By Luis Huerta, Vice President and Intelligent Automation Practice Head, Europe at Firstsource
Over the last year, the Bounce Back Loan Scheme (BBLS) has been a lifeline for small and medium-sized businesses (SMBs). In fact, by the end of March 2021, over one and a half million SMBs had borrowed a total of £46.5 billion through the scheme. The demand for emergency liquidity through BBLS has left lenders with a huge influx of new customers to service. Navigating these relationships will require a more personal touch.
Even as lockdowns lift, the pandemic is expected to have a long-lasting effect on the UK economy with SMBs arguably hit the hardest. Accordingly, implementing a more considered and empathetic approach to BBLS servicing is paramount to maintaining healthy customer relationships. Using technology to understand, predict and adapt to customer needs will be central to this.
Using AI to make space for empathetic, human interaction
As BBLS providers move into loan repayments they will have sensitive conversations with SMB customers who are reopening their businesses. These interactions will be longer and much more sensitive than typical interactions. To provide the time and headspace for these, lenders are automating most other routine, transactional contacts, and transactions. They are doing so by leveraging technological solutions like robotic process automation (RPA) and artificial intelligence (AI). For example, conversational AI is now much better at processing and delivering natural human language, making it suitable for follow-ups on standard queries and simple transaction execution. Using AI in this way means lenders give time back to agents to support customers with more complex needs that require a human touch.
Deploying analytics to enhance customer communications
To optimise customer interactions, lenders should tailor communications to reflect SMBs’ needs and preferences. AI and advanced analytics can play an instrumental role here. Applying data insights to ascertain age, location, and interaction patterns, can help to identify contact preferences for each customer. This means personalised communication can be delivered in the best way, at the best time, and via the preferred channel. For example, analytics could predict that some SMB owners are more likely to respond to interaction initiated over traditional voice calls in the morning. Analytics can also identify customers who find calls intrusive and would prefer to engage via digital channels such as web-portals, emails, text messages or mobile apps. These insights empower lenders to deliver tailored engagement to match customer preferences.
Customer services capabilities can be further supported by Machine-learning (ML). Regression algorithms can analyse historic data to predict ebbs and flows in customer call and chat volumes with a high degree of accuracy which allows organisations to align capacity more effectively. Lenders can better predict resourcing needs and manage staffing levels to meet service needs.
Harnessing automation to streamline servicing
Customer service agents can be empowered to focus on more value-adding activities through back-office automation. For instance, RPA can seamlessly integrate data from various sources while operators handle customer enquiries. With bots handling the complexities of logging-in and navigating different applications and screens in the backend the agents are freed-up to focus on servicing queries that require human attention.
Automating repetitive, time-consuming tasks is generally more efficient and cost-effective than training additional staff to manage administrative burden. The added benefit of automation is that employees can also be trained on processes far more quickly as they have fewer screens to manage. This increases job satisfaction, not to mention improving customer services.
Embracing digital analysis to combat fraud
Beyond enabling more empathetic customer service, technological innovations – such as AI – can be used to detect and prevent fraud. Tech solutions can help lenders identify anomalies between data points, spot unusual behaviours and raise red flags much faster.
Applying this to BBLS is essential as, when these loans were first rolled out, lenders saw an increase in fraudulent activity. For example, to tackle the rising number of business accounts becoming fraud targets, one leading UK bank invested in data analysis, increased ID checks, red flags awareness training, and revised questioning to spot potential social engineering attempts. As a result, the bank was able to quickly recognise and protect potential fraud victims. In just four months this reduced fraud losses by 62 percent, while simultaneously triggering a positive elevation in quality assurance performance.
Driving customer satisfaction and potential growth
As we begin to make our way out of lockdown, we can be hopeful that a better economic future is on the horizon for SMBs. To ease their return to ‘normality’, financial institutions need to continue to practice empathy and offer guidance in their interactions with customers around BBLS repayments. To facilitate this process, lenders can use technology to facilitate strategic engagement and reduce the pressure on customer services agents. This will be essential for maximising customer satisfaction and ultimately for encouraging client growth. The good news is that those lenders who leveraged AI and automation to help them navigate the difficult times will be left with robust capabilities that will translate into competitive advantage for their businesses and be better prepared for subsequent operations disruptions.