Generative AI in Banking: Key Use Cases and Applications in 2024

However, serving the diverse needs of customers efficiently and effectively can be a challenge. Regulators are closely watching expanded uses of AI and generative artificial intelligence. Learn how generative AI differs from other forms of AI and see the ways financial Chat GPT institutions are using GenAI today. Business can either rely on off-the-shelf large language models or fine-tune LLMs for their use cases. For instance, internal audit functions can be greatly enhanced by generative AI through automated analysis and reporting.

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. For example, a customer may need help understanding how much of a mortgage they can afford. When AI models are provided with the relevant details such as interest rate, down payment amount, and credit score, Generative AI can quickly provide an accurate home purchasing budget. You can also use gen AI solutions to help you create targeted marketing materials and track conversion and customer satisfaction rates.

Build Production-Grade RAG

Corporate and investment banks (CIB) first adopted AI and machine learning decades ago, well before other industries caught on. Generative AI models analyze financial data, economic indicators, market trends, and client profiles, generating predictive models for optimal asset allocations and investment strategies. These models adjust portfolios in real-time based on market conditions, maximizing returns while managing risk. This leads to personalized investment strategies, improved client satisfaction, and reduced operational costs. The transition to more advanced generative AI models represents a shift towards addressing the challenges traditional AI systems can’t grapple with. Some banks have already embraced its immense impact by applying Gen AI to a variety of use cases across their multiple functions.

We work hard to produce accurate, timely, impactful journalism without paywalls that keeps our region informed and moving forward. Government use of generative AI comes with risks, including the possibility of convincing fake images, that could erode public trust. You can foun additiona information about ai customer service and artificial intelligence and NLP. Experts worry that officials haven’t properly regulated those algorithmic tools that have been around for years. Since the release of ChatGPT in November 2022, it’s been all over the headlines, and businesses are racing to capture its value. Within the technology’s first few months, McKinsey research found that generative AI (gen AI) features stand to add up to $4.4 trillion to the global economy—annually.

• For slower-moving organizations, such rapid change could stress their operating models.
• Furthermore, the technology can explain the features of different cards, compare them, and guide users through the application process.
• According to an Accenture study, 91% of consumers are more likely to buy from brands that identify, recall and provide relevant offers and recommendations.

For example, when stable diffusion was asked to produce pictures of criminals, most of the output was images of black men. Predictive AI can help determine an individual’s risk profile, helping insurers decide on deductibles and premiums accordingly. This, in turn, allows startups to find the right product for the right market. Platforms like LovoAI have made it quite easy to create an AI voice, edit existing music, and even add layers.

Given Metaverse’s need for enormous content, Generative AI is a perfect technology for content creation. The paper analyses the Generative AI models by grouping them according to the type of content they generate, namely text, image, video, 3D visual, audio, and gaming. Various use cases in the Metaverse are explored and listed according to each type of AI Generated Content (AIGC). This paper also presents several applications and scenarios where the mixture of different Generative AI (GAI) models benefits the Metaverse.

AI is already used in digital banking to improve the customer experience,

automate processes, and reduce the risk of fraud. But the hyper-personalization of banking opens up even more incredible prospects for customer experience. Generative AI models analyze vast amounts of market data, historical trading patterns, news sentiment, and even social media trends. These models then generate sophisticated algorithms that can make split-second trading decisions based on the insights derived from this data. However, as noted above, not all financial institutions are jumping into GenAI with both feet. That compares to 39% of global or national banks with more than $10 billion in assets.

When we had 40 of McKinsey’s own developers test generative AI–based tools, we found impressive speed gains for many common developer tasks. Our research found that marketing and sales leaders anticipated at least moderate impact from each gen AI use case we suggested. They were most enthusiastic about lead identification, marketing optimization, and personalized outreach. It uses Natural Language Processing to understand human input and engage in real-life conversations. When the model becomes skilled at identifying these patterns, it’s able to create similar patterns based on its intensive training.

A waterfall graph shows the potential additional value that could be added to the global economy by new generative AI uses cases. An initial $11.0 trillion–$17.7 trillion could come from advanced analytics, traditional machine learning, and deep learning. And additional $2.6 trillion–$4.4 trillion of incremental economic impact could be added from new generative AI use cases, resulting in a total use-case-driven potential of $13.6 trillion–$22.1 trillion. Gen AI tools can already create most types of written, image, video, audio, and coded content.

The huge force powered by the technology of AI can either make our lives better than ever before or result in disaster. This could happen if AI is integrated without a sharp focus on human centricity. It’s true that the key to becoming a successful financial company post-COVID is having 100% focus on solving the customers’ problems in the most effective way possible, instead of following a standardized scenario.

Know more than you did before? Support this work with a gift!

AI-powered chatbots can provide fast and accurate responses to customer queries, freeing up human customer service representatives to handle more complex issues. Virtual assistants can provide personalized support to customers, answer their questions, and

assist them with tasks such as making transactions or managing their accounts. Balancing these benefits and challenges is essential for banks looking to leverage generative AI effectively. By addressing data privacy, regulatory compliance, fairness, and change management, financial institutions can harness the power of AI while safeguarding their reputation and operations. Generative AI can be used for fraud detection in finance by generating synthetic examples of fraudulent transactions or activities.

According to Cybercrime Magazine, the global cost of cybercrime was $6 trillion in 2021, and it’s expected to reach $10.5 trillion by 2025. Our research found that equipping developers with the tools they need to be their most productive also significantly improved their experience, which in turn could help companies retain their best talent. Developers using generative AI–based tools were more than twice as likely to report overall happiness, fulfillment, and a state of flow.

An NVIDIA AI Enterprise subscription lets you unlock your business data with generative AI and enable better business insights in real time with enterprise-ready RAG. Drafted in October and updated in February, the city’s policy on the use of generative AI — computer systems that create new content — bars city staff from including private city data in interactions with tools like ChatGPT and Bing Chat. This means there are risks of hallucinations, biases and challenges with integrations with proprietary data. But techniques like fine tuning and RAG (retrieval augmented generation) can help to mitigate the problems.

Organizations undergoing digital and AI transformations would do well to keep an eye on gen AI, but not to the exclusion of other AI tools. Just because they’re not making headlines doesn’t mean they can’t be put to work to deliver increased productivity—and, ultimately, value. For one thing, gen AI has been known to produce content generative ai banking use cases that’s biased, factually wrong, or illegally scraped from a copyrighted source. Before adopting gen AI tools wholesale, organizations should reckon with the reputational and legal risks to which they may become exposed. Keep a human in the loop; that is, make sure a real human checks any gen AI output before it’s published or used.

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. Generative AI-driven chatbots can engage customers in natural, human-like conversations, providing instant assistance 24/7. These bots are not just rule-based; they understand context, sentiment, and nuances in language, making interactions seamless and personalized. However, generative AI brings a new level of precision and predictive power to this process. By analyzing vast datasets and generating sophisticated credit scoring models, it can evaluate an applicant’s creditworthiness more accurately than ever before.

Tools like IBM’s Watson Education give teachers a closer look at how their students are doing and help them create more effective lesson plans. These systems can also provide real-time feedback on student assignments so educators can tackle these issues promptly and adjust their teaching strategies as needed. Take platforms like Content Technologies and Carnegie Learning, for example.

Abrigo enables U.S. financial institutions to support their communities through technology that fights financial crime, grows loans and deposits, and optimizes risk. Abrigo’s platform centralizes the institution’s data, creates a digital user experience, ensures compliance, and delivers efficiency for scale and profitable growth. Since customer information is proprietary data for finance teams, it introduces some problems in terms of its use and regulation. Generative AI can be employed by financial institutions to produce synthetic data that adheres to privacy regulations such as GDPR and CCPA. By learning patterns and relationships from real financial data, generative AI models are able to create synthetic datasets that closely resemble the original data while preserving data privacy.

We also need to check the AI regularly for biases and update it to fix any problems. By prioritizing fairness and inclusivity, educational institutions can help ensure that AI tools provide equitable learning opportunities for all students. When we bring AI into education, a major concern is keeping student data private and secure. Indeed, these systems often rely on vast amounts of data to function effectively, including sensitive information about students. And sadly, many educational institutions face challenges in ensuring the security of student data when using AI technologies. AI’s impact extends beyond student learning to include improvements in teaching methods.

Strategy topics will include board performance, technology implementation, data, talent acquisition, deposits and much more. Content concerning risk will cover such as interest rates, liquidity concerns, regulatory considerations, cybersecurity, stress testing and more. Content related to lending will address topics ranging from small business and commercial to hedging, digitalization and more. 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.

This can provide valuable insights for banks, helping them to improve their products

and services and make more informed decisions. Generative AI powers advanced a new era of chatbots that handle customer inquiries with accurate human-like responses. These virtual assistants can understand and generate natural language, offer personalized support, resolve issues, and provide 24/7 support, significantly improving customer satisfaction and operational efficiency. They also reduce the workload on human agents, allowing them to focus on more complex tasks, and can be integrated across various platforms such as mobile apps, websites, and messaging services, ensuring a seamless customer experience.

By evaluating transaction history, online activity, and social media interactions, AI models can generate insights into customer needs and preferences. This enables banks to tailor their products and services, improve customer engagement, and enhance satisfaction. Personalized offerings based on behavior analysis can also lead to increased cross-selling and upselling opportunities.

We also keep up with the latest news in AI, including any changes in rules and regulations around its use. This ensures that the tools we recommend are compliant and that we’re aware of any developments. Predictive AI also uses ‘big data’, which are large, complex, and fast-growing collections of data, so big that average data-processing software can’t handle this amount of information.

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. While we’re still in the early stages of the Generative AI revolution powered by machine learning models, there’s undeniable potential for vast changes in banking. Verticals within financial services predicted to undergo significant transformation include retail banking, SMB banking, commercial banking, wealth management, investment banking, and capital markets. The high interest in gen AI solutions in the banking industry highlights its transformative potential and practical applications.

• It allows organizations to quickly and efficiently locate data and documents stored across various platforms and repositories.
• What differentiates robots from people is the ability to feel emotions and empathy toward one another.
• Compared with only about 30 percent of those with a fully decentralized approach.
• Generative AI is proving to be a formidable ally in enhancing Anti-Money Laundering (AML) practices.

This era of generative AI for everyone will create new opportunities to drive innovation, optimization and reinvention. To address these issues, it’s critical to integrate human expertise into Gen AI’s decision-making processes every step of the way. Such a human-in-the-loop approach is a sure-fire way to detect the model’s anomalies https://chat.openai.com/ before they can impact the decision. Using generative AI to produce initial responses as a starting point and creating feedback loops can help the model reach 100% accuracy. For example, Fujitsu and Hokuhoku Financial Group have launched joint trials to explore promising use cases for generative AI in banking operations.

This ultimately leads to improved financial outcomes for their clients or institutions. However, the banking sector presents unique challenges due to numerous risks and limitations, especially concerning privacy concerns inherent to generative AI technology. Therefore, before diving into implementation details, it is crucial to understand these risks and limitations in full.

Generative AI in education makes it possible to create customized learning experiences. Traditional education often follows a one-size-fits-all approach, which means some students get left behind while others zoom ahead. It can tailor learning materials to fit each student’s unique needs and pace. It also helps teachers find areas where students are struggling and offer help, making education more efficient and available to all. Machine learning further enhances this process by continuously improving the AI’s ability to adapt and predict student performance, making education more efficient and engaging. As AI keeps improving, it will be a key player in changing how we teach and learn.

This helps prevent financial losses, protects customers, and maintains the institution’s reputation. The dynamic landscape of gen AI in banking demands a strategic approach to operating models. Banks and other financial institutions should balance speed and innovation with risk, adapting their structures to harness the technology’s full potential. As financial-services companies navigate this journey, the strategies outlined in this article can serve as a guide to aligning their gen AI initiatives with strategic goals for maximum impact.

The most popular example is Chat GPT, followed by the best AI writing tools like Jasper and Rytr. AI tools stay compliant by implementing robust data protection measures, regularly updating their privacy policies, and adhering to regulations like GDPR and FERPA. Costs can vary widely depending on the complexity of the AI solution, the scale of implementation, and ongoing maintenance. It’s best to consult a provider for a detailed cost analysis based on your needs. Generative AI enables the creation of customizable learning aids that adapt to individual needs, making education more accessible and personalized. Generative AI is changing the game for students with disabilities by making education more inclusive.

Here’s how AI improves access to education and supports students with various challenges. They provide personalized tutoring sessions that adapt to each student’s style and progress. This means students can get the support they need, no matter where they are or the time of day.

Risk Management

For instance, securing student data and ensuring AI tools are used ethically are essential to maintaining trust and fairness in education. Not only is this good business practice, but it will help accelerate the beneficial outcomes your financial institution can achieve with GenAI. According to the McKinsey report, corporate and retail banking have the most to gain from the appropriate deployment of GenAI, with projected gains of $56 billion and $54 billion, respectively. 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.

A good option would be hybrid infrastructure, which allows banks to work with private models for sensitive data while also leveraging the public cloud capabilities. The mitigation solution is to have robust cybersecurity measures in place to prevent hacking attempts and data breaches. As for regulatory compliance, Gen AI itself provides banking and finance with an efficient means of keeping abreast of changing regulatory environments.

For example, BloombergGPT was also evaluated in the sentiment analysis task. As a fine-tuned generative model for finance, it outperformed other models by succeeding in sentiment analysis. Financial institutions can benefit from sentiment analysis to measure their brand reputation and customer satisfaction through social media posts, news articles, contact centre interactions or other sources. OCBC Bank in Singapore has recently reported that a six-month generative AI chatbot trial brought them a 50% efficiency lift, streamlining writing, translation, and research activities.

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. In “Capturing the full value of generative AI in banking,” McKinsey estimates that GenAI could add the equivalent of between $200 billion and $340 billion in value annually across the banking industry. The greatest absolute gains forecast (largely from increased productivity) are tied to corporate and retail banking. A common example of a generative AI-driven tool that many in the financial services industry are familiar with is ChatGPT, which can produce coherent and diverse texts on various topics. Generative AI models emulate data put into them to generate apparently new content such as text, images, audio, or video.

However, it’s crucial to acknowledge that Generative AI is the encompassing circle in the Venn diagram, with ChatGPT being one of the circles nested within it. Today, let’s delve into the broader perspective of how Generative AI is poised to revolutionize the payments landscape. There is a need for highly emotionally intelligent people who serve as translators between customers and the complexity of the opportunities uncovered by new technologies.

Cross-functional teams bring coherence and transparency to implementation, by putting product teams closer to businesses and ensuring that use cases meet specific business outcomes. Processes such as funding, staffing, procurement, and risk management get rewired to facilitate speed, scale, and flexibility. Using conversational AI in the banking sector has become increasingly prevalent in recent years. Major financial institutions such as Bank of America and Wells Fargo have integrated this technology as the backbone of their AI virtual assistants. These AI-driven platforms improve customer experience by providing instant responses and personalized interactions and streamlining numerous banking processes. The banking industry has long been familiar with technological upheavals, and generative AI in Banking stands as the most recent influential development.

Eventually, businesses might find it beneficial to let individual functions prioritize gen AI activities according to their needs. Finally, scaling up gen AI has unique talent-related challenges, whose magnitude will depend greatly on a bank’s talent base. Banks with fewer AI experts on staff will need to enhance their capabilities through some mix of training and recruiting—not a small task. At MOCG, we’re not just a Generative AI development company; we’re your strategic partner in capitalizing on AI to optimize your banking operations. Our team of seasoned experts is well-versed in a wide range of models, including GPT, DALL-E, PaLM2, Cohere, LLaMa 2, and other LLMs. One of the world’s biggest financial institutions is reimagining its virtual assistant, Erica, by incorporating search-bar functionality into the app interface.