The bank receive thousands of enquiries every day, many of which relate to regular and recurring payments such as direct debits and standing orders. Staff have access to vast amounts of information internally which they rely on to resolve these queries. The documentation is indexed in their existing knowledge management system, comprising many articles on the numerous different regular payment mechanisms that exist within the bank.
Like many such information stores, the content is very substantial and wide-ranging. Staff have to navigate these articles and on finding an article that might be relevant, must scan through the pages of detailed information to find the section relevant to the specific query they are dealing with.
The articles contain general guidance, and the employee needs to determine how best to put this in context for each individual customer. This process can be time consuming, and less than optimal for staff and customers.
Knowledge degradation is also an issue, in areas of the bank where staff turnover is higher, such as in contact centres. Expert knowledge can be lost if experienced staff leave, and the consistency of judgement can suffer as a result.
Less experienced staff tend to staff seek out ‘experts’ who then become a focal point for queries, detracting from their own day to day responsibilities.
The bank recognises the power of emerging technology, both to drive operational efficiencies but also to deliver entirely new products and services that will benefit their customers. AI is a key component of this technical evolution and Rainbird was selected for it’s potential to increase efficiency while concurrently improving the customer journey and reducing complaints.
The bank engaged Rainbird to build a tool that any employee could use to answer client questions and resolve problems with complex recurring payments. Rainbird was integrated with IBM Watson’s Natural Language Processing (NLP) front-end to deliver a chatbot that can resolve over one hundred different trajectories of enquiry. IBM Watson is used to identify the intention of the employee asking the question, and Rainbird then takes over, handling the staff consultation and cognitive reasoning process leading to a contextual solution.
Rainbird’s services arm Aigen worked closely with highly experienced members of the bank’s team. The result of this collaboration was a Rainbird Knowledge Map that incorporated all of their knowledge with the information from the internal knowledge management platform.
The resulting chatbot is available to staff who can efficiently consult with it to rapidly solve customer queries. The result includes a high-quality contextual recommendation including detailed advice on how to meet the customer’s needs and signposting to the actions required.
The chatbot is being deployed across the branch network and UK based contact centres, ensuring consistency of judgement across all areas of the business – using this powerful shared repository of knowledge.