As financial services were one of the first to embrace Robotic Process Automation (RPA), they were also among the first to encounter its roadblocks and limitations. While RPA excels at handling repetitive work, it doesn’t deal well with variance, exceptions, and unstructured data.
With an influx of disruptive digital natives chipping away valuable customer segments, smartening up operations is a matter of survival for the traditional financial institution. Adopting Intelligent Process Automation (IPA) is a natural step forward that helps to automate more and with better efficiency.
To win, traditional institutions must go forward with higher-impact, customer-centric interactions and leave technology to do the rest. But how?
Two key elements bring automation journeys to the next level:
- Strategizing and targeting the right areas to create a real impact
- Incorporating artificial intelligence (AI) into their RPA program
Strategize and automate the right processes to create real impact
Before incorporating AI into your RPA program, you need a strategy and a clear purpose for your automation program. Any automation solution, no matter how intelligent, will fail to achieve the desired results without the right plan; one that is both clearly defined and agile.
A good starting point to target automation opportunities is to pinpoint labor-intensive areas that are draining resources, costs, and business hours. Yet the single most important ingredient for impact is to approach and execute your automation strategy holistically.
A frequent implementation problem we see within the finance sector is siloed RPA projects scattered across a range of disconnected processes and departments. These isolated implementations can deliver short-term cost reductions but often fail to move the needle on a larger scale. Additionally, siloed RPA implementations can be more difficult to scale at a later stage.
Another common issue we see within the industry is that some processes are not a fit for automation but end up getting automated regardless. Assessing and adapting existing processes through a lens of automation will determine the success and outcome of a program.
Weave in layers of AI and cognitive technology into your RPA program
Until now, financial services have used RPA with the main intention to drive productivity and optimize operations. Yet, while RPA is indeed an optimization enabler, its ultimate advantage is its data independence. This allows it to work in parallel with machine learning and AI, as well as human operations, creating opportunities far beyond employee productivity.
Embedding AI into your RPA program makes it more agile and intelligent. IPA can handle many exceptions, adapt in real-time and continuously learn. While a robot can extract data, such as totals of payments, and send them to a human for verification, IPA can take the next step by detecting developments and irregularities when changes occur, and with that, tightening security – for instance, by detecting fraud.
Practical example: Corporate pension department
We saw the impact of this kind of approach when addressing a set of challenges a client was experiencing in processing pensions. Every month, the corporate pensions department received updated pension contributions for its customers from different companies. Although encouraged to follow the same template, companies sent unedited output in the form of spreadsheets from various HR and accounting systems, leaving it up to our client’s employees to verify and untangle the output manually. This manual intervention was both time-consuming and prone to errors.
We needed to simplify the entire process. We started by designing an intelligent solution that looked for emails with relevant attachments in the corporate inbox. When a relevant document was identified, the processing began, which entailed matching the input with required database formats. When the smart algorithm didn’t find an exact match, it interpreted the column names and compared them to a self-learned known formats database.
Our solution has streamlined accuracy and continues to deliver a significant number of hours back to the bank every month, enabling employees to spend time on higher-value customer interactions.
Practical example: International bank transfers
Another common process issue in banking is data falling outside the defined fields of incoming international payments. Different currencies, sort codes, and other variables between source and target banks are often misaligned, making it difficult to standardize certain actions and process the payments.
For one of our clients – a major international bank – this issue required a dedicated team of 12 to repair and complete transfers. With nearly 20.000 incoming mismatched cases per day, a downpour of negative outcomes was inevitable. So much could go wrong in the process of transferring data, and for employees? Well, the same stubborn, tedious task awaited them every morning.
To address this challenge, we helped our clients design a solution that reduced the manual work by over 90%. First, we created automated workflows to assess the transactions that were failing to go through. The process ran over 60 checks such as identifying payment types or missing information, running blacklist checks, and establishing the need for the transaction, conversion, and other fees. When errors or misplaced fields were identified, our solution repaired the transaction, and passed it on for final review, authorization and processing.
To survive – and thrive – financial services must continue to transform and improve experiences so they can focus on what is key: their customers. This means connecting the right strategy and implementation to level up their RPA program and drive true transformative value. Getting this combination right is key to ensure growth and stay competitive.