Financial recovery post-pandemic: how technology can help banks to help their customers – Global Banking And Finance Review


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By Lexie Bryson, Digital Transformation Director at Ciklum

Financial recovery post-pandemic: how technology can help banks to help their customers 2

Lexie Bryson, Digital Transformation Director at Ciklum

The Bank of England reported in October that UK banks are resilient enough to continue supporting households through the post-pandemic recovery. They are thus in a good place to help those customers in financial difficulty (in particular, the one in ten UK adults who anticipate having to borrow money to cover essential costs, and the 1.6 million people who were still on furlough by mid-August). 

Banks will be key to the financial recovery of the households and businesses rocked by Covid-19. But can they carry the weight of the public’s financial worries?

The importance of investing in tech-driven solutions

If banks are to withstand the demands made on them by customers in times of financial challenges, it is vital that they invest in tech driven solutions now. Technology can address the key areas of customer experience, data optimisation, costs to serve, and security, risk and compliance. 

A strong technological footing will enable banking providers to focus on their human response to customers, all whilst unlocking incredible value for the business. Think of it as this – tech can support banks, so that banks can support the public.

Record breaking investment in UK fintechs of almost £18bn in August this year should also ring alarm bells for traditional banking providers. But the latter do have distinct advantages over fintechs, which put them in a position of real power- reputation, credibility, trust, and a lot more financial security. Their foundation is strong, and simply needs to be built upon. 

Frictionless convenience, immediacy and intimacy without contravening perceived privacy boundaries are now everyday demands for the modern consumer. Exploiting AI and associated technologies will be the difference between success and failure in the coming years. According to McKinsey, AI can potentially unlock $1 trillion of incremental value for banks – but what should they be building, and how can it help banks to become more human in their approach to customers?

Data and analytics (D&A) will provide the all-important personal touch

Banks represent some of the organisations with the most data – both internal and external – and yet many traditional banking businesses are struggling to fully leverage it. 

Data and analytics can help banks to make swifter lending decisions, set more accurate risk exposure limits for customers, tailor offers to individuals, and more accurately market specific products to particular customers. 

For those customers requiring loan products, payment holidays and credit cards, data can help banks to better understand customers and their preferences and habits. This information can then be used to build better, more intelligent methods of serving and retaining that particular cohort – in a way that also protects the banks themselves from great risk. 

In order to achieve this, banks must have a smart data and analytics strategy that scales and spans across the business. They should strive towards mass personalisation and a single view of the customer across all products – including lending, savings and insurance. 

AI, Machine learning and Robotic Process Automation (RPA) can efficiently process customer demands

Since the start of the pandemic, millions of people in the UK have been granted payment holidays (which incidentally, end later this month). In addition, almost nine million people have been on furlough, and more than 800,000 people have lost their jobs.

With branches closed and life confined to the four walls of our homes, it’s easy to imagine that customer service teams for big banks would have found themselves inundated with queries and requests from worried customers during the height of the pandemic.

AI, RPA and machine learning are revolutionising finance – and chatbots in particular are a great example of how machine learning can be used to increase productivity and improve customer experience by automating 24/7 customer support. The application of these technologies can also improve retention, increase product sales and drive top line growth. 

With chatbots, customers can get quick answers to their queries. Machine learning can be trained to identify which of those queries can be swiftly solved with RPA, and which require support from an employee – that all important human touch. 

In order to truly enhance the customer experience, banks need much more than a standard, rules based chatbot. Conversational AI harnesses Natural language processing (NLP) and ML to understand the intent of customers, predict what they might want, and even predict and determine the mood of a customer. 

Robotic process automation (RPA) can also be used to assist with processing repetitive tasks such as customer onboarding and account opening. RPA makes the process easier and more straightforward – enabling customers to access much-needed financial services quickly, and saving banks time in what is traditionally a long, drawn-out process. 

Predictive algorithms can inform and improve the customer experience

Historical data can be used to predict future events and trends – benefiting both the bank and the customer. For example, predictive analytics helps banks to identify and segregate ‘risky’ customers from risk-free customers – and target specific, affordable products appropriately. 

Algorithms can also improve the customer experience by offering budgeting support – helping people to avoid late overdraft payments and other penalties. For those customers experiencing financial worries, this is an incredibly valuable part of the customer service experience, and would help embed trust in the banking provider.

Customised loans can help banks to make the best decision for the customer

One of the toughest challenges for banks lies in predicting the level of debt that is affordable for each customer. AI systems trained on credit decision data can help banks to make the best decisions for both consumers and the business – helping to prevent customers from taking on unaffordable loan products.  

But AI is only as good as the data that feeds it. AI systems must be vigorously tested, as, like humans, the algorithms that determine credit decisions can be subject to bias. This bias can have a negative material (or even harmful) impact on people. 

The right training and testing is vital, as AI systems can be fed on biased credit decision data that ultimately prevents groups of customers from accessing certain financial products. It is therefore vital to mitigate bias in AI systems to build trust between humans and machines. 

Other tech solutions worthy of mentioning here also, include moving operations to the cloud in order to reduce cost; blockchain; and virtual and augmented reality (such as a virtual digital bank branches and digital wallets). 

With banks playing a crucial role in the financial recovery of both households and businesses post-pandemic, reinventing them for the future with optimal infusion of new-age technology is a prime necessity. Tech, ultimately, can help banks to help their customers. 

However, this should not come at the cost of the all-important human touch. Tech should act as an enabler, promoting meaningful relationships and interactions rather than replacing them. The option to speak to a human should always remain – particularly given the reality of financial vulnerability across society. By striking a balance between an empathetic emotional approach and technology-led innovation, banks can deliver a sustainable and profitable customer experience that makes lives better. 

In conclusion, as we move on to the next post, may I add that camDown and I know your friends would feel the same.