This article was originally published in the Summer 2019 issue of The Record. Subscribe for FREE here to get the next issue delivered directly to your inbox.
Artificial intelligence (AI) holds seemingly endless possibilities for capturing and manipulating data to enhance communication with customers, create business value and boost worker productivity. But with IDC predicting that global spending on AI and cognitive systems will reach US$46 billion by 2020 – and that the financial services sector will account for a quarter of that spend – the technology’s true potential as a catalyst for digital transformation in banking is just beginning to emerge.
“Banks have seen some major changes in the past due to developments in technology,” says Monique Dahler, director of Worldwide Financial Services and head of AI at Microsoft in the report, ‘Artificial Intelligence – a key enabler for accelerating digital transformation’. “However, these could pale into insignificance when compared with the changes that are currently happening as a result of the exciting opportunities and challenges presented by the arrival of AI.”
Co-authored with Efma, a global non-profit organisation for banks and insurance companies, the report details the findings of a survey that asked senior financial executives how they are leveraging AI, what opportunities and challenges it brings them, and the impact it’s likely to have on their business.
“Developments in AI have already started to transform the financial services landscape by improving decision-making; providing better information about customer behaviour; and predicting customer needs,” says Dahler. “In its discussions with customers, particularly in relation to robotic process automation (RPA), Microsoft has found that streamlining processes and automating repetitive manual tasks are the first items that banks tend to handle from a cost and operational efficiency point of view. However, banks now want to immediately add intelligence (such as cognitive services, machine learning) to that in order to gain or maintain a strong market position and to improve the service they provide to their customers.”
Nonetheless, many banks need to overcome obstacles to achieve their AI aspirations. “There is an increasing demand for innovation and yet many banks are hampered by the challenge of having a lot of legacy systems,” says Dahler. “At the same time, banks need to attract and retain employees who have the knowledge and expertise – or at least the ability and capacity to learn – to take advantage of the new technologies.”
As a result, there is currently great variation in the industry’s ability to leverage AI. “A few banks are far ahead of the rest,” says Dahler. “They know what they want to accomplish in terms of key performance indicators (KPIs) and also know how to set up the technology so that it becomes part of the culture and strategy of the bank – perhaps in the form of a centre of excellence or an AI lab. In contrast, some banks are only just starting on their AI journey. They aren’t yet fully familiar with AI tools and resources such as machine learning. Some are trying to move from a pilot stage to a more rigorous strategy which will enable them to take advantage of the opportunities that AI can bring.”
Those efforts are paying off, with banks achieving transformation in various areas of the business. Key areas of focus include improving predictive data analysis and combining AI with machine learning to streamline processes, reshape business models and help with decision making. Mitsubishi UFJ Securities, for example, is using Microsoft Azure to help analysts process more than 100 gigabytes of data every night and during weekends without the need to buy more servers or lease a new data centre. And UBS uses an automated screen engine based on Microsoft Azure and Power BI, to catch any sanctioned entities trying to slip through manual Know Your Customer checks.
Customer-centricity is critical to competitiveness in today’s market, and banks have been quick to grasp the potential of AI in this area. “AI is helping to improve customer service in various ways, including new customer insights; treating each customer as a segment; incorporating AI into advisory services; and providing AI assistance for both employees and customers,” says Dahler. “AI can help a bank to understand and predict a customer’s specific transactional behaviour, which in turn can lead to more personalised products.”
Metro Bank, for instance, uses AI to analyse customer interactions and track KPIs including customer satisfaction so it can identify and address problems before they begin to affect the customer relationship. The bank is also launching a service that uses predictive analytics and AI to identify trends in customers’ spending habits and provide personalised prompts to help them manage their finances.
In many cases, AI capabilities complement banks’ regulatory obligations. Dahler notes that mandatory data collection requirements are helping banks to improve the quality of their data and, using AI, their analysis of it. For instance, some banks are using AI to identify risk in areas such as data confidentiality to ensure compliance with the General Data Protection Regulation.
As a broad spectrum of AI projects and use cases continues to develop across the financial services industry, it’s clear that banks see the technology’s potential to transform their business. But those that don’t act now risk being left behind.
“Banks appear to be at very varying states of the AI maturity roadmap and overall banks need to move much faster in making AI part of their digital transformation, looking into new business models and achieving a data-driven culture,” says Dahler.
“Ultimately, banks will need to ensure that they can use analytics and intelligence effectively in the future. This will help them to build deeper relationships in terms of engaging with their customers; optimise their operations; empower their employees to be more effective; and transform their products so that they truly meet their customers’ needs. This all becomes part of a ‘digital feedback loop’ that will eventually help banks to build a more successful and prosperous future.”
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