AI Case Study

ML "is particularly important with customer support, where agents are aided by machine learning scripts that improve the more they process queries, rather than automating everything through chatbots.

Neal Lathia, data scientist and machine learning lead at the UK-based bank, told The Drum how his team is delivering “incremental gains” in the app, in customer and the product using smarter automation since he took the role on one year ago.

Lathia says: “When a customer writes to us 'why was this transaction declined' or 'I lost my card', we use machine learning to check what the customer has sent and generate possible responses for our customer service agent.”

The language of the written query (most complaints are text-based and are sent in the app), is scanned by the system and responses are generated. This helps speed up how requests are processed – users cannot be denied access to their funds or be sitting with the chat box open forever. It's not a good look for a disruptor brand that has to build trust and signal reliability.

Lathia believes "chatbots were very over-hyped a few years ago and still haven't found their rightful place in the world". Instead, he says, Monzo prefers to use machine learning "to help both sides talk to each other better, rather than just talking to a machine".

The programme measures the volume of queries submitted to the team, and agents tag their nature. Once a week, the team analyses the complaints and uses the patterns to fast track improvements to the service."