Generative AI in banking and financial services

Fraud detection and prevention is one of the famous Generative AI use cases that every sector needs. Generative AI capabilities help banks proactively identify and fix vulnerabilities before they worsen the system. Fraudulent activities such as unusual spending patterns, transactions from odd locations, or detection of new device usage by Gen AI help discover transaction anomalies. In the future banking marketplace, users don’t have to browse a long list of financial products. Instead, using Open Banking APIs, Light Bank itself will choose the right solution from hundreds of products delivered by third-party providers.

Looking ahead, gen AI is likely to develop unanticipated capabilities that may affect a banks’ cybersecurity posture. These will inevitably be double-edged, both in terms of facilitating attacks and defending against them. Knowing the nature of the models and tools will only assist in bolstering defenses. For all the promise of the technology, gen AI may not be appropriate for all situations, and banks should conduct a risk-based analysis to determine when it is a good fit and when it’s not.

This is instrumental in creating the most valuable use cases in both customer service and back-office roles. AI plays a significant role in the banking sector, particularly in loan decision-making processes. It helps banks and financial institutions assess customers’ creditworthiness, determine appropriate credit limits, and set loan pricing based on risk. However, both decision-makers and loan applicants need clear explanations of AI-based decisions, such as reasons for application denials, to foster trust and improve customer awareness for future applications.

AI algorithms deployed to monitor transactions for compliance violations, ensure data privacy, and enhance cybersecurity measures bolstered customer trust and loyalty as digital banking was gaining traction. A frontrunner in financial technology, the company is stepping up its AI game with “Moneyball”. This tool is designed to assist portfolio managers in making more objective investment decisions by analyzing historical data and identifying potential biases in their strategies. The “virtual coach” approach aims to enhance decision-making processes, prevent premature selling of high-performing stocks, and ultimately improve investment outcomes for clients, by drawing on 40 years of market data.

Over the past ten years or so, a handful of corporate and investment banks have developed a genuine competitive edge through judicious use of traditional AI. Use our hybrid cloud and AI capabilities to transition to embrace automation and digitalization and achieve continued profitability in a new era of commercial and retail banking. Ensure adequate storage capacity and data accuracy necessary for developing and training AI solutions. Address any gaps in data infrastructure to support the implementation of generative AI technologies effectively.

Advanced models like OpenAI’s GPT series and other next-generation models have the potential to bring significant benefits to the banking industry. It further helps create marketing campaigns for different customer groups and track campaigns’ performance (Conversion and customer satisfaction) to evolve a marketing strategy that improves the results. Personalized marketing campaigns with customized email responses, automated query handling, and follow-ups engage customers in specific bank services. Similar to every industry vertical, the banking sector must invest in targeted marketing that helps attract customers and maximize the outcomes. It requires investing in Gen AI implementation that analyzes customers’ online behavior and preferences to create different buyer personas.

However, employing GANs for fraud detection has the potential to generate inaccurate results (see Figure 1), necessitating additional improvement. As a major player in the Dutch banking sector, ING used to handle 85,000 customer interactions weekly, but their existing chatbot could only resolve 40-45% of these, leaving 16,500 customers requiring live assistance. Morgan Stanley also introduced an AI assistant powered Chat GPT by OpenAI’s GPT-4, enabling its 16,000 financial advisors to access a repository of approximately 100,000 research reports and documents instantly. The AI model is designed to assist advisors in efficiently locating and synthesizing information for investment and financial inquiries, providing tailored and immediate insights. In capital markets, gen AI tools can serve as research assistants for investment analysts.

The chatbot is designed to handle a wide range of research and administrative tasks, allowing counselors to concentrate on delivering personalized financial advice and building stronger consumer relationships. With this support, consumers make informed decisions and choose the card that best suits their needs. Ultimately, AI-powered systems provide a convenient and efficient way for customers to find answers to all of their questions. Additionally, take note of how forward-looking companies like Morgan Stanley are already putting artificial intelligence to work with their internal chatbots.

Introduction to Cutting-Edge Generative AI Models

Financial institutions must ensure that their AI systems are transparent, secure, and aligned with industry standards to maximize the benefits of this transformative technology. As a bank, you don’t just want to gain new customers; you also want to retain existing ones, and gen AI tools can help you achieve this. And to do that, you must always improve customer service and invest in creating a good customer experience. Moreover, this technology significantly enhances customer experiences by ensuring services are closely tailored to individual needs and preferences.

As a result, generative AI can significantly enhance the performance and user experience of financial conversational AI systems by providing more accurate, engaging, and nuanced interactions with users. For instance, Morgan Stanley employs OpenAI-powered chatbots to support financial advisors by utilizing the company’s internal collection of research and data as a knowledge resource. Also, while AI can automate and streamline many processes, it should not have the final say in critical decisions such as loan approvals. Instead, AI should handle data analysis and initial assessments, leaving the ultimate decision to human financial professionals. This approach ensures that AI serves as a powerful tool to enhance banking operations without overstepping its limitations.

NLP-based chatbots offer human customer support services 24/7, including answering customer queries, updating profile information, executing transfers, and providing balance updates. Second, Generative AI can automate many routine tasks, such as account balance inquiries and password resets, freeing customer service representatives to focus on more complex issues. It can increase efficiency and reduce costs for banks while providing faster and more accurate customer support, allowing banks to avoid the need for large customer support teams. And all of this would be available 24/7, making it easy for customers to get help whenever needed by answering questions, resolving issues and providing financial education outside of regular business hours. Generative AI-driven fraud detection systems are designed to constantly monitor transactions and identify irregularities. These systems employ machine learning models that not only analyze historical transaction data but also generate predictive models to detect fraudulent patterns as they evolve.

Information around regulatory preparations and concerns as well as credit risks will also be addressed. Its ability to comb unstructured data for insights radically widens the possible uses of AI in financial services. Though they cost billions to develop, many of these cloud-based AI solutions can be accessed cheaply. The ability for any competitor to use and string together these AI tools is the real development for banks here.

Loan applications

Generative AI models can analyze massive volumes of transaction data, customer profiles, and historical patterns to identify suspicious activities. These models not only detect known money laundering techniques but also adapt to evolving schemes, ensuring banks stay ahead of criminal tactics. The mitigation solution is to have robust cybersecurity measures in place to prevent hacking attempts and data breaches.

This application saves time, reduces human error, and ensures that stakeholders receive accurate and timely financial insights, allowing financial analysts to focus on more strategic tasks. As per research, 21%-33% of Americans regularly check their credit score, a critical factor in financial health. The score is a three-digit number, usually ranging from 300 to 850, that estimates how likely you are to repay borrowed money and pay bills. An intelligent FAQ chatbot is able to answer questions such as “What is credit scoring? ” Generative AI for banking could get even further, enabling customers to make informed decisions. It’s capable of instantly analyzing earnings, employment data, and client history to generate one’s ranking.

Beyond customer service, generative AI in banking is also transforming fraud detection and risk management. By analyzing vast amounts of transaction data, AI models can identify unusual patterns that might indicate fraudulent activities. This proactive approach enables banks to mitigate risks more effectively, safeguarding customer assets. https://chat.openai.com/ While using AI applications, data privacy and compliance with regulatory requirements are crucial for maintaining customer trust and meeting industry standards. Advanced AI systems such as large language models (LLMs) and machine learning (ML) algorithms are creating new content, insights and solutions tailored for the financial sector.

When it comes to technological innovations, the banking sector is always among the first to adopt and benefit from cutting-edge technology. The same holds for generative artificial intelligence (Gen AI), the deep-learning technology that can generate human-like text, images, videos, and audio, and even synthesize data for training other AI models. Formerly limited to physical establishments, banking has morphed into a completely digital realm, due in no small part to generative AI.

Content concerning risk will cover such as interest rates, liquidity concerns, regulatory considerations, cybersecurity, stress testing and more. Regulation topics address reserve requirements, capital requirements, restrictions on the types of investments banks may make and more. Audit topics will include financial reporting, concerns related to regulatory and legal compliance, ESG, effectiveness and more. This, in my opinion, is where the ultimate potential of AI lies—helping humans do more work, do it better, or freeing them up from repetitive tasks. For banks to stay ahead in the AI-driven landscape, they must invest in AI research and development. This includes funding academic research, establishing partnerships with AI research organizations, and nurturing in-house AI talent.

As a rule of thumb, you should never let Generative AI have the final say in loan approvals and other important decisions that affect customers. Instead, have it do all the heavy lifting and then let financial professionals make the ultimate decisions. All that said, Generative AI can still be a powerful banking tool if you know how to use it properly. Like all businesses, banks need to invest in targeted marketing to stand out from the competition and gain new customers.

Instead, they turned to Gen AI, a powerful tool that swiftly parsed the dense regulatory document, distilling it into key takeaways. This AI-powered analysis empowered risk and compliance teams, ensuring rapid understanding and informed decision-making. A testament to Citigroup’s innovative approach, this move showcases how AI is disrupting the domain in the face of complex regulations. Data quality—always important—becomes even more crucial in the context of gen AI. Again, the unstructured nature of much of the data and the size of the data sets add complexity to pinpointing quality issues.

Moreover, statistics suggest that it could boost front-office employee efficiency by 27% to 35% by 2026. Financial institutions are already actively employing Gen AI in their operations, and the technology’s potential for transforming the industry is vast. Brand’s predictive AI also reduces false positives by up to 200% while accelerating the identification of at-risk dealers by 300%. Faster alerts to banks, quicker card replacements, and enhanced trust in the digital infrastructure.

While AI chatbots are indeed a common use case in the sector, there is much more behind the technology, and a number of large market players are already taking advantage of this promising potential. By analyzing large volumes of data at high speeds, AI algorithms provide actionable insights that enable faster and more informed decision-making. For instance, AI-powered risk assessment models can swiftly evaluate creditworthiness and detect fraudulent activities, reducing decision-making time and enhancing accuracy. AI-driven automation optimizes resource allocation and reduces dependency on human intervention in routine tasks, leading to significant cost savings for financial institutions. By automating back-office processes like data entry and compliance checks, AI minimizes operational expenses and frees up human resources to focus on more strategic initiatives. At this very early stage of the gen AI journey, financial institutions that have centralized their operating models appear to be ahead.

Bank Director offers free minute presentations from thought leaders, covering timely topics facing bank leadership and the board. Bank Director hosts a variety of events throughout the year covering topics such as M&A, talent, compensation, board training, technology, audit and risk. Designed specifically for banks, Bank Director works with boards and/or executive teams to develop and facilitate an agenda, from one hour to a full day. Our in-depth understanding in technology and innovation can turn your aspiration into a business reality. Generative AI can provide rapid and effective customer care by answering common questions and fixing simple issues.

We shared our perspective on applying existing MRM guidance in a blog post earlier this year. We work with policymakers to promote an enabling legal framework for AI innovation that can support our banking customers. This includes advancing regulation and policies that help support AI innovation and responsible deployment. Further, we encourage policymakers to adopt or maintain proportional privacy laws that protect personal information and enable trusted data flows across national borders. Understanding the future role of gen AI within banking would be challenging enough if regulations were fairly clear, but there is still a great deal of uncertainty. As a result, those creating models and applications need to be mindful of changing rules and proposed regulations.

• It also shouldn’t be relied upon to stay compliant with different government regulations, such as the General Data Protection Regulation (GDPR) or the California Consumer Privacy Act (CCPA).
• The companies envision using the technology to generate responses to internal inquiries, create and check various business documents, and build programs.
• Our review showed that more than 50 percent of the businesses studied have adopted a more centrally led organization for gen AI, even in cases where their usual setup for data and analytics is relatively decentralized.
• A one-stop destination to help you identify and understand the complexities and opportunities that AI surfaces for your business and society.

Compliance with legal and data protection requirements is essential to maintain customer trust and avoid penalties. A focus on data quality and addressing data scarcity is required to accomplish this. Ensuring data quality is vital as AI models rely on vast amounts of accurate and up-to-date information to make informed decisions. Banks need to invest in robust data management systems, data cleaning processes, and partnerships with reliable data providers to create high-quality data sets. Data scarcity, on the other hand, can hinder the performance of AI models, especially in niche areas or when analyzing new financial products.

Like any tool, it’s safest and most effective when used by the right people in the right situation. New gen AI tools can direct a large model—whether it be a large language model (LLM) or multimodal LM—toward a specific corpus of data and, as part of the process, show its work and its rationale. This means that for every judgment or assessment produced, models can footnote or directly link back to a piece of supporting data.

Let’s examine the top applications where this technology is making the most significant impact. Discover more examples of how Generative AI in banking is transforming the landscape, along with strategic insights to realize its maximum capacity for your organization. Unlike traditional IVR systems, and even many basic AI voice solutions, which often frustrate members with inaccurate information and repetition loops, Olive offers a more personalized and intuitive experience.

Generative AI shines in algorithmic trading thanks to its adaptability and ability to learn. These models continuously update themselves, allowing them to react to changing market conditions and emerging trends with precision. This results in more efficient trading strategies that can maximize returns and minimize risks. Algorithmic trading has become a cornerstone of modern finance, and Generative AI is at the heart of its evolution. Banks and financial institutions rely on AI-driven trading strategies to optimize their investments and stay competitive in the fast-paced world of financial markets.

This growth is primarily driven by increased productivity.In today’s landscape of banking and finance, Generative Artificial Intelligence (Gen AI) has emerged as a game-changing catalyst for transformation. Far beyond traditional data processing, Generative AI possesses the remarkable ability to generate insights, solutions, and opportunities that are redefining the financial sector. The advent of generative AI in the banking industry is not about technology evolution—generative artificial intelligence is set to redefine the very essence of banking by shaping entirely new business models. The impact Gen AI has on the banking sector is immense across literally all banking functions, especially in terms of banking operations and decision-making.

Banks are expected to continue investing in generative AI models and testing them over the next 2-5 years. In the short term, banks will likely focus on incremental innovations—small efficiency gains and improvements based on specific business needs. Employees will maintain an oversight role to ensure accuracy, precision, and compliance as the technology matures. Morgan Chase & Co. announced the launch of IndexGPT, an AI-powered tool designed to provide investment advice to retail clients in Latin America. This cloud-based service uses advanced AI to analyze and select financial assets tailored to each client’s needs, democratizing access to sophisticated investment tools. In February 2024, Mastercard launched a cutting-edge generative AI model designed to enhance banks’ ability to identify suspicious transactions across its network.

For more on conversational finance, you can check our article on the use cases of conversational AI in the financial services industry. For the wide range of use cases of conversational AI for customer service operations, check our conversational AI for customer service article. Banks are increasingly adopting generative AI to elevate customer service, streamline workflows and improve operational efficiency. This adoption advances the ongoing digital transformation of the banking industry. While traditional machine learning and artificial intelligence have demonstrated efficiency across various aspects of financial management and banking, generative AI stands out as a true game changer for the industry. As artificial intelligence (AI) penetrates operations, streamlines decision-making, and reinvents every facet of customer interactions across multiple industries, it’s also having a transformative impact on banking and finance.

By training on past instances of scams and continuously scrutinizing financial operations, it swiftly pinpoints unusual behavior and promptly notifies clients. Banks also need to evaluate their talent acquisition strategies regularly, to align with changing priorities. They should approach skill-based hiring, resource allocation, and upskilling programs comprehensively; many roles will need skills in AI, cloud engineering, data engineering, and other areas. Clear career development and advancement opportunities—and work that has meaning and value—matter a lot to the average tech practitioner. Generative Artificial Intelligence can also educate on other financial tasks and literacy topics more generally by answering questions about credit scores and loan practices—all in a natural and human-like tone.

Train, validate, tune and deploy generative AI, foundation models and machine learning capabilities with IBM watsonx.ai, a next-generation enterprise studio for AI builders. Integrating data-driven AI systems increases the risk of data breaches, requiring continuous monitoring and updates to protect sensitive customer information. Furthermore, AI models rely on accurate and up-to-date data to produce reliable results. Poor or incomplete datasets can lead to incorrect outputs, negatively impacting financial decision-making and customer trust. Generative AI can handle vast amounts of financial data but must be used cautiously to ensure compliance with regulations such as GDPR and CCPA.

Implementing Generative AI in banking brings forth a host of benefits and, in tandem, some challenges that require careful consideration. Preventing money laundering and complying with regulatory requirements is a paramount concern for banks. Generative AI is proving to be a formidable ally in enhancing Anti-Money Laundering (AML) practices. Gen AI can craft targeted messages, content, and even product offerings that resonate with each customer’s preferences and needs.

You need answers that are not just backed up by evidence, but evidence that is easily retrievable and can be proven to be accurate. This requires a combination of AI and human intelligence, along with a well-thought-out risk-based approach to gen AI usage. What makes Generative AI particularly effective in AML is its ability to generate predictive models that can identify anomalies and patterns indicative of money laundering. These models learn from new data, making them highly adaptable to emerging threats. There has never been a better time to seize the chance and gain a competitive edge while large-scale deployments remain nascent.

Gen AI to reshape banking business models

Generative AI is a game-changer when it comes to enhancing the customer experience in banking. With the ability to analyze and learn from vast amounts of customer data, AI-driven systems can create highly personalized experiences tailored to individual preferences and needs. This level of personalization extends to product recommendations, targeted marketing campaigns, and customized financial advice. Traditional credit scoring methods often rely on outdated or limited data, leading to inaccurate assessments of borrowers’ creditworthiness. Generative AI transforms this process by leveraging vast amounts of data from multiple sources, including social media, transaction history, and alternative financial data. By analyzing this wealth of information, AI-driven algorithms can create a more accurate and nuanced credit score, enabling banks to make better-informed lending decisions.

These AI systems can automatically generate financial reports and analyze vast amounts of data to detect fraud. They automate routine tasks such as processing documents and verifying information. These three domains—new product development, customer operations, and marketing and sales—represent the most promising areas for the technology.

Manual processes often include errors that hamper bank operations; instead, Gen AI technology automates repetitive tasks and scales operations with optimal resource utilization, enabling banks to deliver great value to the customers. To provide customized proposals for each customer, AI could be used for a more accurate customer credit scoring based not only on the user’s bank’s profile and credit history, but also social profiles and offline activity. This would allow the bank to generate a personalized proposal even before the user has requested it. All that the customer has to do is choose the proposal that best fits his/her needs and tap a single button. To secure a primary competitive advantage, the customer experience should be contextual, personalized and tailored.

It’s expected that Generative AI in banking could boost productivity by 2.8% to 4.7%, adding about $200 billion to $340 billion in revenue. While the technology is enhancing customer-facing services, it’s also making significant strides in the realm of investment banking and capital markets. It empowers analysts to rapidly sift through mountains of data, revealing hidden patterns and potential opportunities that might otherwise go unnoticed. Complex risk assessments become more streamlined, allowing for informed decision-making. However, the deployment of generative AI in banking comes with its challenges, including data privacy concerns and the need for regulatory compliance.

Making part of dedicated digital assets, generative AI algorithms can improve financial forecasting by analyzing historical data and current market conditions, providing more accurate and timely predictions. Financial institutions can leverage such tools for strategic planning processes and continuously train AI models with the latest data to ensure relevance and accuracy in predictions. The adoption of AI in banking accelerated further with the integration of big data analytics and cloud computing technologies.

To ensure that, it’s not enough to have brilliant engineers with a highly developed IQ. It’s clear that the explosive growth of the challengers’ customer base depends on the ability to remove obsolete practices and adopt a new, user-centered approach to doing business by adjusting to growing customer needs and digital tendencies. The banking industry has been pressured to adapt new technologies for some time now. The growing pressure from competition with Big Tech companies and the emerging number of Fintechs was largely accelerated by the impact of the pandemic, leaving no choice but to take immediate action. You can foun additiona information about ai customer service and artificial intelligence and NLP. If not developed and deployed responsibly, AI systems could amplify societal issues. Tackling these challenges will again require a multi-stakeholder approach to governance.

It has already become a personal AI assistant and advisor for millions of content creators, programmers, teachers, sales agents, students, etc. Notable generative AI systems include ChatGPT (and its variant Bing Chat), a chatbot built by OpenAI using their GPT-3 and GPT-4 foundational large language models, and Bard, a chatbot built by Google using their LaMDA foundation model. Other generative AI models include artificial intelligence art systems such as Stable Diffusion, Midjourney, and DALL-E. I compare Generative AI appearance with the launch of the internet, in terms of impacting the future of humanity.

Predict ICU readmissions with accuracy using advanced algorithms and data analysis. They can execute trades with unparalleled speed and accuracy, improving their market position and profitability. Algorithmic trading powered by Generative AI also allows for the exploration of new trading strategies that were previously unimaginable. It learns from new data and adjusts its fraud detection algorithms accordingly, making it highly effective against both known and emerging threats. Moreover, it reduces false positives, ensuring that legitimate transactions are not mistakenly flagged as fraudulent.

For example, in this video, we explore how gen AI can speed up credit card fraud resolution — a win-win for customers and customer service agents. The nascent nature of gen AI has led financial-services companies to rethink their operating models to address the technology’s rapidly evolving capabilities, uncharted risks, and far-reaching organizational implications. More than 90 percent of the institutions represented at a recent McKinsey forum on gen AI in banking reported having set up a centralized gen AI function to some degree, in a bid to effectively allocate resources and manage operational risk. Generative AI in banking isn’t just for customer-facing applications; it’s reshaping internal operations as well. Fujitsu, in collaboration with Hokuriku and Hokkaido Banks, is piloting the use of the technology to optimize various tasks. By using Fujitsu’s Conversational AI module, the institutions are exploring how AI can answer internal inquiries, generate and verify documents, and even create code.

In the financial services industry, new regulations emerge every year globally while existing rules change frequently, requiring a vast amount of manual or repetitive work to interpret new requirements and ensure compliance. Developers need to quickly understand the underlying regulatory generative ai use cases in banking or business change that will require them to change code, assist in automating and cross-checking coding changes against a code repository, and provide documentation. We have found that across industries, a high degree of centralization works best for gen AI operating models.

AI helps to refine loan and credit scoring processes by generating detailed risk profiles for potential borrowers. Used in combination with data analysis tools and dedicated machine learning, it helps lenders make more accurate credit decisions and offer personalized loan terms. AI-powered risk models continuously monitor transaction patterns, market trends, and regulatory changes to detect anomalies and mitigate risks in real-time. This proactive approach improves compliance with regulatory requirements and enhances overall risk mitigation strategies, safeguarding the financial stability of institutions and increasing trust among stakeholders. AI-powered virtual assistants are available around the clock to answer inquiries and offer guidance tailored to each individual’s goals.

This personalized approach helps customers make informed financial decisions, achieve their financial goals, and improve their overall financial well-being. Currently, GenAI in banking is primarily used in the back office where it can easily and effectively integrate with simpler workflows. The technology is often focused on automating critical but repetitive processes, including fraud detection, security and loan origination and enhancing the automated customer service experience. GenAI is already driving efficiency and, as McKinsey pointed out, increased productivity is the primary way it will deliver those billion- dollar returns. The transition to more advanced generative AI models represents a shift towards addressing the challenges traditional AI systems can’t grapple with.

When banks expand or work with new client categories, it’s crucial that they provide excellent customer service. This is achieved by addressing FAQs and offering clear guidelines on how to proceed. The information provided should be communicated clearly, using understandable language. Generative AI conversational systems powered by deep learning models can be a valuable resource. The technology improves their understanding of essential financial concepts, banking products, and services.

As we look ahead, the transformative potential of Generative AI remains boundless. Emerging trends like AI-powered financial advisors and predictive analytics are reshaping the industry. By embracing Generative AI and addressing its challenges, banks can lead innovation and deliver exceptional value. Here at Ideas2IT, we offer Generative AI solutions tailored to the banking and financial sectors. Balancing these benefits and challenges is essential for banks looking to leverage generative AI effectively.

If your focus is just banking, a subset of these use cases are listed in generative AI use cases in banking. As a result of this study, it appeared that training GANs for the purpose of fraud detection produced successful outcomes because of developing sensitivity after being trained to identify underrepresented transactions. This is an especially important application for financial services providers that deal with enormous number of transactions. Marketing and sales is a third domain where gen AI is transforming bankers’ work.