Intelligent Automation: The New Frontier in Banking

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This is a guest contribution to Finance TnT by Sarah Burnett and Amardeep Modi of Everest Group, a Dallas-based management consulting company. The views expressed are those of the Burnett, the company’s vice president, and Modi, senior analyst, and not of Finance TnT.

Back in the 1990s, ERP and shared services concepts fueled the emergence and growth of centralized finance and accounting, human resources, procurement, and other business functions. In addition to providing cost savings, these provided other benefits such as more predictable and transparent business operations.

As the use of offshore has begun to reach saturation, organizations are looking for further ways to deliver innovation in business and IT services to cut costs and achieve additional objectives, such as improved service, optimized processes, and reshoring. In the shifting world order, technology and automation are fast emerging as key levers to create best-in-class business outcomes.

sarah

Sarah Burnett, vice president of Everest Group.

Service Delivery Automation

Service delivery automation (SDA) is Everest Group’s term for a raft of newer technologies that automate a range of business and IT processes. Automation — at its most basic level — must uses technology to replace a series of human actions. SDA can encompass different portions of a process, from the input to the core part of the process through to its output. SDA technologies include robotic process automation (RPA) and automation tools with intelligence built into them.

RPA refers to automation that interacts with a computer-centric process through the user interface, or user objects, of the software application supporting that process. A robot is usually a runtime environment on which different processes or tasks (executables) can be run and uses surface and non-invasive integration. RPA tools can handle rule-based and repetitive tasks and process structured data.

Artificial-intelligence-based automation tools have a variety of intelligent capabilities, such as natural language processing (NLP), pattern recognition, and machine learning. They can handle unstructured, semi-structured, and structured data.

The Rise of Robotic Process Automation

When RPA first arrived in the global services market, there was some degree of doubt and incredulity at the ability of robots to fulfill business processes. This has given way to enthusiasm and adoption by many organizations. This is reflected in the revenue growth of technology vendors who have seen impressive growth rates of 70%–120% over the last two years. The growth is accelerating too.

On the buy-side of the market, Everest Group research shows that the banking, financial services, and insurance (BFSI) sector is at the forefront of adoption of SDA technologies with a 25% share of the market. This largely reflects the adoption of RPA-style of automation.

Some of the examples of common-use cases of RPA are transaction processing, data entry in high-volume, repeatable, and computer-centric processes within system upgrade scenarios. In scenarios like this, the double and concurrent data entry into old and new systems is required during the period of change, and RPA is a great efficiency booster.

Evolution of Automation into Artificial Intelligence

While RPA tools typically focus on processing structured data (data in databases), intelligent or smart automations come with the ability to process unstructured data (e.g., web and document content) as well as semi-structured and structured data. Using smart software is the new frontier in automation, and we will soon also see smarter enterprise software emerge.

Already, there are software tools that automate IT and business processes with varying degrees of intelligence, using capabilities such as pattern recognition, trend spotting, and predictions that leverage big data and analytics, semantic search, natural language processing, and intelligent knowledge management systems. They also offer machine learning — meaning that they can monitor manual processes and learn how to undertake each process. At the higher end of the spectrum, there are deep learning and neural network-based intelligent software.

Applications of smart automation in the BFSI sector include, but are not limited to, anti-money laundering (AML) checks, company financials and results processing, due diligence, director information checks, incoming document processing (for example, insurance claims with their supporting evidence), fraud prevention, “know your customer” (KYC), sanctions alerts, trade algorithms, and robot advisors.

Amardeep Modi, at Work Fusion

Amardeep Modi, senior analyst at Everest Group.

Key Drivers of SDA

There are many reasons an enterprise might want to implement their SDA capabilities. The following are just a few:

  • Cost reduction: SDA can yield cost reduction of 35%–65% for onshore process operations and 10%–30% in offshore delivery
  • Improved processes: Better quality, reduced errors, speed, compliance, security, and business continuity
  • Return on Investment (ROI): It can take as little as six to nine months to recover SDA investments
  • Non-invasive nature: It is largely non-invasive — not requiring major IT architecture changes or deep integration with underlying systems
  • Easier management and control: UI-based automation creation options along with dashboards for monitoring operations makes it convenient for running and managing the automations.

The Future and New Opportunities

The impact of SDA technologies will be even more far reaching as these change the future of work as well as the means of doing work. Enterprises are already in the midst of the RPA revolution and are at the beginning of the smart automation journey.

The journey will see smart automation go from processing information and capturing insights to making increasingly more complex decisions. Examples include identifying fraud patterns and rings of perpetrators using insights gleaned from big data combined with machine learning algorithms for decision making. These scenarios can lead to new business opportunities and spawn new digital businesses powered by superior analytics and better knowledge management than we have today.

While we may see a reduction of knowledge-worker jobs, the improved, AI-based, complex decision-making capability will in turn lead to new global sourcing opportunities and new job roles, such as decision algorithm designers, that will partly make up for job losses due to automation.

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