Technology provides a lending hand to the banking industry
There isn’t an industry untouched by digital transformation.
Whether it’s the healthcare industry using machine learning to detect early signs of cancer, or the construction industry using smart technology to keep abreast of building maintenance, data has influenced entirely new ways of working for almost all organisations.
Banking is no different. Huge strides have been made over the last few years by the sector to harness the benefits of digital transformation. And yet the initial adoption of nascent technologies like machine learning (ML) and artificial intelligence (AI) has been much slower compared to other industries.
Of course, there isn’t a one-size-fits-all approach to implementing a technology strategy. Every organisation has its own ways of working, challenges to overcome and objectives to achieve. Digital strategies must, therefore, be uniquely tailored to each individual business. On top of that, the higher risks involved in banking, whereby one small error could have a substantial impact on a huge number of customers, means organisations have been rightly cautious.
But over the past five years, there’s been a seismic shift in the sector’s approach to the use of technology. The inception of Monzo and Revolut in 2015 showed what could be achieved in banking by taking a digital-first approach, and it wasn’t too long until the larger banks started to look at how their own organisations could benefit.
Indeed, in our own research where we spoke to senior IT decision-makers in banking, we found that most financial services firms are now using ML in areas like compliance and to improve customer experiences. While progress has started to be made, our research also uncovered important areas where the industry has yet to tap into.
Data, data and more data
The banking industry has an unrivalled amount of customer data. Very few sectors have as much regular dialogue with customers and as many touchpoints – from branches to online. This presents a huge opportunity for the industry.
Currently, however, analysis of much of the industry’s customer data isn’t consistent. For example, the sector does analyse its structured data, which includes things like names, addresses and credit card numbers. Using this insight, banks have been able to identify and block fraudulent activity in real-time, as well as a host of other benefits.
However, our research found that only three per cent of financial services firms are harnessing the potential of their unstructured data. Unstructured data includes data stored as audio, video and email files, and it accounts for around 80 per cent of the data that financial services firms hold.
Analysing unstructured data with ML could help banks uncover important patterns in customer liaison and react to potential problems proactively. For example, it can indicate that a customer may be on the brink of switching their current account, or identify when a customer may be about to default on debt payments. Analysing both unstructured and structured data allowing banks to mitigate those risks and take the necessary precautions to help the customer.
This approach ultimately unlocks a treasure trove of insight for the sector. And it’s this that could pave the way for entirely new ways of engaging and building meaningful, long-lasting relationships with customers.
Leaving behind a legacy
The potential that technologies like these offer the banking industry is huge, and it’s no wonder other sectors have been quick to embrace digital transformation across their organisations. The biggest barrier for the banking industry, however, is overcoming its legacy IT infrastructure problems – often switching from siloed infrastructure to cloud systems that are secure and allow data to be collected and analysed effectively.
For many of the high street banks still working with decades-old IT systems, the clear benefits of using technologies like AI and ML to improve customer relationships could provide the justification for IT investment. And as challenger banks continue to set the pace for innovation, it will be vital that traditional banks keep up if they are to remain a market leader.
Overcoming these barriers won’t happen overnight. Banks wanting to make the most of the data at their disposal have some work ahead if they’re to truly undertake a digital transformation. But, by looking at how other sectors – and competitor challenger banks – have embraced new technologies, the evidence is clearly there for banks to take the next step in their digital journey.
This article was contributed by Ryan Stewart, Financial Services Lead, Cloud Technology Solutions
AI and the future of global trade
Artificial intelligence (AI) is becoming entrenched in our daily lives, but the technology is still surrounded by misconceptions and skepticism. Ask the public and they may jump to dystopian scenarios where robots have taken over the world.
While this makes for a good sci-fi blockbuster plot, the reality is different and more benign. Those products that Amazon suggested you buy? AI. That TV series you were recommended to watch on Netflix? AI. That self-driving Tesla car you crave to take for a spin? You guessed it: AI.
There is no single industry that is not being re-shaped by technology. Until recently, however, there was one noteworthy exception: global trade. Fortunately, that is slowly changing.
The mechanism that underpins global trade – trade finance – is an industry that remains largely paper-based and reliant on manual processes. This US$18tn a year industry is now being influenced by a new wave of technological innovation, including AI.
Exploring the potential of AI in Trade Finance
AI refers to the use of computer-aided systems to help people make decisions or make decisions for them. It relies on large volumes of data and models to make sense of information and draw intelligence.
In trade finance, AI is helpful in analysing quantitative data, and the repetitive nature of trade finance means that there is a lot of non-traditional data at our disposal.
This means that when trade finance providers need to assess the risks of funding a transaction, AI models can be a very efficient tool for data analysis and reveal intelligence and risks relating to small companies.
AI helps the industry move beyond traditional credit scoring processes, which are often outdated and remain reliant on historical accounting entries – a barrier that prevents small companies from accessing trade finance and has resulted in a $1.5tn global shortfall.
Overcoming the barriers
AI can tackle this shortfall by creating accurate credit scoring models. This can include a company’s payment history, measure the risks of funding a transaction, identify supply chain risks, and benchmark them against their peer group.
Trade finance providers can use this information to communicate effectively with their SME clients, ultimately helping establish better business relationships.
Towards a technological utopia?
The adoption of AI has the potential to do a lot of good in the industry, and the industry is in the early stages of radical transformation.
Advances are driven by fintechs as well as a willingness to change. The industry is working together to create new infrastructure for distributing trade finance assets to other investors in a transparent, standardised format.
The creation of infrastructure is possible due to improvements in technology and integrated across the trade ecosystem in cooperation with banks, insurers, and other industry participants.
It’s collaboration at its best: together, the industry is using technology to re-shape global trade as we know it.