• Imran Parekh
  • Published: 22 September 2020

The current situation with COVID-19 has resulted in unforeseen challenges within the financial industry while also creating opportunities to adopt innovative solutions. One such solution that is seeing accelerated uptake within the industry is the move towards digital automation.

Financial institutions (FIs), in particular, are mobilizing and taking steps to enhance digital customer journeys and transform risk/regulatory processes to achieve greater efficiencies while reducing costs.

Previously, investments in technology were largely made to support improvements in the front office space to drive growth, while the middle and back office (mid-back office) barely saw any enhancements to its labor-intensive, paper shuffling processes, thus creating operational inefficiencies. This has led to increased operational risk forcing firms to re-evaluate their legacy mid-back office ecosystem and look towards disruptive low-cost technologies like robotic process automation (RPA), artificial intelligence and machine learning, collectively called ‘intelligent automation (IA)’ for solutions.

IA promises to offer significant operational efficiencies by mimicking the behavior of end-users to find, evaluate, transform and enter data according to established rules. IA alleviates the need for investments into large scale transformational projects while helping reduce manual interventions.

Although firms are exploring more advanced forms of automation, such as machine learning (MI) and artificial intelligence (AI), RPA has been in use for some time now. This is especially so in horizontal functions with its scope and adoption rapidly expanding in the mid-back office space.

Bill Gates once stated: “The first rule of any technology used in a business is that automation applied to an efficient operation will magnify the efficiency. The second is that automation applied to an inefficient operation will magnify the inefficiency.

To ensure RPA is a success within the organization, financial institutions must create a comprehensive roadmap that models RPA as a strategic platform driving tactical change through four key stages: plan initiative, run pilot programs, implement robotic operating model (ROM), and scale to steady-state.

1. Plan Initiative

A comprehensive plan should first be established that lays the foundation for the initiative based on well-defined business objectives. This ensures that the goals of the roadmap are aligned with the firm’s overall strategy. This plan should include

  • Vision for enterprise automation
  • Definition of RPA governance
  • Strategy for building support within key stakeholders
  • Firm-wide implementation approach

2. Run Pilot Programs:

Once a plan is established, the initiative should move to the pilot stage. This will allow the firm to demonstrate RPA value to stakeholders, identify pitfalls and gaps within the plan and recalibrate expectations and timelines. It is necessary within this stage that firms:

  • test on a scale that requires minimal investment
  • collaborate with a trusted vendor that has prior experience and can understand the firm’s needs, walk them through the tool selection, execution process and cost of ownership audit success/failures to decide if business goals are being achieved in accordance to plan expectations and identify areas of improvement

3. Establish and test enterprise-wide ROM:

This is a critical step in the process of establishing maturity, standardizing methodologies and building a solid foundation for scaling up. The ROM should, at the minimum, include:

  • A framework that is in alignment with expected business benefits
  • An established Center of Excellence (CoE) that defines an organizational structure to best support RPA delivery, including roles and responsibilities
  • A governance pipeline to optimize process selection
  • An engagement delivery model for rapid and efficient development in a structured, controlled, and reproducible format
  • A technical architecture that can support scalability
  • A training program ensuring upskill across key RPA competencies

4. Scale to steady-state:

Retaining the ability to evolve organically should be the goal at this stage. Fostering alignment between business and technology teams through an established CoE will avoid stagnation. Supporting operational teams with tools needed to manage a mixed workforce of humans and bots and involving HR to retroactively redeploy the workforce to alleviate the anxiety that comes with this change.

Roy Amara, past president of the Institute for the Future, once said that “We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.” Like many other roadmaps before, setting the right expectations in terms of what it should achieve is critical to success. However, firms also need to acknowledge that the roadmap is no fixed silver bullet but a strong foundation that should be flexible enough to evolve and mature over time.

The trade lifecycle process within capital markets is at the period of transition, where legacy systems can no longer sustain the complexity of today’s financial markets without major investments.

Intelligent automation, especially in the post-COVID-19 work environment, has been helping financial institutions in effectively transforming capital markets through an incremental, modular approach without the massive infrastructure costs that typically come with large technology projects.

They are already demonstrating instant ROI benefits within pilot use cases for early adopters within the industry, thus helping firms meet the gap between increasing workload and reduced funding.

A version of this article was published in Banking Exchange. To read it, click here