. It is utilised to automate and develop an automation platform for usually the front office, back office, and support tasks using robots.
And with several repetitive, often mundane tasks currently performed by employees being automated, RPA clearly has profound implications for the financial services industry in terms of transforming the nature of work within banks, delivering significant improvements in customer experience, as well as cost savings and more efficient resource allocation.
Why RPA Could Be Proven Better Than Humans For Financial Sectors?
Among the primary benefits of delegating such activities to robotics are cost savings; time savings, as RPA frees up staff to focus on more difficult tasks; a decrease, or maybe elimination, of human error; and scalability, since robots are capable of doing tasks at speeds unequalled by humans.
Additionally, scalability ensures that automated systems can handle significantly bigger volumes and complete jobs in record time. Account opening is a classic example of a procedure that is repetitive, laborious, and excessively time-consuming for employees to complete. However, automation enables these operations to be completed more quickly and correctly. Furthermore, RPA has the potential to greatly enhance the integrity and quality of account data within financial institutions' systems in the long term.
The convergence of AI and RPA in banking and financial services has been accelerating as all financial organisations have embraced new technologies such as artificial intelligence (AI), machine learning (ML), deep learning, and natural language processing (NLP).
According to the latest survey, executives in financial services and bank executives are extremely interested in using automation, with 95% of them planning to begin on or prepare an automation strategy. "Cognitive Automation is the way forward," IBS Intelligence stated. Automation, supplemented by Artificial Intelligence, will drive the future value proposition of banks. This recent research from Aspire Systems demonstrates precisely that."
RPA-driven focal points
Typically, the key emphasis areas that qualify for driving robotics are those with highly automated activities that need less decision-making. These include standard back-office functions, validation and verification activities, and repetitive tasks. ICICI Bank, for example, reportedly uses over 200 software robots to emulate, automate, and perform repetitive, high-volume tasks across multiple business process functions, including retail banking operations, trade, forex, treasury, and human resources, with a daily transaction processing capacity of over a million.
This is anticipated to result in a 60% reduction in customer response time and a 100% improvement in accuracy. After addressing low hanging fruit, the method often shifts to regions with a better user interface, even if it remains focused on highly repeated actions that clog bandwidth. Intelligent process automation enables new generation bots to learn and improve at what they do.
Chatbots connected to social media and websites are excellent instances of this, as they provide clients with prompt and suitable replies. RBS has piloted the AI chatbot Luvo, which is focused on screening client interactions and offering relevant answers. The technology is expected to result in a 200% reduction in investment adviser personnel, increased operational efficiency, and a reduction in response time to enquiries. While the more fascinating applications of predictive analytics-driven AI have a significant impact, they often demand a considerably greater level of effort and preparation.
Enhancing client experiences is also aided by advances across online platforms, which preserve the traditional customer experience.
Impact Assessment of RPA
This is where things become a bit more complicated. Regardless of how unique the technique or how compelling the concept is, there must be quantifiable results. The impact is essentially quantified on a dual-axis: Robotics-driven projects may either reduce costs (efficiency) or increase the quality, convenience, and accuracy of services (effectiveness), or they can accomplish both.
Benefits of RPA
The benefits of RPA may be summarised in five main categories: cost savings, increased accuracy, increased productivity and scalability, quality assurance, and risk mitigation. Here are a few instances of applications of artificial intelligence and robotics, as well as the type of metrics they use.
1. Increased efficiency
Typical examples include the automation of routine operational tasks. This is mostly concerned with reporting, reconciliation, data cleanup, and similar duties. The immediate result of this is the abolition of manual intervention through the use of automated technology. Direct cost factors include back-office mortgage processing.
2. Increased effectiveness
Accurate risk and compliance monitoring, as well as predictive analytics-driven collections, are instances where the impact is primarily on 'potential savings' and increased income via the quality of the process, as driven by RPA.
3. Improving efficiency
These are areas where banks seek to enhance the client experience through higher-quality interactions and increased automation. Account origination, lending, investment advising, and customer service are all examples. Increased throughput and improved returns are both conventional metrics in this case.
Significance Of RPA In Finance Sectors
Sectors of banking where RPA is currently causing a sea change are mortgages and lending. Given the breadth of routine processes involved in purchasing a home—employment verification, credit checks, title orders, and inspection reports, to name a few—RPA has emerged as a prime candidate to take over many of these tasks without human intervention, significantly increasing efficiency, reducing loan processing times, and reducing total turnaround times dramatically. For example, OCBC (Oversea-Chinese Banking Corporation) makes substantial use of RPA in this field, allowing the Singaporean bank to cut the time required to re-price house loans from 45 minutes to only one minute.
OCBC's RPA bot verifies clients' eligibility for re-pricing, proposes acceptable re-pricing alternatives, and even produces customer-facing suggestion emails. All of this implies that it can process many more re-pricing applications than was previously feasible, up to 100 per day.
Perhaps most significantly for lending-related operations, RPA improves the visibility of each individual job that must be completed as part of the broader process. "With workflow automation, each step of the loan process becomes computerised, allowing for the collection of data at each stage. The days of tracking down a loan application to determine its current status are over," RapidValue, a digital product engineering startup specialising in RPA, noted. "You may conduct a search within your document management system for the document and quickly determine its state." As such, it has the potential to significantly improve the customer's entire loan experience.
RPA will also have a substantial influence on banks' compliance efforts, which is particularly helpful considering the increased expenses banks have incurred in the previous decade or so to comply with mounting regulatory requirements.
RPA can help remove the need for manual operations such as know your customer (KYC) and anti-money laundering (AML). Automating substantial portions of these critical needs will assist to minimise human error, lowering expenses, and significantly increasing the efficiency of the new client onboarding process.
Similarly, fraud detection would benefit from automation, particularly considering the constantly increasing number of instances banks have seen in recent years, making compliance teams' jobs more difficult. However, using RPA, a bot may be taught to recognise patterns of fraud and immediately escalate those instances to the proper bank departments.
And Gartner forecasted in September that worldwide RPA software revenue would reach $1.89 billion in 2021, up 19.5% from 2020 levels, notwithstanding the fiscal restrictions imposed by the COVID-19 epidemic.
The primary driver of RPA initiatives is their capacity to enhance process quality, speed, and efficiency, all of which are becoming increasingly crucial as enterprises attempt to satisfy COVID-19 cost-cutting requirements," said Fabrizio Biscotti, research vice president at Gartner. "Investing in RPA software enables businesses to make rapid progress on their digital optimization objectives, and the trend isn't going away anytime soon."
Conclusion
Indeed, Gartner anticipates that the RPA market will increase at a double-digit pace through 2024, demonstrating just how much promise banks see in this technology in the long run. With AI and automation likely to influence a large portion of how the world runs, it only makes sense that financial institutions that adopt RPA across a broad variety of business units as quickly as feasible will benefit the most from efficiency gains and cost savings. With this in mind, it's clear that the race to "become robotic" is well and far on.
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