Automation will fail to achieve most of its potential if treated as a specialty, single-function tool applied only to accelerate narrow parts of business processes. In most industries, financial services included, automation done properly is a journey that delivers a steady stream of benefits resulting from building a broader and increasingly powerful multilayered stack of automated processes and analytics.
The idea is not just to have a bunch of separate enhancements. Rather, the entirety of a financial services business should be reconsidered as one integrated automated process. While discrete parts of the system could be made better with traditional automation tools, newer tools such as robotic process automation, AI and machine learning (ML), and analytics can leverage basic automation to create a foundation that optimizes the system at higher levels.
What can this broader perspective of automation enable in practice?
Consider these real examples of success:
- A large liquidity clearing business had been clearing its trades at the end of every day, which meant it only discovered defaults after the fact, which was too late to take meaningful action. Recognizing that challenge, the company automated its clearing processes to create a new real-time clearing system. Not only does this system flag defaults, but it also can issue a line of credit to a customer proactively, creating value for the customer and an entirely new revenue stream for the bank.
- Another bank identified compliance and governance as a critical area for enhancement to reduce its risk profile. By codifying the compliance processes, the bank has created an automated system that picks up vital information from the flow of real-time trading to validate that transactions are fully compliant. It automatically tags, catalogs and archives information in governance and compliance databases to be available on demand.
- AI/ML and robotic process automation make it possible to enhance fraud detection dramatically. A global bank combined these technologies with OCR to resolve issues related to checks. Its real-time system increases protection for both customers and the institution by scanning checks, processing data and verifying signatures while continuously building on its ability to identify and flag counterfeits and anomalies.
Naturally, these are the kinds of opportunities needed today, not just at the end of a multiyear deployment. The following four-pillar strategy makes it possible to reshape your business progressively, enhancing existing processes even as more extensive capabilities become available to you over time.
Pillar 1: Implement Basic Forms of Automation on Business Processes
The foundation of intelligent automation is having the discipline to expand basic automation as widely as possible. The foundation is a mandate at the line-of-business level to reduce or eliminate manual processes wherever they can be performed as well, or better, by machine. The industry knows about such techniques; it just has to put them to work. Customer onboarding, portfolio management and many other labor-intensive activities are ripe for such business process automation. It should not take four days of people opening and closing multiple applications, filling out numerous forms in different systems, and manual back-end due diligence by people to complete the opening of a bank account. These kinds of information-driven tasks are by default the kinds of things that machines are better and faster at completing.
Pillar 2: Expand Basic Automation With Robotic Process Automation (RPA)
RPA has two kinds of impact. It can fill gaps in basic automation that current techniques cannot address. It can also sit on top of existing forms of basic automation and knit processes together in robust ways at a higher level. This type of automation addresses processes of much broader scope that have lots of complexity and exceptions. By using RPA, you can push automation both deeper and wider.
Pillar 3: Apply AI/ML To Process Information at Scale
Almost all forms of automation rely on expanding the data foundation. Automation increases access to the metadata describing processes and to transactional data. AI and ML algorithm capabilities can make sense of this much broader landscape. AI and ML can surface new patterns that point to more automation and detect anomalies that get in the way and require special handling. Applying AI and ML to automation results in better automation and a deeper understanding of the business.
Pillar 4: Integration: the Last Mile of Automation
To maximize impact, all of the automation and insights gathered so far in the automation stack must be, in essence, productized and adapted to support high-volume processes. The last mile of automation brings everything together and supports the actions of a broad set of staff in a way that makes it easy to do a better job. Part of this is automation, but it also means creating advanced environments to recommend actions and to make it easy to process exceptions. Such automation is applicable across the entire business for revenue-producing operations, compliance and governance activities, including regulatory reporting. In this stage, just as with AI and ML, the focus is on automation, data and visualization.
Avoid Traps by Changing Perspective
Automation represents both opportunity and challenge for financial services. Some of the potentials seem obvious. The talk of making processes faster, more efficient and less costly is as old as business itself. That line of reasoning is also a trap. The tendency has been to think about automation in a contained and sharply focused way, as applied in a discrete, narrow context. Automation should be viewed much more broadly as part of the foundation of the financial services industry.
Intelligent automation is that bigger notion. Instead of devoting resources to individual processes, the proposal is to apply automation at multiple business stack levels. The objective is to make the entire business better, not simply to smooth out a single process.
These four interlocking pillars make it possible to envision business-wide automation that solves top-level business challenges for financial services companies. Instead of limiting automation to narrow tasks, taking a broad approach capitalizes on places where lines of business overlap. It is no longer necessary to replicate processes multiple times to serve different parts of the business. Integrating applications with underlying AI-powered data pipelines enables various lines of business to benefit in novel ways from new resources that were previously out of reach. Moreover, the use and data exhaust from such a system lead to the organic growth of new applications and opportunities across the financial services industry.
Inderjeet Rana is CTO of Financial Services at Hitachi Vantara.