Financial services companies are facing significant challenges including cost increases and disruptive non-traditional market entrants providing a new digital experience to customers.
Change is not easy, especially when considering the traditional silos of operations, complex legacy systems and constant regulatory changes. Most organizations have begun their journey to intelligent automation (IA) in recent years, and to ensure IA programs meet initial expectations and deliver real benefits, it is crucial the project takes the correct approach from the start.
Intelligent automation combines artificial intelligence (AI) – including natural language processing, machine learning, and decision networks – with robotic process automation (RPA). As technologies (such as voice recognition, natural language processing and machine learning) become more sophisticated, the range of business issues to which intelligent automation can be applied to is expanding. The rapid advance in intelligent automation capabilities - and at a lower cost - means that there are many potential applications for IA within financial services organizations, ranging from automation of decisions (fraud, underwriting, claims, anti-money laundering (AML), credit approvals); to automated classification and processing of documents (contract management, income verification, loan processing, regulation horizon scanning); to voice recognition (conduct screening in call centers, next best offer for relationship managers).
Get the basics right
Before starting any IA project, it’s important to get the basics right. Any intelligent automation program should begin with undertaking a thorough and realistic assessment of what you want to achieve through IA adoption, and how best to achieve it. The identification or assessment phase is a vital first step. If the right automation opportunities and the best processes for automation are not identified, you reduce the chances of maximizing the benefits.
A key stage in kicking off an IA project, is identifying the levers you want to pull through IA. Are you looking to improve the customer experience? Do you want to improve quality of process delivery – reducing errors and compliance risk? Do you want to improve efficiency and reduce costs?
Having clarified the overarching goal, the next step is to assess end-to-end processes, taking into account factors such as; the variety of applications being used, the number of handoffs and manual interventions or approvals, and the extent of decision-making required. This helps build the business case for IA including the return that could be achieved and the potential bottom line gain, after implementation costs.
The assessment also allows you to identify any processes that can be reengineered before automation – it is more efficient to automate optimized processes. An outcome-first thinking may lead you to identify, assess and optimize the automation project. It merges the design thinking with an automation toolkit to deliver transformed functions.
It’s clear that an effective assessment requires input from across the organization. The assessment will draw on the combined insights of stakeholders who understand the business and potential compliance challenges including IT, functional teams (for example: HR, finance and procurement), and well-coordinated and governed development teams who will build the “bots”.
An intelligent automation project will involve a combination of a number of tools and technologies available in the IA portfolio, so combining the strengths of stakeholders across the business will reap huge benefits.
In summary, intelligent automation can be applied to improve end-to-end processes across financial services businesses. But, before launching into an IA program, remember these golden rules:
- Be honest about your primary automation goals, so that the potential to achieve the real benefits you seek can be realized.
- Find the right tools and technologies for your project. Ensure process and design thinking is considered first.
- Encourage business and process owners to explain all steps in current processes as completely as possible – so no unexpected or hidden steps emerge later to create complications.
- Look for opportunities to reengineer bad processes before launching into the build stage.
- Check there are no competing initiatives being launched that could derail your IA program once it’s under way.
Once a thorough opportunity assessment is conducted, building an automation program can then commence.