Artificial intelligence is changing the dynamics of businesses and the banking system is no exception. From mobile banking to customised customer service, the role of AI technology is transformational. The hassle of standing for long hours to get banking services is slowly becoming a thing of the past for retail consumers. Consumers’ desire to reach banking services from the comfort of their homes has increased the demand for mobile banking. A recent study by Insider Intelligence showed that more than 45 percent of respondents considered mobile banking among the top three features that influence their selection of financial institutions.

The Big Tech billionaires of the world including Mark Zuckerberg, Elon Musk, and Bill Gates have given life to AI. They are using AI tools and apps in determining consumer preferences and are now influencing other businesses to adopt AI-based technologies. Consequently, banks are investing heavily in AI and predictive analytics to make better decisions and provide customised services.

Even banks that have been reluctant to use AI technology in their processes are using AI chatbots to handle customer queries. As predicted by Elon Musk, “there certainly will be job disruption because what is going to happen is robots will be able to do everything better than us.”

Risk management
Money laundering is an emerging issue for banks because these institutions, in most cases, are unintentionally facilitating such processes. The Financial Action Task Force (FATF) considers money laundering an international issue and stresses the importance of global cooperation. A study conducted by The United Nations Office on Drugs and Crime (UNODC) also highlighted this, stating that nearly 3.6 percent of global GDP, which is equal to $1.6trn, is being laundered each year. A recent report by Zippia showed that the US is dealing with money laundering worth $300bn each year. These figures are alarming for the banks and it is crucial that action is taken when the recessionary pressures on global economies are approaching 2008 levels.

Leading banks are using real-time AI risk management technologies to determine customer behaviours and transaction patterns to combat terrorist financing and money laundering. It closely monitors high-risk accounts by matching a customer’s expected monthly turnover with their actual monthly transactions to raise red flags. This ultimately assists banks in implementing controls to safeguard against losses, fraud and in turn enhances ROI for their consumers.

However, it is worth noting that implementing AI technologies is not the end of the story. AI processes will need optimised frameworks and hardware accelerators to manage AI assignments. Furthermore, financial institutions also need to prepare processes and effectively communicate them with staff to achieve their AI goals rapidly. “Artificial Intelligence technology invariably needs human beings,” says Simon Carter, Head of Deutsche Bank’s Data Innovation Group.

And, as pointed out by Deloitte’s survey, organisations that can communicate a bold vision with an AI strategy are approximately 1.7 times more likely to achieve high outcomes as compared to enterprises that do not. Thus, by using big and complex data sets, banks can create risk frameworks that can provide precise and timely analysis.

Consumer behaviour and AI
Banks offer services and products integrated with AI to customers based on their preferences and searches. One of the best features of AI in banks is its ability to learn. It matures and becomes more intelligent over time. Standard Chartered is using machine learning that helps the bank to decode complex data compilations and slim down the related information.

Banks are using these data analytics to develop their marketing strategies. “Ensuring transparency and explainability in AI-based decision-making is not just a competitive advantage for us, but also the right thing to do by our client,” says Standard Chartered’s Retail Banking Group Head, Vishu Ramachandran. In this way, they are identifying consumers’ preferences and offering targeted products and services, which has helped it to decrease costs and increase productivity.

However, data breaches are a continuing concern for banks that are using AI technology in their processes. Every bank records a large number of transactions daily. The collection of data is a never-ending task, one which raises considerable security issues. A recent data breach in Flagstar Bank, one of the largest banks in the US, has put its 1.5 million customers at risk.

Of course data protection remains a challenge for banks, but they cannot ignore the significance of AI in modern banking. Implementing robust data protection protocols is necessary to counter such threats. On the other hand, banking institutions need to lay the groundwork to support AI teams who can promise efficiency, consumer satisfaction, and improved ROI.

AI offers tantalising opportunities and modern banking must include accessible, secure, and consumer-driven data centres to accelerate data collection and analytics.