5 Game-Changing AI Applications in Banking Through 2024

Introduction

Artificial intelligence (AI) is redefining the financial services industry as we know it. Far from just a buzzword, AI technologies like machine learning, natural language processing, and intelligent automation are already driving tremendous value for banks across the globe.

Consider these eye-opening statistics:

• According to a recent report by IHS Markit, the global business value of AI in banking is expected to reach $300 billion by 2030, with North America accounting for nearly 79% of the total market (see Figure 1).

• A study by Autonomous Research projects that AI will enable $1 trillion in cost savings for the banking industry by 2030, which includes a 22% reduction in operating expenses.

• 77% of banking executives believe that AI will become an essential part of how banks interact with their customers within the next two years, as per the Economist Intelligence Unit.

Clearly, the strategic importance of AI to the banking industry‘s future cannot be overstated. Banks that fail to prioritize AI capabilities risk falling behind more agile, tech-savvy competitors, from major institutions to disruptive fintech startups.

So which specific AI applications should be on every bank‘s radar through 2024? Let‘s dive in.

1. Chatbots and Virtual Assistants

Perhaps no other AI technology has made a bigger splash in consumer banking than chatbots and virtual assistants. These AI-powered conversational agents are rapidly transforming how banks engage and support customers in the digital age.

By leveraging natural language processing (NLP) and machine learning, today‘s banking chatbots can carry out natural, human-like conversations via text or voice to handle a growing range of customer service and advisor tasks, such as:

• Answering frequently asked questions
• Checking account balances and recent transactions
• Making payments and transfers
• Providing personalized financial advice and product recommendations
• Resolving common technical issues

The chatbot market‘s growth has been nothing short of explosive. Gartner predicts that by 2022, 70% of white-collar workers will interact with conversational platforms on a daily basis. And according to Juniper Research, the adoption of chatbots across the retail, banking, and healthcare sectors will realize business cost savings of $11 billion annually by 2023, up from an estimated $6 billion in 2018 (see Figure 2).

Banks have been at the forefront of the chatbot revolution. Bank of America‘s Erica virtual assistant, launched in 2018, now has over 10 million users and has completed over 100 million customer requests. Other notable examples include Capital One‘s Eno, HSBC‘s Amy, Deutsche Bank‘s Debbie, and many more.

The newest generation of banking chatbots are remarkably sophisticated, capable of understanding complex queries, anticipating needs, and learning from each customer interaction. For instance, Emirates NBD‘s virtual assistant EVA can match customer service inquiries to past interactions to predict why a customer might be calling and provide a faster resolution.

Emerging NLP technologies like Google‘s BERT and OpenAI‘s GPT-3 will only accelerate the advancement of chatbot capabilities in the coming years. Expect virtual assistants to become primary interfaces for an expanding range of banking services, from account opening to financial advice to proactive insights.

2. AI-Powered Fraud Detection and Security

As banks rapidly shift to digital channels, the risk of fraud and financial crime looms larger than ever. According to McKinsey, AI and machine learning can be a game changer for banks in detecting and preventing fraud – potentially reducing losses by up to $12 billion annually in 2022.

Traditional rule-based fraud detection systems simply cannot keep pace with the speed and sophistication of modern financial crime. In contrast, AI and machine learning algorithms excel at analyzing massive transaction volumes in milliseconds to uncover suspicious patterns and anomalies that humans and rules would likely miss.

Some of the key ways banks are leveraging AI to bolster security and anti-fraud efforts include:

• Real-time transaction monitoring to flag abnormal spending behavior
• Identifying synthetic and stolen identities used to create fraudulent accounts
• Network/linkage analysis to uncover criminal rings and money laundering schemes
• Text mining and NLP to detect phishing emails and social engineering scams
• Device fingerprinting and behavioral biometrics to authenticate legitimate customers

A growing number of major institutions have seen impressive results from AI fraud detection deployments:

• Danske Bank‘s AI-powered fraud detection system, which monitors billions of transactions, has reduced false positives by 60% and increased true positives by 50%.

• HSBC has partnered with AI startup Quantexa to analyze internal and external data to uncover suspicious activity indicative of money laundering.

• Mastercard recently rolled out its AI and machine learning technology in Latin America and the Caribbean after a successful trial that saw a 50% increase in fraud detection accuracy.

The next frontier for AI in banking security is shifting from reactive to proactive fraud prevention. Rather than just identifying fraud events after the fact, advanced machine learning techniques can predict which customers, accounts, or transactions are most likely to be fraudulent before they occur. Banks can then take steps to prevent fraud attempts and reduce losses.

3. Personalized Recommendations and Insights

One of the most promising applications of AI in banking is hyper-personalization – the ability to offer highly relevant product recommendations, advice, and experiences tailored to each customer‘s unique needs and context.

Historically, banks have taken a largely segment-based approach to personalization, with broad offers targeted at demographic groups. But with AI and machine learning, banks can now microsegment down to the individual level by analyzing the breadth and depth of customer data at their disposal.

By mining transactional data, engagement patterns, third-party data, and more, AI models can build dynamic profiles on each customer‘s financial situation, life stage, risk tolerance, channel preferences, and likelihood to respond to different offers. Banks can then deliver the right message to the right customer at the right time on the right device.

For example, an AI-powered recommendation engine might:

• Analyze a customer‘s monthly spending and upcoming bills to provide a timely debt consolidation offer
• Recommend personalized wealth management advice based on a customer‘s financial health, risk appetite, and investment goals
• Proactively alert a customer about a suspicious charge and suggest a temporary card freeze
• Identify a customer showing signs of financial stress and share relevant money management tips

The potential impact of AI hyper-personalization on key banking metrics like customer satisfaction, loyalty, and lifetime value is immense. According to The Financial Brand, 75% of customers are more likely to buy from a bank that provides recommendations relevant to their financial needs.

Several banks are already seeing strong results from AI-driven personalization initiatives:

• U.S. Bank‘s AI-powered recommendation system has generated a 3X lift in loan balances and an 18% increase in average household revenue.

• RBC has rolled out NOMI Insights, an AI-powered budgeting and savings tool, to 1 million mobile customers – resulting in a 20% increase in the likelihood of savings transactions.

• Santander‘s AI assistant offers contextual financial tips and product recommendations, with 78% of customers following through on the advice provided.

As AI enables experiences that are radically convenient, empathetic, and attuned to customer needs, hyper-personalization will become a key differentiator for banks in attracting and retaining customers over the next five years.

4. Process Automation and Optimization

The rise of robotic process automation (RPA) and AI is driving massive efficiency gains in banking by digitizing and optimizing cumbersome, manual processes across the enterprise.

According to Accenture, 54% of tasks in the financial services workforce can be augmented with technology, and Deutsche Bank believes AI and automation could replace 1 in 5 of its workforce. Leading banks are aggressively applying these technologies to streamline operations from the front office to the back office, such as:

• Loan application processing and underwriting – JPMorgan Chase‘s COiN uses machine learning to review commercial loan agreements, cutting review time down from 360,000 hours to just seconds.

• Know-Your-Customer (KYC) compliance – HSBC has deployed AI bots to process internal requests to open new accounts, cutting turnaround time from 2-3 weeks to just 48 hours.

• Account servicing and processing – Bank of New York Mellon uses RPA to automate manual account closure processes, reducing risk and saving $300,000 annually.

• Call center operations – RBS has deployed AI to route customers to the right agent and provide agents with real-time transcription and suggested responses, cutting call times by 4 minutes on average.

The potential efficiency and productivity gains for banks are staggering. McKinsey estimates that AI and automation can potentially deliver $200-300 billion in annual run-rate savings for the financial services industry.

But beyond just cost savings, AI-powered process automation can dramatically improve accuracy, compliance, and speed – enabling banks to deliver a far better customer experience. For instance, ICICI Bank uses robotics and AI to power decisions for millions of transactions in real-time, with a 25% reduction in response times for customers.

5. AI-Enhanced Risk Management

Managing risk is at the core of banking, and AI is on the cusp of transforming how institutions measure, monitor, and mitigate risk.

Traditional approaches to risk modelling have limitations in terms of the types of data they can process, the complexity of patterns they can uncover, and the speed at which they can adapt to changing market conditions. AI and machine learning offer powerful new capabilities for assessing risk in real-time across massive structured and unstructured datasets.

Some exciting developments in this space include:

• AI-powered credit risk models – Lenddo uses AI and non-traditional data (like telco and social media data) to provide credit scores for millions of unbanked consumers in emerging markets.

• Market and liquidity risk management – ING has developed an early warning system that uses AI to monitor and predict liquidity risk based on factors like FX rates, equity prices, and real-time payments data.

• Algorithmic trading – Sentient Investment Management‘s AI trading platform ingests millions of data points in real-time and autonomously executes up to 8,000 trades daily in global futures markets.

• Fraud and AML risk detection – NASDAQ has implemented an AI-powered market surveillance tool that monitors up to 17 billion transactions daily to flag high-risk activity and market abuse.

As risks grow in volume and complexity – from sophisticated cyberattacks to high frequency trading – AI will become an indispensable tool for modeling and quantifying risk exposure across the enterprise. Banks that can harness AI to dynamically optimize risk/reward decisions in real-time will gain a significant edge.

How Small Banks Can Leverage AI

While AI may seem like the domain of only the largest banks, smaller institutions cannot afford to ignore this disruption. Regional banks and credit unions can tap into the power of AI without massive investments by taking a smart approach:

1. Partner with AI-focused fintechs and technology providers – from chatbot platforms to AI-powered security solutions

2. Participate in AI consortia and industry utilities to mutualize costs and simplify implementation

3. Utilize AI-as-a-Service and open source tools to quickly pilot AI applications with minimal resources

4. Identify focused use cases to prove out value before scaling AI initiatives

5. Upskill and reskill employees to collaborate effectively with AI through training programs

By starting with lower-risk, higher-return AI applications and gradually expanding capabilities over time, smaller banks can make the leap to an AI-first mindset at their own pace.

Envisioning the AI Bank of the Future

So what do all these AI trends mean for the future of banking?

In five years‘ time, we can expect to see AI firmly embedded into nearly every banking engagement, process, and decision. The AI bank of the future will likely be characterized by:

• Ubiquitous virtual assistants that become the primary interface for customers to get financial advice, open accounts, and complete transactions

• Fully automated and self-optimizing processes running on enterprise-wide AI platforms – from claims management to regulatory reporting

• Real-time, personalized insights and recommendations that guide customers to better financial outcomes at every touchpoint

• Intelligent, adaptive fraud prevention and compliance monitoring that enables secure, frictionless customer experiences

• Autonomous, AI-driven portfolio management, trading, and hedging strategies that optimize risk-adjusted returns

According to Business Insider Intelligence, 75% of banks with over $100 billion in assets are already implementing AI strategies, and 46% of banks with less than $100 billion in assets are doing the same. By 2024, AI will become a mainstream technology across the banking industry.

However, succeeding with AI will require banks to make bold changes to their operating models, workforce strategies, and data architectures. Data silos must be torn down to feed AI‘s insatiable appetite for information. Employees need to be upskilled to interpret AI outputs and make informed decisions. Ethics and governance frameworks must be established to ensure AI systems are transparent, explainable, and unbiased.

Most importantly, banks will need to put AI at the center of their business strategies and view it not just as a cost-reduction tool, but as an engine for reimagining the end-to-end customer experience. The institutions that will thrive in an AI-first world will be those that use the technology to solve customer problems faster, cheaper, and more effectively than ever before.

As the great AI disruption in banking accelerates, one thing is abundantly clear: the future belongs to banks that successfully transition from doing digital to being digital.

Is your institution ready for the AI revolution?