With the sudden wave of transformation across the financial landscape, there is extreme pressure on companies to remove repetitive and monotonous tasks with robotic process automation (RPA). According to Data Hadoop, 10% to 20% of the entire work hours of a company are focussed around repetitive tasks.
According to Forrester, the RPA market which was hovering around a valuation of 250 million USD in 2016 is expected to soar to 2.9 billion USD by 2021 and according to McKinsey, 6.7 trillion USD by 2025.
Thus in order to remain competitive within the BFSI space, it is highly important to understand the work of automation, the role of robotics in creating an effective virtual taskforce and why companies are failing to initiate RPA to its finest capabilities.
According to Business Today, around 30-50% of robotic projects are failing.
Mapping the Journey for Robotic Automation Services in the Financial Space
When considering the enormity of potential that RPA has, in creating a virtual taskforce, most enterprises haven’t made much use of it. Major financial institutions have barely made a scratch or two on its surface without harnessing its true power mainly due to the complex and technical concerns regarding the safe integration of it with the existing systems. Hence there remain several mundane tasks such as financial accounting and processing where a strong labour force is working in spite of an RPA toolkit.
According to a report by KPMG, Financial institutions could be expected to save up to 75% with the implementation of RPA in their current processes. Failure of implementation could cause these very same institutions to become obsolete.
Gearing up for the New Year requires a 360° panoramic view of the offerings that robotics can bring out. The success of RPA would not only depend on how well these automation technologies can be extended to provide human advantage, but also on how well RPA combines well with the existing technologies to produce an intelligent toolkit which aides enterprise operations and provides a powerful human and virtual workforce.
Mandatory Implementation for 2019
- Data Entry and Invoice Generation: Repetitive tasks like data entry are monotonous and can easily cut 40% in savings. During the on-boarding of customers or clients, 90% of the work involves manual data entry. More importantly, usually in any accounting process, the most number of errors arise during the data entry process. Robotic automation processes cannot complete erase data entry processes, however the workforce on such processes can be reduced through data pattern recognition and conversion of text into machine encoded text.
A Success Story for the RPA market
Australian bankers ANZ were among the first financial institutes and enterprises globally to bring into effect an RPA solution to departmentalise and solve their major workflow problems. A single software bot was introduced to take care of menial tasks. Upon its success, 100 bots were introduced to take of several more tasks and soon a 1000 software bots were taking care of repetitive tasks. The number of resources working in the payments department went from 45 to 2.
At present, RPA provides with a lesser count of errors and a higher consistency in the performance of end-to-end robotic processes aimed to remove human workforces from repetitive tasks. However should RPA technologies become intelligent? Almost every company has started implementing RPA in one way or the other, but very few have actually thought of providing for intelligent process automation (IPA).
Expectations for 2019
RPA has always seemed to be unintelligent or a dumb automation process yet efficient while AI has shown its role in providing intelligent perspectives for decision makers. However banks and other financial institutions, to gain the most from the technology available will be expected to integrate RPA and AI together to obtain a fresh customer experience (CX). Also, even though AI has been known for improving customer experience, there is scope for increase in operational efficiency within organizations which can be observed through the integration of RPA and AI.
Certain tasks which require human judgement cannot be actioned by RPAs. They require human-like intelligence and not just human programmed technologies. However with the coupling of AI and RPA, these actions such as reviewing and analysing financial documents can be performed with faster accuracy and efficiency than any human. Digital documents can be comprehended by the use of deep learning technology and account transactions could be monitored by natural language processing (NLP) tools. Organisations end up with massive cost cuts, sometimes up to 70%, lesser human work force, speed, accuracy, efficiency and a highly productive human work force.
An area for banks to focus on process automation would be in the field of credit card payments. Card personalization, delivery, understanding customer behaviour and card blocking are some of the tasks that can be automated and what can reduce the workforce significantly. Other areas for banking and financial institutions can invest with RPA are customer servicing, customer fraud education and complaint handling. With such improvements, banks would be able to effectively serve customers.
Intelligent RPA: Concentration Points for 2019
- Breaches: Compliance breaches and breaches against rules and regulations of the account can be performed with the use of intelligent RPA. Data collection from the customer can be obtained through the robotic device while the different conditions based on which the breach is announced can be obtained through NLP. With the integration of AI and specifically machine learning methodologies, the account can be verified to confirm the breach.
- On boarding of Clients and Customers: During the on-boarding of clients or customers, the verification process is a long and tedious task. The collection of several financial and identification documents and the verification of each are done manually by several financial institutions. This can be altered with the introduction of intelligent RPA to which the risk factor is inputted and AI can use machine learning algorithms to filter high risk clients from low risk clients with minimal human workforce. This same methodology also could apply to financial eligibility checks on loans and policies. At present the time taken and the resources needed for such processes is astronomical when compared to where it could be.
In conclusion, RPA has been introduced by most financial organisations for their operational efficiency. However, most CXOs and decision scientists are still sceptical about its integration with the existing systems and the security of confidential data. It is highly important and an undeniable fact that RPA allows your work force to focus on more important tasks which require complex analysis. Also it is highly expectant that certain organisations will tread the path of mounting their AI technologies with RPA to sap the untapped performance that can be provided by it.