Artificial intelligence (AI) use cases in banking and financial sector

How Artificial Intelligence is Used in Banks

AI’s transformative impact has been profound since its advent, changing how enterprises, including those in the banking and finance sector, operate and deliver services to customers. The introduction of AI in banking apps and services has made the sector more customer-centric and technologically relevant.

AI-based systems are now helping banks reduce costs by increasing productivity and making decisions based on information unfathomable to a human. Also, intelligent algorithms can spot fraudulent information in a matter of seconds.

A report by Business Insider suggests that nearly 80% of banks are aware of the potential benefits of AI in banking. Another report by McKinsey suggests the potential of AI in banking and finance would grow as high as $1 trillion.

In this blog, we will discover the key applications of AI in the banking and finance sector.

The evolution of AI in banking

Given the nature of their business models, it is no wonder banks were early adopters of artificial intelligence. Over the years, AI in baking has undergone a dramatic transformation since machine learning and deep learning technologies (so-called traditional AI) were first introduced into the banking sector. With the release of Python for Data Analysis, or pandas, in the late 2000s, the use of machine learning in banking gained momentum. Banking and finance emerged as some of the most active users of this earlier AI, which paved the way for new developments in ML and related technologies.

Traditional AI systems in banking primarily rely on machine learning. They use the technology to recognize patterns in historical data to identify root causes of past events or define trends for the future. Such systems use predefined rules and are trained on structured data often stored in databases and spreadsheets.

Common use cases of traditional AI systems in banking include:

• Fraud detection
• Customer service automation
• Credit score calculations and risk assessment
• Algorithmic trading
• Market trend and customer behavior prediction

Each successive FinTech innovation that came along incrementally transformed banking across its multiple functions, one by one, until generative AI entered the scene to drastically reinvent the entire industry.

The most promising use cases for generative AI in banking

While some financial institutions are adopting generative AI tools at a breakneck pace (though mostly as pilot projects on a small scale), corporate implementation of Gen AI tools is still in its infancy. For the majority of banking leaders, the question of how and where generative AI could deliver the biggest value still stands.

So let us elaborate on how the traditional banking experience can be transformed into a highly differentiated, secure, and efficient service by the convergence of generative AI and banking. These most promising generative AI use cases in banking, with some real-life examples, demonstrate the potential value arising from the technology.

Customer service enhancement

• Advanced chatbots for 24/7 customer support
• Due to their capability to produce meaningful text that resembles human-written content, AI-driven chatbots allow banks to provide 24/7 support, removing customers’ need to wait in long lines or navigate difficult phone menus. These smart virtual assistants act autonomously and can provide customers with on-the-spot guidance and prompt support by responding to basic customer requests, such as:
• Recommending financial services and banking products
• Showing deposit options
• Checking account balances and transaction history
• Initiating and completing transactions

A good example is Wells Fargo’s generative AI virtual assistant named Fargo. The assistant has reportedly handled 20 million interactions since it was launched in March 2023 and is poised to hit 100 million interactions annually. Using Google’s PaLM 2 LLM, the app is designed to answer customers’ everyday banking queries and execute tasks such as giving insight into spending patterns, checking credit scores, paying bills, and offering transaction details, among others.

• AI-driven personalized financial advice
• Along with direct use by customers, generative AI-based chatbots can also significantly assist the front line by suggesting client-specific actions. By examining real-time customer interactions and transactions, Gen AI can offer insightful data on specific customer behaviors and preferences, which can then be used by financial advisors to offer more personalized user experiences.
• Generative AI algorithms analyze vast customer data, including transaction history, account balances, spending patterns, investment portfolios, and financial goals, to build a comprehensive customer profile. This allows banks to enhance their service operations by offering hyper-personalized recommendations based on their specific circumstances, creating customized financial plans, and providing tailored financial advice and product suggestions.
• For example, Morgan Stanley has launched an AI assistant based on OpenAI’s GPT-4 that allows its 16,000 financial advisors instant access to a database of about 100,000 research reports and documents. The AI model aims to help financial advisors quickly find and synthesize answers to investing and finance queries and offer highly personalized instant insights.

Data-driven decision-making

• Trend analysis for market and investment strategies
• There is a rich potential for generative AI tools to considerably assist in strategic decision-making. For one, generative AI can analyze market trends, financial market data, economic indicators, and investment opportunities to generate personalized investment recommendations. Furthermore, it can synthesize and test different market scenarios to propose and evaluate the effectiveness of new trading strategies, thereby helping banks identify profitable opportunities and minimize losses.
• While there’s been increasing interest in applying generative AI across these functions, banks are still exploring how generative AI could be used for generating market and investment strategies. According to Jason Napier, Head of European Banks Research at UBS, “While later there will be other, probably more important, deployments, a lot of the potential of AI appears really nascent at this stage.”
• Fraud detection and risk assessment
• With generative AI on board, banks are well-equipped to enhance their fraud detection capabilities and improve risk assessment. Here is how Gen AI can help.
• With little help from human users, generative AI can promptly identify transaction anomalies indicating fraudulent activity, such as odd locations and devices or unusual spending patterns, and automatically flag potential hazards.
• What’s more, Gen AI techniques, such as GANs, can create synthetic fraudulent transactions to provide a more diverse set of scenarios for training fraud detection models. This can prove critical in improving the robustness and accuracy of fraud detection algorithms.
• Gen AI algorithms can provide insights into the underlying patterns contributing to fraud alerts, which enables more effective decision-making.
• Generative AI models can help banks identify possible risk areas and preserve profitability by analyzing historical data patterns and market trends. By simulating different economic scenarios, GANs can help banks assess and mitigate risks, such as credit risk, market risk, and operational risk.

Mastercard has recently announced the launch of a new generative AI model to enable banks to better detect suspicious transactions on its network. According to Mastercard, the technology is poised to help banks improve their fraud detection rate by 20%, with rates reaching as much as 300% in some cases. The 125 billion or so transactions that pass through the company’s card network annually provide the training data for the model.

Compliance and regulatory checks

• Automating KYC (Know Your Customer) verification processes
• Just as banks and financial institutions need to verify the identity of their clients to avoid commercial relations with synthetic businesses or people related, for example, to fraud, corruption, or money laundering, it is equally critical for them to achieve requirements set by multiple KYC compliance regulations, such as AML, GDPR, and eIDAS.
• Banks can use generative AI technology to automate the time-consuming process of customer due diligence by analyzing large amounts of customer personal data. This may help cut down on customer onboarding time, reduce false alarms, and enhance the accuracy of risk assessment while also ensuring compliance with stringent AML and KYC regulations.

Cost optimization and process efficiency

• Automating routine tasks to reduce operational costs
• One of the highest-value generative AI use cases in banking revolves around automating tedious activities that previously required human input. As an MIT Technology Review Insights report says, “Banks and insurance are among the industries with the greatest proportion of their workforces exposed to potential automation.” Since personnel expenses account for a sizable amount of total costs, the introduction of Gen AI automation into banking operations has the potential to substantially reduce operational costs. These cost savings mostly result from using Gen AI technology to take away the need for analyzing vast volumes of frequently unstructured data.
• Owing to its enhanced ability to understand context and generate natural language texts, summarization capabilities, and predictive intelligence, generative AI holds promise to automate and streamline most of the back-office processes for greater operational efficiency. This will enable operations staff to focus on customers rather than crunching numbers. Accenture forecasts that by 2028, the banking industry will witness a 30% increase in employee productivity. Here is how generative AI can augment the back-office workforce:
• Accelerate report generation using gen AI tools to search and summarize volumes of data in unstructured documents
• Compress lengthy documents into summaries
• Speed up complicated post-trade processes in post-trade operations
• Process loan applications by analyzing various data points, including the applicant’s credit score, financial history, and current data
• Create summaries following business interactions or phone conversations

Several banks are already using generative AI to automate their routine tasks.

How to Become AI-First bank

To overcome the challenges that limit organization-wide deployment of AI technologies, banks must take a holistic approach. To become AI-first, banks must invest in transforming capabilities across all four layers of the integrated capability stack

• the engagement layer,
• the AI-powered decisioning layer
• the core technology and data layer
• the operating model.

When these interdependent layers work in unison, they enable a bank to provide customers with distinctive omnichannel experiences, support at-scale personalization, and drive the rapid innovation cycles critical to remaining competitive in today’s world. Each layer has a unique role to play — underinvestment in a single layer creates a weak link that can cripple the entire enterprise.

Conclusion

As technology advances, the future holds the promise of witnessing even more sophisticated applications of Generative AI in the banking sector. As a result, customers can expect an enhanced banking experience characterized by efficiency, security, and personalization, fostering greater trust in the industry.

Generative AI plays a pivotal role in minimizing the risk of errors by detecting inconsistencies and enhancing the overall quality of financial practices and work associated with banks. Professionals in the banking industry benefit from valuable insights into complex financial matters, as Generative AI can identify patterns and trends within banking data.

Moreover, Generative AI contributes to improved client services by facilitating quick and precise legal research, enabling banks to provide more accurate and efficient assistance to their clients.