Sibos 2016 & innovation: crossing the next frontier
There has been a huge amount of hype at this year’s Sibos about financial technology and its role in financial services. Devie Mohan* looks at the technologies that will help financial institutions cross the next frontier of innovation.
The world of financial technology has seen a clear and significant shift in the past year – the focus of innovation has moved from pure technology to information and learning. Platforms around payments, lending and advisory have reached a certain level of maturity. Now, the attention of banks, investors and the financial technology industry has shifted to use cases around emerging technologies that build on and learn from all available data and information.
Financial services firms globally are experimenting with cognitive systems, applying them to the vast amount of unstructured data they hold in order to identify potential use cases that will have immediate impact. Whether it is data from industry research, advisory reports, news, product brochures, terms documents, stock information or customer transaction data, these tools can be deployed to help provide a better and more efficient customer service. Firms are increasingly aware of the potential of cognitive computing technologies to increase engagement with the customer, thus improving customer experience and loyalty. Personalised engagement, intelligent targeting and marketing and advisory around personality traits are some of the areas where these technologies are beginning to be applied.
Artificial intelligence techniques including machine learning and neural networks have several applied use cases today, especially around security optimisation, risk management and fraud detection. Financial technology firms such as Feedzai and BillGuard use big data mining and machine learning to detect fraud and risky customers in a much more efficient manner than any traditional method used in banks. Machine learning is being used for image and voice recognition to improve authentication accuracy. However, the vast majority of banking use cases for artificial intelligence is focused on customer support. ‘Chat bots’ and deep insights are used to deliver a human-like, conversational customer experience with quick and easy access to relevant information.
If last year was the year of big data, this year all activities for banks are centred on getting the best out of the now available data infrastructure. They are using analytics, especially predictive analytics, to improve their understanding of customer behaviour. A recent report from Aberdeen Group found that firms using predictive analytics tools experienced an 11 per cent increase in customer acquisition numbers (compared to 8 per cent for those not using them) and an 8 per cent increase in cross-sell revenues (compared to 3 per cent for those not using them).
Almost all banks globally are using predictive analytics techniques across their product portfolio. Citi has used predictive analytics tools as part of its treasury management portal and several banks including Commerzbank, HSBC, Bank of America and BNP Paribas have used it to handle cash forecasting. Although corporate banking products such as cash, trade finance and supply chain finance have been popular with predictive experimentation so far, several innovations are being applied in consumer banking and insurance, for example, to handle peer to peer lending, automated investing and customer underwriting and risk management.
Authentication and know your customer requirements have remained two of the biggest challenges for banks. Many banks are working closely with financial technology firms to explore solutions that offer both accuracy and automated efficiency. Facial recognition, fingerprints, finger vein patterns and iris recognition are some of the most commonly used biometric inputs in branch and cash machine banking. Several banks such as Wells Fargo are experimenting with voice recognition and multi-factor authentication methods to improve online and phone banking security.
One of the most important areas of future development in this space is expected to be around wearables, with banks including Halifax and TD Bank experimenting with heartbeat-based online authentication, which is considered more reliable than fingerprinting.
The obvious application for robots in banking is around automating repetitive manual tasks, especially in customer-facing functions. Software robots can help provide a human-like interface between different systems in a process, or to bring together disparate data sources, with minimal intrusion and increased accuracy. For example, according to Acca Global (Association of Chartered Certified Accountants, the global body for professional accountants), Barclays Bank’s work with robotic automation software resulted in a £175 million annual reduction of bad debts.
Bank of Tokyo-Mitsubishi UFJ and the Bank of Taiwan have launched robot tellers that can learn, adapt and respond to customers’ branch banking queries. Apart from these physical robots, virtual robots or bots are widespread in the banking industry today, handling everything from personal finances and payment of bills through to money transfer and IT support. Most banks have experienced tremendous impact from this automation; for example, Swedbank’s robotic assistant achieved 78 per cent first contact resolution in its first three months of operation.
Augmented reality and internet of things
When Commonwealth Bank of Australia experimented with augmented reality-based property search apps in 2010, it was perhaps the first step towards the digital revolution seamlessly collaborating with the physical world and its senses. Any product that has a physical presence can be digitalised using augmented reality. Such technology can be applied to corporate banking challenges around collateral and inventory management and insurance claims processing. It also can be applied to consumer banking challenges around personal insurance, health sensors, credit cards and retail shop rewards, for example.
Combined with the big data initiatives most banks have taken on, there is tremendous potential for internet of things to connect all payments or sensory devices to a bank’s data networks. Risk management and security remain two areas of concern and there is considerable research and development in these spaces to help find sustainable solutions that work across both urban and rural banking customer groups.
Use cases for emerging technologies
These emerging technologies are being applied in three main areas of banking: customer and IT support, marketing automation and fraud anomaly detection.
Whether it is about humans and machines working together or machines replacing humans in certain tasks, all of the use cases around these emerging technologies are focused on deriving huge efficiencies in terms of human and physical resources.
Quantum computing is another technology being used to achieve vastly improved efficiencies by banks across all groups.
It is clear that with the help of these technologies, both humans and machines will learn, adapt and grow with each other, thus driving the speed of innovation further. With most global banks experimenting actively with new technologies and desiring to be ahead of the innovation curve with these tools, financial technology has crossed over to a new dimension where back-end use cases driven by customer experience, accuracy, security and sustainability of costs have taken over from user interface or user experience improvements and front-end platforms.
* Mohan Devie is a financial technology industry advisor and analyst based in London