Emergent tech that are poised to drive the financial and banking domain in the very near future Part 5
Human error within the financial space has the outcome of 25,000 hours of preventable rework, as an average per enterprise and expenses $878,000 annually. Robotic Process Automation (RPA) is one of the obvious solutions to this issue. Going by a research carried out by McKinsey, approximately 6/10ths of occupations can help in automation of more than 3/10ths of activities with RPA.
The fifth part of this multi-part blog series looks at the tasks that you can undertake automation for within banking and financial enterprises. And upon determination of the tasks, where to begin with RPA implementation in banking?
RPA within finance and banking
There has been a steady uptick of RPA implementation within the banking and financial space, and within industry at large. RPA has been a revelation for teams globally, in facilitating the creation of plumbing to assist processes in a streamlined way. Banks, in particular, suffer from the issue of well-integrated applications. This is especially true in the back-end. With these factors in mind, RPA can serve as the foundation of enterprise-wide transformation that is needed.
RPA, or Robotic Process Automation is restricted in what it can perform. It merely adheres to the rules in automation of tasks that have no variance. For instance, it is capable of logging into an account, shift some files, and log out.
To further improve RPA, banks undertake deployment of smart automation by adding AI technologies, like machine learning (ML), and natural language processing (NLP) capacities. This facilitates RPA software to manage sophisticated processes, comprehend human language, identify emotions, and adapt to real-time information.
Based on the research by McKinsey, general accounting operations have the biggest prospect for RPA within the finance space. Business development can only be automated for approximately 56%.
Advantages of RPA within the finance and banking space
RPA is being harnessed in various industries, like manufacturing, healthcare, and insurance. The international RPA market had an approximate value of $1.57 billion in 2020 and is predicted to appreciate to a CAGR of 32.8% in the timeframe ranging 2021 to 2028. Based on the opinion of Gartner, 8/10ths of leadership personnel are currently leveraging some variant of RPA for divergent purposes.
RPA can have transformative potential in specific scenarios. AI-driven RPA is gaining traction within the financial and banking space, and it functions as a fortified version of regular RPA. This section of the blog will look into the differences, and take a deeper look at both.
AI-driven RPA / AI-driven vs Conventional RPA
- Via automation of well-defined tasks like creating users in SAP to reboot servers and operating with structured data such as process of PDF or Excel in a specific format.
- RPA has conferred advantages to enterprises with speed and expenditure-savings.
- However, in a majority of scenarios, there can be unstructured data such as Excel, invoices, email requests, in differing formats.
- Conventional RPA has troubles undertaking process of natural language, web content, imagery, and textual data.
- AI and RPA in conjunction facilitate task automation and undertake processing of unstructured data where conventional RPA cannot.
- AI broadens RPA capacities in the pursuit of processing unstructured data, image content, and comprehend natural language.
AI is a technology where machines can comprehend the natural language and function like a human agent. RPA + AI facilitates automation of work activities and process the unstructured data where conventional RPA cannot. AI widens the gamut of RPA capacities to undertake processing of unstructured data, image content, and comprehend natural language, etc.
Cognitive RPA has the edge over Conventional RPA
Cognitive automation is an extra feature included to the RPA repertoire, facilitating solutions to harness AI tech for automation of tasks by comprehending the natural language. In days prior, this could only be carried out by human employees. One of the most critical capacities of cognitive automation is the process of unstructured data, imagery, text.
Document process is a vital task for the enterprises undertaking analysis of the unstructured data such as documentation, or imagery is time intensive as it has to be performed manually. Automation of this is innovation and can have a major influence on the enterprise’s efficiency.
Key capacities for Cognitive Automation
- Natural Language Processing (NLP): Fundamental language comprehension makes it a lot simpler to deploy automation for a majority of client service processes. NLP is capable of identifying resolutions to queries with no intervention from human agents.
- Optical Character Recognition (OCR): OCR facilitates automation of the document formats such as images, handwritten forms and copies that have been scanned. This can have a major impact on the business procedures in document-oriented domains such as banking, law, insurance, manufacturing, retail, and law through automation of processing documents such as invoices, handwritten forms, cheques, etc.
If an enterprise obtains thousands of invoices on a day-to-day basis, undertaking process it with RPA + OCR will minimize the massive number of man-hours to process the documentation with enhanced TAT and reduced expenditure.
- Machine Learning (ML): Decision making is carried out by ML algorithms through comprehension of the natural language of the process. ML algorithms produce data patterns and have the potential to learn from historical data to comprehend the meaning.
With the assistance of ML, automation of processes by substituting human judgment with machine judgment. With the help of historical data, bots can comprehend the email requests and put out a ticket within service desk systems.
Structured data vs Unstructured data
- Structured data is ordered and undergoes labelling in an appropriate manner where the machine can comprehend it easily.
- This data is fitted into a relational SQL database and can function well with the most basic algorithms.
- The structured data is really simple to adapt for automation and has an improved success rate.
- Several enterprises are leveraging structured data for the purposes of automation.
- Unstructured data is tough to interpret through algorithms.
- Unstructured data consists of textual data, imagery, PDFs, natural language input, scanned documentation or web content.
- This data is really tough for automated systems and frameworks has to be translated to structured data manually by a human to undertake subsequent processing.
- Most enterprises are identifying complications in the extraction of data from unstructured data.
- Automation of the unstructured data turns into a pressing issue for several RPA solutions.
- The critical docs that a majority of RPA solutions can’t parse include invoices, scanned apps, imagery, client emails, and voice messaging.
- This makes it tough for automation to be harnessed in all front- and back- office business processes. To surpass these problems, enterprises have to take up cognitive automation.
Transformation and the part played by low-code development
The uptake for digital banking has been slower than desired. It has gained traction during pandemic times, with customer-facing, digitally based deployments being increasingly used. To further accelerate the penetration of these technologies, concepts like low-code and no-code software dev platforms have propped up on the scene. Low code is the easiest solution to implement that could assist financial service players (FSPs) tide over the hurdles encountered by the banking space to adopt digital transformation quicker.
The kicker with the tech is that it can accelerate software development cycles with zero investment of time resources and labour. Who doesn’t want that?
The reality of low-code development
- Integrated, homogenous experiences have been the norm in the space.
- There is prevailing reluctance with regards to low-code; devs tend to be hesitant of relinquishing control over their code, as low-code doesn’t provide this level of control.
- Universal penetration is being prevented and delayed owing to a lack of consensus in developer acceptance (among the community).
- The Upper layers of application architecture, and how microservices function with each other is a different paradigm.
- There is an inability in identifying resolutions to long-running transactions.
Customer frustration almost always leads to cart abandonment. Customer frustration can be caused by broken application features, or website repairs that impact normal functionality during the installation of a feature upgrade. Unfulfilled clients have a way of informing you of their battles and it confers advantages on an institution to identify them. The complexity and difficulty in bringing these feedback into dev is due to a massive gulf amongst departments that operate in siloes.
Financial enterprises are finding it a challenge to identify the balance amongst development and business application for their digital ventures. Low code falls in between this struggle by including the business-oriented viewpoints into development by reducing the gap between tech dev and business dev.
9/10ths of IT Leadership recognize that the flexibility of design of these platforms assists in considerably enhancing client experiences in contrast to conventional development platforms. Low code minimizes the number of feedback loops by swift implementation alterations into the application software.
As we’ve just observed, RPA has transformative potential in specific settings, under the right conditions. AI-driven RPA is penetrating the market, and we saw some fundamental things that distinguish AI-driven RPA and conventional RPA. We also saw the difference between structured and unstructured data, and we took a deeper look at Cognitive RPA.