When was the last time you visited a bank – and what was it for?
Chances are that most of us would struggle to recall this in today’s digital age. As the need for convenience takes over, more traditional services will move to digital platforms – be it online or mobile.
Digital banking is all about making what can be seen unseen – making services so smooth and seamless that it becomes invisible to the customer. Despite all the automation and improvements that digital banking has the potential to achieve, customers and their needs still form the very core of the banking sector. This emphasis on the customer’s needs and convenience is set to continue in the future economy.
Millennials, in particular, are gravitating towards digital banking. This is why even traditional banks are releasing apps to empower their customers to manage their accounts, reducing the need for physical branches.
The key to all this is analytics – the quiet force that is powering banks – small and large, fueled by the vast amount of data collected over time. Banks are long-time experts in the area of descriptive analytics, which looks at historical data to figure out the who, what, when and how in their organisations. But with artificial intelligence (AI), they can now move towards focusing on high-value activities and creative solutions around the customer experience.
The predictive capability of analytics, even through simple forms such as sentiment analysis, helps banks with quickly understanding customers’ opinions and experiences from across multiple channels. More advanced versions can go well beyond what simple rules or recommendation engines provide, by delivering the best recommendations and decisions to a user’s interactive customer channels – all in real time.
“This opens up a world of bespoke investment advice – traditionally the privilege of more affluent customers – to almost anyone”
Analytics in the form of machine learning and AI also offers banks the power to manage large numbers of transactions in a sophisticated manner and at a lower cost. Consequently, clients can expect more tailored advice, products and services, and even the option to conduct their banking and investment activities on their own.
This opens up a world of bespoke investment advice – traditionally the privilege of more affluent customers – to almost anyone, and opens up investment opportunities to a much wider audience. The power of machine learning and AI also means that each customer transaction or interaction can be highly personalised, given the technology’s ability to analyse data and learn from experience.
AI can also be used to simulate human conversations without any human intervention, in the form of chatbots or automated chat systems. By collecting a massive amount of data through its interactions with the customers, these chatbots are able to learn their behaviour and habits.
Already being used extensively in the banking industry, chatbots are revolutionising customer relationship management at a very personal level.
“The computer could take its own action to manage any potential fraud upon detection by contacting the person concerned, and finding out what has happened.”
Beyond traditional analytics, machine learning and AI also give banks an edge in risk mitigation. By looking at patterns and monitoring atypical behaviour, they are able to detect and even predict fraudulent activities – a concept previously popularised by American science fiction film, Minority Report. This improvement in credit risk assessment is a key part of the digitalization of the financial sector.
AI also helps banks and credit unions manage their regulatory and compliance burden, generating audit trails and flagging suspicious behaviours. AI-based systems are also more robust and intelligent in detecting the anti-money laundering patterns. Over the coming years, these systems are set to become faster and more accurate, with the continuous innovation and improvements in the field of artificial intelligence.
This new a priori fraud detection system will change the traditional method of managing fraud. The computer could take its own action to manage any potential fraud upon detection by contacting the person concerned, and finding out what has happened. This frees up human fraud investigators to investigate and manage more complex fraud issues, which can have a wider systemic impact.
“It is imperative that leading bankers re-imagine their existing business models, build momentum, fail fast and learn from their mistakes to stay ahead in the digital age.”
In many ways, machine learning and AI are a natural extension of existing analytics within banks. The challenge for the industry is to find areas where they can be used to solve problems. Consider where you have a lot of data, where you might need more automated decisions in your capital and liquidity management, or where you might need more personalized interactions with fewer business rules. What can be automated or simplified to help employees carry out their tasks more efficiently or productively?
A digital bank that employs machine learning and AI does not necessarily mean an automatic and robotic bank. It must remain a human bank, one that really understands its customers and the basics of human interaction with technology. Fintechs have been winning largely because of their ability to make the customer journey integrated and seamless. According to Accenture, global investment in financial technology ventures tripled to $12.21bn in 2014, clearly signifying that the digital revolution has arrived in the financial services sector.
The real threat to banks is losing the customer relationship to the new high-tech players like Google, Apple, Facebook and Amazon, who are becoming a new kind of “bank”. They are focused on digital payments, and not on savings or withdrawals. Unless traditional financial organizations can build partnerships with providers outside the financial services industry, they risk losing even their most loyal customers.
Blockchain technology is another shift that is concurrently taking place, with high potential for AI.
As the foundation of the next-generation financial services infrastructure, blockchain will reduce the clutter and complications associated with traditional big-data distributed databases. It will encourage data sharing through decentralised or shared control, which can be applied to AI training data and models as well. This will significantly augment the power and potential of AI, which is data-hungry by nature!
All these digital disruptions highlight the need for banks to invest more in AI technologies. The banks that will thrive in the future are those that embrace openness and collaboration. It is imperative that leading bankers re-imagine their existing business models, build momentum, fail fast and learn from their mistakes to stay ahead in the digital age.
The only way PSD2 will function effectively and securely, will be through the mobile banking application itself. However, the directive does not specify how secure this access will be, nor, what risks will arise, and for who.
PSD2 heralds a new dawn for mobile payments, as the regulatory technical standards around the upcoming European open banking regulations are expected to put mobile devices at the heart of new payment techniques. But despite the regulatory environment nudging markets towards certain payment types, it is not easy to predict exactly how consumers will adopt the technology.
These are interesting – and uncertain – times for global retail banking, from Trump's desire to remove Dodd Frank to Brexit and new British banking regulations.
Matching incoming payments with invoices has long been a frustration for companies with many valuable hours being spent trying to determine who’s paying for what. However, artificial intelligence (AI) and machine learning solutions are starting to emerge that claim they can combat these treasury headaches.