65% of staff time was spent servicing information requests, 39% communicating via email and chat, and 26% managing data.
The capital markets division of a major Swiss bank discovered that its operations function, far from being a procedural back office environment with well-defined tasks, was complex and inefficient. Processing over 150 million emails a year, 65% of its employees’ time was spent servicing information requests, 39% communicating via email and chat, and 26% collecting and manipulating data.
At least two-thirds of staff time was spent in a parallel world outside core IT systems, running on email and Excel. This created enormous amounts of excess work and generated vast communications complexity beyond anything an RPA solution could solve.
The capital markets operations team knew it first needed to understand its workflows to automate inputs and initial processes. Most importantly, it had to perform process discovery on the 70% of daily work that took place unmonitored outside of core IT systems. It would then be able to feed the bank’s RPA engine and other applications with labeled, structured data for the purposes of automation.
The bank adopted the Re:infer Conversational Data Intelligence platform, to interpret raw communications data and apply natural language processing to identify relevant data clusters. Staff reviewed these clustered conversations and taught the model how to interpret them.
The models were then connected to multiple inbound communications channels – including email, chat application programming interface, call transcripts, customer relationship management systems – to extract recurring themes and process insights through Communications Mining. The platform was also set up to deliver structured data to downstream users and systems, including the bank’s connected-RPA enterprise platform.
Originally developed and deployed only within the capital markets operations function, the platform has since been scaled to a full enterprise solution that has also driven improvements in the bank’s wealth management arm.
By reducing communication complexity, the combination of Re:infer and RPA enabled the bank to overcome a seemingly impossible roadblock. With Conversational Data Intelligence, the bank has been able to deliver a programme of continuous process improvement, in addition to greatly improved customer service.
Improved management information through Re:infer has driven additional operational gains through root cause analysis of processing errors, and the detection of information security risks. The platform also detects failing trades in specific assets from email conversations, further preventing operational risk.
- Huge operational inefficiencies, bottlenecks and risks identified, resulting in many millions of dollars in cost savings
- 40% of all transactional customer requests automated, enabling resolution in minutes rather than days