Going from RPA to Cognitive Solutions in Banking

Going from RPA to Cognitive Solutions in Banking

Snapshot Insights

Snapshot Insights,

Most banks Cognizant works with today, are still maturing in their use of task-oriented automation with a single automation tool. A few banks, however, are further along the maturity curve – and they’re moving toward enterprise-wide automation using cognitive technologies.

Automation solutions are typically applied to routine, repetitive, human-assisted tasks, and they often deliver 20% to 30% cost savings, based on our experience working with banking organizations. Quite a few of those with a satisfying experience of task-level solutions, start to consider cognitive technologies as a natural next step. 

What should you think about when moving from proof of concept-based, siloed automation to overall operations transformation? More complex end-to-end process automation applications usually require emerging technologies, such as machine vision, natural language processing (NLP), ML, adaptive alteration technologies and chatbots, among many others. No single product vendor currently provides all of these in one platform.

Therefore, before jumping too quickly into these technologies, it makes sense to explore the complexities associated with them, set realistic expectations about ROI and understand how automation tool vendors are charting their specific product roadmaps. Among the things to consider are these.

  • ROI for complex, end-to-end process automation may not be as quick as for task-level automations. Since there is no single vendor for all cognitive solutions that may be used, banks will need to invest carefully in multiple tools. 
  • End-to-end process automation can also take months to implement because of the time required to justify the business case, identify and onboard new tools, and hire skilled staff. Cognitive automation tools often require highly skilled resources and data scientists. 
  • Cognitive solutions need massive transaction volumes and enough time for the machine to learn so it can provide meaningful insights, which adds to the project’s timeline. 
  • Licensing costs for cognitive automation tools are several times that of task-level automation tools.

All of this combines to make the initial investment higher and break-even period longer. What to do then? We suggest that businesses start with carefully evaluating the feature roadmaps of various product vendors to assess alignment with their own automation journey and the degree to which they are truly open from an architectural standpoint to enable integration with other tools.

As banks move along the automation maturity curve, many opportunities for process improvement, cost savings and data-driven insights await them. If you want to learn more about automation in banking, please also read RPA in Banking: Here Is Where It’s Most BeneficialRPA in Banking: 7 Pitfalls to Avoid in Your Strategy, and RPA in Banking: The Different Adoption Approaches.