Technological advancements are reshaping industries worldwide, banking and financial services being no exception. Among other cutting-edge technologies, artificial intelligence (AI) is getting the most traction in the banking industry.

Banks are adopting AI to enhance their back-office operations, improve customer experiences, and uncover new business opportunities. Why do banks include adopting AI in their business transformation strategy? What are the top applications of artificial intelligence in banking? Let’s sort it out.

Why are banks adopting AI?

AI’s capabilities perfectly match modern banking’s needs, making adopting artificial intelligence a critical part of the digital transformation in the banking sector. AI encompasses a wide range of capabilities, each serving different purposes and applications. Here are some of them:

• Machine learning (ML): A subset of AI that enables systems to learn from data. ML algorithms can recognise patterns, make predictions, and continuously improve their performance through experience.
• Natural language processing (NLP): NLP allows machines to understand, interpret, and generate human language. It powers language-based applications like speech recognition, sentiment analysis, and translation.
• Computer vision: This capability allows computers to interpret and understand visual information. Computer vision is used in facial recognition, autonomous vehicles, and object detection applications.
• Speech recognition: AI can recognise and transcribe human speech into text, facilitating voice-based interactions with various devices and systems.
• Expert systems: Expert systems are AI programs that mimic the decision-making abilities of human experts in specific domains. They use rules and knowledge bases to provide advice and solutions.
• Robotics: AI-driven robots can perform tasks autonomously.
• Pattern recognition: AI can identify patterns and anomalies in data, making it useful for fraud detection and predictive analytics.
• Emotion and sentiment analysis: AI can detect emotions and sentiments from text, speech, or facial expressions, helping businesses become more customer-centric.
• Deep learning: A subset of ML that uses artificial neural networks to process complex data, such as image recognition and natural language understanding.
• Chatbots and virtual assistants: AI-driven chatbots and virtual assistants can interact with users through natural language, providing support, answering queries, and performing tasks.

These are just a few examples of AI’s broad range of capabilities. While they generally boil down to automation and data-driven decision-making, the reasons why the finance sector leaders are adopting AI are apparent:

• faster and more accurate decision-making;
• increasing revenue through providing personalised services;
• enhanced risk management;
• lower costs as a result of automation;
• more efficient resource utilisation;
• rapid innovation cycles resulting from the ability to generate insights from vast amounts of data.

AI for banks: 7 applications that drive change

With the impressive opportunities AI offers, there’s little wonder that banks and other financial organisations are increasingly switching to AI-based systems. In this section, we’ll explore the most disruptive applications of AI in banking.

1. Fraud detection and protection

Fraud has always been a significant concern in the banking industry, becoming even more severe with the advent of the internet and digital banking products. AI algorithms can analyse massive amounts of transactional data, detect anomalies, and identify potential fraud patterns in real-time. By swiftly identifying and mitigating fraudulent transactions, banks can protect their customers and their own assets, ensuring a more secure financial environment.

Mastercard employs artificial intelligence to detect fraud and prevent unauthorised transactions. They developed Decision Intelligence, a fraud detection system that utilises AI algorithms to analyse transaction data and spot patterns of fraudulent activity, ensuring a more secure digital ecosystem.

2. Chatbots

Chatbots are among the most common use cases for AI in the banking sector. AI-powered chatbots — voice assistants, text-based chatbots, or multimodal chatbots — understand natural language queries from customers and provide solutions, communicating with them like real customer support specialists.

Chatbots go a long way toward improving customer engagement while helping banks manage transactions more efficiently. By adding the chatbot feature to banking applications, banking institutions become available for their clients 24/7. Moreover, chatbots can learn through ML, which allows them to offer personalised customer services to banking customers, reducing the workload on other channels.

One of the most prominent examples of AI-driven chatbots is Erica, Bank of America’s virtual assistant. Erica efficiently handles credit card debt reduction and card security updates. As of July 2023, Erica surpassed 1.5 billion client interactions, totalling over 10 million hours of conversations.

3. Credit decisions

Remarkable analytical capabilities of artificial intelligence help banks make more informed loan and credit decisions. An AI-based loan and credit system can analyse the behaviour of customers with limited credit history to check their creditworthiness. In addition, these tools can notify banks of behaviours that increase the likelihood of default.

Capital One, an American bank holding company, has adopted AI to streamline loan underwriting processes. Their AI-driven tool, CreditWise, leverages machine learning to analyse customer credit data and automate credit score checks and approvals. This helps save time and cost, which would be otherwise spent on manual credit checks, and enhances the efficiency of the bank’s core business processes.

4. Data collection and analysis

Banks deal with millions of transactions daily, handling enormous amounts of data. Managing such volumes of data manually can lead to costly errors. On the other hand, AI-driven banking solutions help streamline data collection and processing, minimising errors and ensuring that the data is accurate, up-to-date, and readily available for analysis and decision-making. This increased efficiency saves time and enhances the accuracy and reliability of bank operations, leading to improved customer experiences and decision-making processes.

UBS, a Swiss multinational investment bank, has adopted AI to bring data analysis to a new level. The bank uses AI algorithms to analyse market trends, customer behaviour, and economic indicators, enabling its financial advisors to provide more informed investment advice to their clients.

5. Monitoring market trends

By combining NLP, data mining, ML, pattern recognition, and sentiment analysis, AI-powered systems can help banks process large volumes of data and analyse and predict market trends. Thanks to this, banks can manage potential risks more effectively, make more informed investment decisions, provide superior customer service, and maintain a competitive advantage in the financial industry.

Citigroup has integrated AI into its trading operations to analyse market data and execute trades more efficiently. AI algorithms help traders identify potential market trends and manage orders at optimal times, enhancing trading performance.

6. Process automation

Robotic process automation (RPA) is used to streamline operational processes, ensuring enhanced efficiency and accuracy and reducing costs by automating repetitive tasks. Moreover, this allows employees at banks and financial services companies to focus on more critical business tasks that call for human involvement.

JPMorgan Chase, one of the top financial services companies in the U.S., uses RPA-powered CoiN technology that reviews documents and extracts data from them, saving the company time and resources and ensuring greater accuracy.

7. Regulatory compliance

Regardless of the jurisdiction, banking is one of the most heavily regulated industries, making compliance a critical aspect of every financial institution’s operations. Strict regulations and compliance requirements protect consumers, maintain financial stability, prevent fraud, and ensure fair and ethical practices. Banks must adhere to rules and guidelines issued by regulatory authorities, and failure to comply can result in severe penalties, legal consequences, and, not to mention, damaging the bank’s reputation.

AI-powered solutions can automate compliance monitoring by continuously analysing vast volumes of data to ensure adherence to regulatory guidelines. This helps financial institutions avoid penalties and maintain a strong reputation in the industry. 

HSBC has adopted AI-driven compliance systems to track and identify potential money laundering activities. The bank employs AI algorithms to analyse massive amounts of transactional data, spot suspicious behaviours, and promptly report suspicious transactions to authorities.

Should all banks become AI-first?

While all industries are massively adopting innovation, implementing artificial intelligence is a natural step for banks to become more efficient and meet ever-evolving customer expectations. However, becoming an AI-first bank takes time. It depends on the bank’s size, customer base, technology readiness, and strategic goals. Given that, not all traditional banks can become AI-first immediately.

A bank should carefully assess its unique circumstances, customer needs, and technological readiness before deciding on the extent of AI adoption. For some banks, gradually integrating AI might be the most sensible approach that ensures a seamless transition to a more technologically advanced future.

What are the challenges of adopting AI in the banking sector?

Despite the numerous advantages of AI in banking, its adoption is associated with certain challenges.

Ethical use

As AI algorithms play an increasingly critical role in decision-making processes, specific concerns are related to AI’s ethical use. As a result, banks have to devote extra effort to ensuring transparency and fairness in AI-driven decisions.

Data privacy and security

AI in banking relies heavily on vast amounts of customer data. Consequently, data privacy and security have become significant challenges. Banks must implement robust cybersecurity measures to safeguard sensitive information and comply with relevant regulations.

Integration complexity

Integrating AI into existing banking systems can be complex and resource-intensive. Banks must invest in the necessary infrastructure and ensure seamless integration with their legacy systems, which requires strategic planning and willingness to adapt to new technologies.

Balancing human and AI interactions

While AI can streamline processes and enhance customer experiences, it’s essential to balance human and AI interactions. Maintaining a personal touch and ensuring customers feel valued can be challenging for banks that increasingly rely on AI-powered interactions.

Final thoughts

Continuous innovation and improvement are crucial to business success, so one day or another, every bank will switch to AI-driven internal systems and consumer applications. However, the earlier you start including the adoption of cutting-edge technologies into your business strategy, the better.

At DeepInspire, we have over 20 years of experience in product development and fintech. We can help you build a high-performing AI-powered solution to drive your business growth.