More banks are turning to practical AI to rapidly analyse customer conversations for sentiment and emotional intent to obtain the insight and automation they have to transform their customer service and processes.
Here we look at 5 ways in which banks are using AI to process their customer feedback more effectively:
Processing incoming queries more efficiently
AI can remove the need for manual review of each incoming query and enables banks to deal with them effectively from the outset.
The analytics can facilitate a significantly smoother omni-channel experience for the customer by: identifying which channels your customers are best suited to – and which work best for specific types of interaction; comprehending the causes of channel failure and what drives customers to switch; and reducing customer effort by delivering service within the customer's preferred channel first-time.
As a current example, at one bank we were able to reduce the maximum time to respond to a customer from 3 weeks to five days. The solution used AI and machine understanding how to automatically analyse and prioritise all customer emails in near real time and routed high-priority cases to a dedicated work queue for fast action.
Automatically identifying customer intent and emotion
When differing people are voicing different issues, they'll use different words and sentiments. Vital information is often missed with traditional models and manual processes. For instance a customer at a bank might say 'by the time they called back, the financial institution was closed'. The keyword would be flagged as 'closed', when in fact the primary issue was the call back. There's also other limitations with using just keywords for example sarcasm, context, comparatives and local dialect/slang. The alternative would be to analyse text data using 'concepts' rather than 'keywords'. This can be done effectively with AI.
Fast tracking customer complaints and issues
With AI you can send complaints straight to the appropriate team for a faster resolution. We've helped banks reduce resolution time by as much as 3 days which really boosts customer retention.
Dealing with specific complaints manually involves using more and more case handlers. Routing complaints automatically and prioritising by issue and category is also difficult due to the nature of complaints i.e. unsolicited, long and sometimes multi-topical. As a result, manual classification is often impossible within an acceptable time frame for the unhappy customer.
Using the latest AI however, banks are now automatically classifying unstructured data to provide an earlier warning of issues that need resolving fastest. This may lead to better and quicker outcomes at a much lower cost.
Spotting vulnerable customers early
Under the Financial Conduct Authority (FCA) front-line staff have to be able to spot different types of vulnerability in customers and support them accordingly. However, the level of communication is just too much to hold this out manually.
The latest in AI speech transcription and text analytics has the capacity to automatically detect hints at vulnerability from conversations with customers. The conversations are automatically analysed by to detect emotionally-driven comments that indicate vulnerability like a basic lack of understanding, likelihood of a disability and circumstances. These vulnerabilities are flagged towards the relevant members of staff for action. Regulated firms can also accurately understand the drivers behind the vulnerabilities so products, services and communications can be reviewed accordingly.
Banks using AI during Co-vid 19
During Co-vid 19 a lot of lenders have customer service agents working at home and/or in strict shifts. There's been a move from voice to webchat for many to cope with these changes which brings its own challenges and opportunities. Post-C19, many of these situations are expected to stay in place or at best not revert 100% back.
AI helps to serve customers better focusing on taking cost out whilst keeping CSat up and channel switching down by improving chat optimisation, email, complaint handling and chatbot supervision.
Case study: Improving customer loyalty
A major UK bank was seeking to improve its customer loyalty. It was already using the latest
analytical tools including social listening, sentiment analysis and a large data science team
but these were experiencing limitations and not making enough progress. They were also interested to see what online feedback their main competitors were receiving.
A quantity of key recommendations for the bank were identified using AI analysis:
- A 10% increase in CSat (c. lb200m pa revenue) from operational improvement
- Comparable best-in-class churn e.g. Nationwide is 25% lower
- Online and mobile banking is really a key issue, and is causing direct churn
- Drivers of churn are mostly customer service, branch closures, marketing offers, interest rates and vulnerability issues
- Early warning can help predict churn tactically and intercept likely churners
- 28% of Tweets and potentially all non-voice queries can be automated. This could be a lb20m pa saving
- Business banking, current accounts and ancillary services have the highest churn, and insurance the greatest negative advocacy
- Mortgages, current accounts, savings and overdrafts cause the most attritional set-up
- There are distinct patterns and possibilities to adjust customer services resources to reduce churn and costs
With AI, this level of insight can be set up in a matter of days, delivered in near real time and without the need for a data scientist to keep the model.