Furthermore, the integration of AI into banking apps and services has driven a shift towards a more customer-centric and technologically advanced industry.
AI-powered systems improve efficiency and decision-making within banks – but they also offer significant cost reductions. A 2023 McKinsey report on banking highlighted the potential for AI to increase productivity by 5% and generate global cost savings of up to $300 billion.
Introduction to AI in Banking
Automation in banking has evolved rapidly… Starting from basic work and Robotic Process Automation (RPA), to deploying AI in data analysis and eventually to sophisticated applications that impact core areas like risk management and fraud prevention.
AI’s deployment in advanced data analytics helps combat fraud and improve compliance. Meanwhile, AI models can streamline anti-money laundering measures, completing tasks in seconds that previously took hours or days.
AI’s data processing speed allows banks to uncover valuable insights that fuel AI development in chatbots, payment advisors, and fraud detection. This translates to a better customer experience for a wider audience, potentially boosting revenue, lowering costs, and improving bank profitability.
Understanding Customer Behaviour
Successful applications in functions that represent relatively “easy wins” have helped shift the focus to customers.
AI unlocked a new level of customer understanding. By analysing everything from spending habits to online behaviour, AI usesd machine learning to predict customer behaviour and tailor services accordingly.
This deep insight helps banks with AI strategies to be proactive. For instance, AI can identify patterns that indicate a customer may soon switch banks. Armed with this knowledge, banks retain customers by offering personalised incentives or targeted offers.
AI analysis of customer data to gain insights into spending habits, savings patterns, and investment preferences. Banks can use these insights to tailor marketing campaigns, enhance customer service interactions, and create new products and services that directly address the evolving needs of their customers.
A rising demand for more personalised customer experiences has dovetailed with the development of generative AI. The latter’s ability to learn, create, predict – and then communicate, promises a further revolution in banking technology and strategies. It also offers a method of automating delivery of better customer experiences at scale.
Personalised Product Recommendations
By implementing AI models, banks can now offer products and services that are tailored to each customer’s unique financial situation and future needs. This shift towards personalised product recommendations fosters deeper customer relationships and loyalty.
Personalised product recommendations ensure customers are only approached with offers that are likely to interest them, optimising the cross-selling and up-selling of financial products. This targeted approach not only increases the success rate of product offers but also reduces the inefficiency of blanket marketing campaigns.
Better Customer Service
AI-driven chatbots are revolutionising customer interactions in the banking sector. These virtual assistants provide personalised, round-the-clock experiences. Powered by natural language processing (NLP), chatbots understand and respond to customer queries in a manner akin to human communication.
This AI strategy allows customers to receive immediate assistance with any banking matter, eliminating the need for long queues or frustrating phone calls. Customers can get instant assistance with various banking matters – from checking account balances and transferring funds to even applying for loans – all through a simple conversation.
Case Studies
Facial and voice recognition are becoming increasingly sophisticated thanks to AI’s ability to analyse vast amounts of data and refine authentication processes. These advancements not only enhance security but also contribute to personalised customer experiences.
A recent example is NatWest, the first major U.K. bank to leverage AI-powered biometrics for remote account opening. Developed with HooYu, the system uses real-time biometric matching to verify a customer’s selfie against official identification documents.
Another example comes from JPMorgan Chase, where researchers use AI and deep learning techniques to develop an early warning system for malware, trojans, and phishing campaigns. This system can identify threats before they occur, providing crucial time for the bank’s cybersecurity team to take preventative measures. These approaches show how AI strategies are shaping the future of banking tech.
Future Outlook
AI has the potential to revolutionise how financial institutions operate and interact with customers.
There is a major security challenge that comes with it. Banks have to prioritise cybersecurity measures to keep sensitive data protected from unauthorised access or accidental disclosures. There are also serious privacy concerns over the use of customer data.
Financial institutions have their own unique vocabulary and styles of communication. While this may seem a disadvantage, these emerged for ease of communication and specificity – and that means AI will be able to both learn and use the same methods finance workers are versed in. AI will likely become a companion tool for individuals within the industry, just as it will be for customers of it. Each will empower and improve the other.
- Artificial Intelligence in FinTech
