Conventionally, banks and other financial institutions start their automation journeys with robotic process automation. While certainly beneficial, RPA has proven to be limited in its automation capacity. Essentially, RPA is only applicable to tasks that
don’t require any cognitive input.
Intelligent process automation, on the other hand, is capable of processing massive amounts of data, learning as it goes, and interpreting anomalies. In a nutshell, IPA is a combination of RPA and AI. Although a considerable number of financial tasks is
highly-repetitive, banks are now augmenting both robots and human workers with AI to widen the scope of tasks that can be automated, driving significantly more value.
The shift from rule-based to intelligent automation can be largely attributed to the introduction of cloud-based data storages. This opportunity to store massive amounts of data opens up new horizons for driving organization-wide efficiency by processing these
datasets in real time. On top of that, the exponential increase in computer processing power coupled with advanced ML algorithms have also made IPA a much more accessible solution. Here are the most potent use cases of intelligent automation in banking.
The ever-increasing customer demand for omnichannel payment support and rising expectations for lightning-fast query resolution calls for finding new ways of customer service provision.
In recent years, conversational AI has proven to be a perfect solution to tackle this challenge. With AI-powered chatbots and cognitive assistants, customers can now receive financial guidance and resolve common issues without banks’ employees ever needing
to directly contact them. These self-service tools not only improve customer experience but also cut operational costs and save employees’ time to be spent on more value-adding tasks.
In addition to dealing with thousands of financial operations on a daily basis, banks also need to keep their ears to the ground regulation-wise. Regulations are frequently changed, making compliance teams skim through an avalanche of documents just to stay
updated on the latest iterations of these rules.
IPA tools can help financial institutions augment compliance professionals’ skills, enabling increased efficiency and decision-making accuracy. For example, NLP-powered tools can help banks generate Suspicious Activity Reports (SARs), which are often requested
by regulatory institutions.
Interestingly enough, according to
Emerj, AI companies offering compliance services received the second-highest total funding in 2019. This shouldn’t be surprising as banks were always on the lookout for a solution that could decrease costs associated with compliance.
Overall, data manipulation is at the core of many banking processes. IPA solutions can effectively transform both structured and unstructured data into useful information.
Optical character recognition (OCR) software coupled with NLP-based systems enables organizations to intelligently automate data capture, extraction, and validation. This way, for example, IPA can simplify the transition from a backlog of physical documents
to a digital database, effectively starting a bank’s digital transformation. With the help of an IPA system, all the incoming data can be automatically labeled, processed, and stored in relevant databases, preparing it for ML training or data analysis.
For example, an NLP-powered intelligent automation system can extract all relevant data from loan applications and enter this data into the account opening system, significantly decreasing the time it takes to complete the commercial lending lifecycle. IPA
can also automate tedious, time-consuming processes like account closure, cancelling of direct debits and standing orders, and transferring of interest charges.
Getting IPA implementation right
Companies should approach IPA as a tool for enhancing operational efficiency and helping employees in their decision-making, rather than a solely cost-cutting opportunity.
Yes, one of IPA’s main advantages is that it can decrease operational costs tenfold, but this should be considered as a side effect, not a fundamental reason for implementation. When C-suite treats IPA solely as an opportunity to shrink budgets, they will most
likely select the wrong use cases. From there, IPA implementation efforts are usually destined to fail.
At this point, selecting the most appropriate use case becomes one of the decisive factors of IPA success. As IPA is often meant to take an active role in decision-making support, the reliability of those decisions depend on the quality of source data. This
is why it’s critical to assess a company’s data availability and quality for a specific area of IPA implementation.
In addition, the best candidates for process automation are often unstable in demand. Digital robots can quickly scale to meet this demand, whereas it’s much more problematic to adjust workforce capabilities on the fly.
In a nutshell, it’s crucial to apply IPA to tasks where it’s possible to drive the most value rather than tasks that can bring ROI faster. Robots provide the most value when they perform tasks for which humans are too slow to react in time or which are too
repetitive and time-consuming.
While the use of AI-driven automation is becoming widespread, there is considerable room for improvement. The predictive powers of IPA solutions are yet to realize their full potential. However, as organizations are learning to trust these algorithms more,
IPA will become an essential part of business operations.
Although IPA can be considered as an extended version of RPA, its impact on organizational workflows is a hundred times more significant. In most cases, financial organizations will need to reorganize their teams, retrain or upskill the workforce, and address
data governance issues to be able to maintain IPA.