Banking In A Second Wave

BANKING SECOND WAVE

Waves of transformation are nothing new for banks. In the past, these waves would have occurred with the onset of new corporate personalities, new technology, or new consumer behaviours.

Now, for all of us, waves have a new meaning. Looking at a second wave of coronavirus begs the question for all of us – how will life change again? And how will this impact banking, an industry which has already seen drastic transformation in the last six months?

The financial landscape has made huge shifts. Banking has seen trends in digital transformation accelerated, while bosses in the industry have an eye like never before for cutting costs and ensuring economic stability.

At a time when the future is unclear for many of us, banking will need to stay agile in adapting to the needs of customers as we face the prospect of a second wave. So, what has the last six months taught us? And, how can banks prepare for the this second wave?

The year of the QR code

The pandemic has forced organisations to accelerate digital transformations that impact how, when and where customers spend. This in turn has had a huge impact on banks as more and more customers turn to QR scanning, Apple wallet paying, or simply keeping their custom online-only.

What’s more, with banking advertising spend focusing on contactless services, a conscious move away from cash has further changed the way we interact with our banks. Already an expensive and outdated way to pay for goods and services, cash now holds a new health risk and a new aversion has accelerated a cashless trend that was coming to the fore for some time.

Navigating a need for fast, effective payment services that keep customers safe, earlier this year the British Retail Consortium was quick to announce an increase to the contactless payment threshold, from £30 to £45, which has led to a boom in higher value contactless payments that sees no signs of decreasing.

Embracing the QR code and going cashless has created a much more frictionless customer experience that is safer and easier for consumers and cheaper for merchants.

This impacts the banks, too. These changes in behaviour and the digital transformation of spending means banks and brands can collect and analyse spending data in a way that was not possible when spending with cash. That data can help brands deliver better services for customers that are tailored to specific spending habits, benefitting the customer, who receives offers they want and use, as well as the banks.

Looking after loyalty

It’s clear that banks have been adapting to the evolving needs of customers as the pandemic continues. With digital transformation already changing the relationships customers and merchants have with their banking providers, personal engagement has been top priority for banks aiming to weather the changes to our economy and our spending.

With the country set to head into the deepest recession in a century, it will be incumbent on banks to assess how they can continue to best serve customers. One way to do this could be to embrace loyalty schemes that are tailored to customers’ spending habits, serving them with rewards that are both practical for saving money and give added engagement and loyalty benefits to the brand.

As the world of retail and hospitality remains tentatively open, such loyalty schemes are a good option for those customers who want (and need) to start spending without hefty price tags. In this environment, it will be up to banks to ensure that their customers stay put.

Riding the second wave

The first wave of coronavirus transformation had consumer engagement at its core, and this trend is not likely to go anywhere as we face the second stage of this virus.

Adapting to the needs of consumers first fundamentally changed the way that many banks behave. Banks leant into improving online experiences, turbo-charging their online offers and adapting the services they had in-store to ensure both staff and customers’ safety was put first.

A port in the storm?

What faces us with a potential second wave will be determined by how the economy reacts. As consumers and businesses continue to deal with financial hardship, there’s no doubt that banks will be the first port of call for those who are experiencing financial distress.

Banks need to consider how they can continue to support those who may need extra at this difficult time – and openness to collaboration with wider players will be key. Keeping an open mind to offers and programmes that can be utilised for the benefit of customers and businesses alike will pave the way for continued trust, loyalty and belief in our banking system in these uncertain times.

Campbell Shaw, Head of Bank Partnerships, Cardlytics.

FINANCIAL INSTITUTIONS AI

Artificial intelligence (AI) is no longer an implausible, futuristic vision but rather a stark reality that is disrupting businesses worldwide. AI has been redefining industries and changing the ways businesses function for some time now and the banking and financial services sector is no exception. In fact, it is regularly positioned as an essential investment to stay ahead of the competition, provide greater customer service to customers, deliver more relevant services and offerings, as well as helping transform many back-end processes.

Its potential use cases have increased further as we see more bank branches than ever having to close due to the impact of the coronavirus pandemic. With consumers growing increasingly dependent on digital banking services, the need to invest in AI to solve resulting challenges has accelerated, whether that’s to provide tailored offerings or speed up the remote onboarding process.

While AI and machine learning algorithms are often seen as a way of speeding up service delivery and helping to offer a more personalised experience, we have seen its application come under scrutiny in other industries. You only need to look at the UK’s A-Level exam grading incident that dominated headlines in August. Come results day, students from certain communities were disproportionately and negatively impacted, while other students saw their results inflated. This all came as a result of the algorithm implemented by Ofqual, which was built to trove through historical data about students’ course work and predicted grades, as well as the data about the actual grade obtained at exams in previous years.

This raises the question as to what would happen if the algorithm used in this instance was applied to a financial decision. The same biases could negatively impact the way millions of consumers and businesses borrow, save and manage their money. Therefore, we must focus on the lessons that need to be learned to prevent similar scenarios suffering the same mistakes.

AI undoubtedly offers huge opportunities for banks to enhance their services, but if they plan to utilise this technology, it is vital that they learn from the Ofqual scenario.

AI as a tool, not an algorithmic saviour

While it is undeniable that AI has opened up a wealth of promising opportunities, it has also led to the emergence of a mindset that can be best described as “AI solutionism” which is the philosophy that, given enough data, machine learning algorithms can solve all of humanity’s problems.

We need to be aware of the limitations of AI and learn to set reasonable expectations with it so not to paint an unrealistic picture of its power. Instead, we must take a step back and separate the actual technological capabilities of AI from magic and remind ourselves that AI, as a tool, is not a solution for everything.

AI can be highly effective when it is given a specific focus and a question to answer. For this reason, if businesses are going to start implementing AI, they will need to know the exact problem they are trying to solve. By identifying the question you want to solve from the start, you will be able to select the best suited options, including AI ones, and come back to this initial goal time and time again throughout the project to ensure it still aligns.

Reducing bias through data

AI systems are built on sets of algorithms that “learn” by reviewing large datasets to identify patterns, on which they are able to make decisions. In essence, they are only as good as the data they feed on. Therefore, without strong and relevant data

underpinning an AI model, it will never be able to produce strong and relevant results. When it comes to designing a fair algorithm, the key is to collect a sufficient amount of data so that the algorithm can be trained to represent an entire community.

While it is possible to buy datasets to speed up the process, when doing so, it is essential that the data you buy meets the criteria you require rather than simply being a large data set. For the financial services sector, this enables employees to treat customers fairly and, when combined with appropriate modelling and processes, allows them to maintain transparency and accountability in their decision-making processes to avoid legal claims or fines from regulators which can cause deep reputational damage.

You also cannot completely rely on the AI. A human eye is still needed to understand how it is working and to continue to improve it through constant monitoring, training and tuning. Companies must be careful that they don’t set up an AI model and assume the problem is solved. It will require attention so that it becomes increasingly accurate and continues to answer the question posed, even if the real-world scenario changes over time.

Walk before you run

As the Ofqual issue revealed, practice is a vital step to ensuring the algorithm works as expected before putting it into a real-world scenario. By running algorithms through a pilot testing phase, companies can assess feasibility, duration, costs and adverse events, and better understand why an algorithm is making a certain decision. As this was not sufficiently done in the Ofqual case, it simply didn’t provide the right answer to the problem it was trying to solve.

Building ethical AI

If we are to reap the benefits of AI, we must first minimise the potential harms of algorithms by thinking about how machine learning can be meaningfully applied. This means we need to have a discussion about AI ethics and the distrust that many people have toward machine learning. Here are some key areas that businesses should consider when putting AI into place:

• Usage consent: make sure that all the data you are using has been acquired with the proper consent

• Diversity and representativity: AI practitioners should consider how diverse their programming teams are and whether or not they undertake relevant anti-bias and discrimination training. This will draw upon perspectives of individuals from different genders, backgrounds and faiths, which will increase the likelihood that decisions made on purchasing and operating AI solutions are inclusive and not biased

• Transparency and trust building: accurate and robust record keeping is important to assure that those impacted by it know how the model works

In the financial services industry, there are many ways that AI can be leveraged. This is increasingly the case in the document-centric identity proofing space whereby an identification document, such as a passport, is matched with a selfie of the user to confirm real and virtual identities. This will be an essential area of focus for financial services companies as they look to confirm that users are who they claim to be when the physical branch is diminishing. When analysing if a person is the same as the picture on their documentation, for example, a biased AI model can completely undermine the decision made. Thankfully, organisations are growing more keenly aware that demographic bias in the performance of identity-proofing processes could reflect negatively on their brand, in addition to raising possible legal issues, according to the 2020 Gartner Market Guide for Identity Proofing & Affirmation.

The Gartner Market Guide predicts that by 2022 more than 95% of RFPs in this space will contain clear requirements around minimising demographic bias. As such, there is a real opportunity to leverage AI solutions to provide the best service when it comes to this function, but financial institutions must ensure that they are doing so in an ethical and accurate way by focusing on these key areas discussed.