AI Revolution in Banking: Unearthing Trillion-Dollar Opportunities & Transforming Customer Experience

In the era of rapid technological advancement, Artificial Intelligence (AI) is emerging as a key disruptor across industries, with the banking sector standing at the forefront of this transformation. The application of AI in banking goes beyond mere automation, presenting an opportunity to unlock an unprecedented value, akin to unearthing a treasure chest worth $1 trillion annually. This article explores the profound ways in which AI can help banks improve personalization, and efficiency, and explore new opportunities. It further delves into the latest trends in the banking sector and how to position AI at the core of banking operations. As the race to become an AI-first bank gains momentum, discover how banks need to work on customer engagement, decision-making, technology infrastructure, and operational models to stay ahead.

Banks have a long history of adapting to modern technologies. They started using Automated Teller Machines (ATMs) in the 1960s and electronic payment cards in the 1970s. Fast forward to the 2000s, online banking became popular, followed by mobile banking in the 2010s.Now, we're in the era of Artificial Intelligence (AI) where technology can automate tasks and even improve human decision-making. AI technologies can help banks create an enormous value. This is like unlocking a treasure chest worth $1 trillion every year.

AI can help banks in multiple ways as follows:

Personalization:

 With AI, banks can better understand the individual needs of their customers and employees. This means that banks can provide services that are specially tailored to each person's preferences, needs, and financial habits. This might include personalized financial advice, product recommendations, or customer service experiences.

Imagine a bank customer named John, who usually spends a significant amount of his income on travel. An AI system in the bank can recognize this pattern and offer John personalized deals on travel insurance or recommend a savings plan for his next vacation.

Efficiency:

 AI can automate repetitive, manual tasks in banking operations, making the whole process faster and reducing the chance of human error. For example, instead of a human manually checking hundreds of transactions for possible fraud, an AI system can do it automatically and in a fraction of the time.

Suppose a bank has to process thousands of transactions every hour. Without AI, bank employees would need to manually review these transactions for potential fraud, which would be time-consuming and prone to errors. However, with AI, this process can be automated, saving time and increasing accuracy.

New opportunities:

AI is great at analyzing large amounts of data quickly. Banks can use this to discover new business opportunities. For example, by analyzing customer data, an AI might identify trends or patterns that suggest a need for a new kind of financial product or service.

Let's say the bank's AI system identifies that many of its customers are starting small online businesses. The bank could use this information to create a new financial product tailored for these small business owners, such as a loan product with low interest rates and flexible repayment options.

Current Trends in the Banking Sector:

The major trends in the banking industry related to AI are as follows

Rising customer expectations:

As more people use online banking services, they expect a high level of service from their banks. This could include things like user-friendly mobile apps, instant customer service support, and personalized financial advice.

Consider Sarah, a millennial who does almost everything online. Sarah expects her bank to have a user-friendly app where she can transfer money, check her balance, apply for loans, and get customer support whenever she needs it.

Increased use of AI in finance:

 More and more financial institutions are adopting AI for various tasks. This could include using AI chatbots for customer service, machine learning algorithms for detecting fraudulent transactions, and AI systems for managing risk.

For instance, a bank might use AI chatbots to handle customer inquiries. If a customer has a question, they can get instant responses from the AI chatbot, regardless of the time of day.

Digital ecosystems:

The rise of digital platforms and 'super apps' is changing the way people access financial services. For example, an app might let users send messages, order food, book taxis, and also access financial services, all in one place.

Users of this app can send messages, make payments, order food, and even access financial services like loans and insurance. It's like having a bank integrated into an app they use daily.

Tech giants entering finance:

 Large technology companies, such as Google or Amazon, are starting to offer their own financial services. They have a lot of customer data, strong technology capabilities, and the ability to scale quickly, giving them a competitive edge in the financial sector.

An example is Apple, a tech giant that has entered the financial services sector with Apple Pay and Apple Card. These services leverage the company's vast user base and technological capabilities to provide seamless financial services to its customers.

 Focus Area for Banks on AI

 Banks need to incorporate Artificial Intelligence (AI) throughout their entire business to stay competitive. In simple words, banks need to be smarter in how they interact with customers, make decisions, manage data, and organize their operations - and AI can help them do this. To do this, the bank needs to focus on four key areas:

Customer Engagement Layer:

This is about how the bank interacts with its customers. The bank needs to provide a smooth and personalized experience across all of its channels. For example, if a bank offers its services through an app, a website, and physical branches, customers should be able to easily move between these channels without interruption. Furthermore, the bank should use AI to understand customer needs and offer relevant services. Imagine if a bank knows that you regularly shop at a certain store, and offers you a personalized loan or discount for that store.

Suppose a bank wants to become a part of their customer's everyday life. This could be compared to how we use our smartphones daily. For this, they need to change how they interact with customers. They need to offer personalized services rather than just standard banking products. For example, if a customer has multiple credit cards, the bank could provide a service that helps them manage all these cards. This service could suggest which card to pay off first based on their income and expenses. This is an example of a "job-to-be-done" approach where the bank is providing a solution for a specific problem a customer faces. The bank also needs to be available on platforms that their customers use daily, such as social media or messaging apps. An example is ICICI Bank in India, which offered basic banking services through WhatsApp and quickly gained one million users

AI-powered Decision-Making Layer:

This is about using AI to make better decisions. The bank can use AI to analyze data and make accurate predictions, which can help it provide better services and manage risks. For example, AI could help a bank decide which customers are most likely to need a loan or identify suspicious activities that might indicate fraud.

Imagine a bank that wants to provide its customers with personalized messages and decisions in real-time. To do this, they would need to use AI to help make these decisions. For example, a bank might use AI to decide which customer is more likely to respond positively to an offer for a new credit card based on their past behaviour. The bank can also use AI to detect potentially fraudulent activities quicker than humans. To do this effectively, the bank needs to have a plan for how they will use AI across the whole business. For instance, in lending, the bank might use AI to automate various decisions, like determining eligibility, loan amount, interest rates, and so on, based on the customer's financial history and current situation.

Core Technology and Data Infrastructure Layer:

This is about the basic technology and data management systems that the bank uses. These systems need to be reliable and able to handle a lot of data, so the bank can use AI effectively. This might involve using cloud-based systems, which can be easily scaled up or down as needed.

Let's consider that a bank wants to use AI across the entire organization. To do this effectively, they need a strong technology backbone and data management. It's like building a house; you need a solid foundation (technology and data infrastructure) to support the structure above (AI and customer engagement). They need to be able to access and manipulate their data easily. They also need a modern system (APIs) to connect different services and data within and outside the bank. For example, a bank could use APIs to link their services with a popular e-commerce site, allowing customers to make direct payments from their bank accounts while shopping.

Operating Model Layer:

This is about how the bank is organized and operates. To effectively use AI, the bank needs to break down internal barriers and encourage collaboration between different teams. This can make the bank more flexible and innovative.

To better adapt to change and to work more efficiently, a bank might decide to change its operating model. Instead of having separate teams working independently, they could organize cross-functional teams that work together towards a common goal. It's like a football team where each player has a different role, but they all work together to score goals. For instance, a team could be responsible for managing the entire customer lending experience, from promoting loans to processing applications and managing repayments. This would require input from marketing, risk assessment, customer service, and data analysis, all working together within a single platform.

To become an AI-first bank, a bank needs to work on all these layers together. If one layer is weak, it will affect the entire organization. To avoid this, they need to evaluate their current position, decide on what changes need to be made, and create a plan for achieving these changes. They also need to balance delivering immediate results and building long-term capabilities. Just like in a marathon race, it's not only about running as fast as possible but also maintaining the pace to reach the finish line.