Banking on Innovation: The Disruptive Power of Generative AI

July 3, 2023

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Generative disruption in Banking

Generative AI is revolutionizing banking by automating routine tasks, enhancing cybersecurity, improving risk management, and personalizing customer experiences. By leveraging machine learning and predictive analytics, it helps banks make informed credit decisions and detect fraudulent activities. It also enables real-time customer service via chatbots, improves regulatory compliance, and assists in tracking market trends. The technology's ability to generate new data and scenarios allows for sophisticated risk modeling and strategy formulation. Despite challenges like data security and model explainability, the adoption of generative AI is set to disrupt traditional banking models and propel the sector towards a more efficient and customer-centric future.


How is generative AI impacting Banking and Finance

Generative AI is having a significant impact on the banking and finance sector. By using large datasets to generate new data and insights, generative AI can help improve several aspects of banking and finance operations. Here are some ways in which it is having an impact:

  1. Risk Management: Generative AI can be used to create simulated data which can predict future trends and risks. This data can be used in stress testing, scenario analysis, and decision-making, helping financial institutions mitigate risks and make informed decisions. 
  2. Fraud Detection: By learning from patterns in existing data, generative AI can identify anomalous transactions that could be indicative of fraud. Early detection of fraudulent activity can save financial institutions significant amounts of money.
  3. Customer Service: Generative AI can enhance customer service by powering chatbots and virtual assistants that can handle customer queries around the clock. These AI-powered systems can generate human-like responses and learn from their interactions, improving their ability to resolve customer issues over time. 
  4. Personalized Services: Generative AI can analyze customer data to identify patterns and preferences. This can be used to generate personalized financial advice or products tailored to the needs of individual customers, improving customer satisfaction and retention. 
  5. Loan Processing: Generative AI can help automate and streamline loan processing. For example, it can generate credit scores based on customer data, automating the decision-making process in loan approvals. 
  6. Marketing: By analyzing patterns in customer data, generative AI can also help banks and financial institutions develop more targeted and effective marketing strategies. 
  7. Compliance: Generative AI can help banks in adhering to various regulatory compliances by generating reports, analyzing transactions for suspicious activity, and ensuring that all activities are within the regulatory boundaries. 
  8. Trading: Generative AI can also be used in algorithmic trading, where it can generate trading strategies based on pattern recognition from historical data. 

While generative AI has many potential benefits, it's also important to consider challenges such as data security, privacy, and the quality of the data used for training AI models. Furthermore, the outputs from generative AI systems may sometimes be difficult to interpret, making it hard to understand how certain decisions or predictions were made.


Which major banks are adopting generative AI?

While major banks recognize the potential of generative AI, they're taking cautious steps towards its adoption, given the technology's nascent stage and the sensitive nature of banking data.

  1. Swift: The head of AI at Swift, Chalapathy Neti, has hailed the progress of generative AI models like ChatGPT and GPT-4 as "mind-boggling" and transformative, signaling an openness to these technologies.
  2. ABN Amro: This Netherlands-based bank is piloting the use of generative AI in its operations, such as automating the summarization of conversations between bank staff and customers and assisting employees with customer data gathering.
  3. ING Bank: Mariana Gomez de la Villa, an executive specializing in strategy and innovation at ING Bank, revealed that the bank is in the experimentation stage with generative AI, mainly for tasks like code refactoring and analyzing client behavior.
  4. BBVA: This Spanish bank has taken a more conservative approach to generative AI, noting that the technology is still in the early and potentially risky stages. 
  5. Goldman Sachs: The bank is experimenting with generative AI tools internally to aid its developers with automatic code generation and testing. The bank also spun off a startup called Louisa from its internal incubator that uses AI for corporate social media use.
  6. Morgan Stanley: The bank is using generative AI to inform its financial advisors about queries they may have. An OpenAI-powered chatbot is being tested with a select group of advisors, aiming to assist its larger advisor base in leveraging Morgan Stanley's research and data repository. 

Major financial institutions and Fintech companies adopting Generative AI?

The growing interest and adoption of generative artificial intelligence (GAI) by the financial services industry, despite the sector's traditionally cautious approach towards the utilization of AI. It outlines some of the key advantages and applications of GAI in financial services, including customer support, fraud detection, personalized marketing, and risk and compliance.

Here are the key financial institutions and fintech companies that are leading the way:

  1. ABN Amro: At the Money 20/20 fintech conference, the bank revealed that it was using GAI to automatically summarize conversations between bank staff and customers and gather customer data for query resolution.
  2. ING Bank: While not having implemented GAI in customer-facing applications, the bank is using it for internal code improvement and customer behavior analysis.
  3. BBVA: The Spanish bank, although taking a conservative approach to AI, is also experimenting with GAI in its operations.
  4. Goldman Sachs: This financial giant has been testing GAI tools for automating code generation and testing. It has also spun off an AI-powered startup called Louisa from its internal incubator.
  5. Morgan Stanley: The bank has been testing an OpenAI-powered chatbot with its advisors to provide better access to the bank's repository of research and data.

How are banks adopting Generative AI. Top use cases for banks using generative AI?

Several banks globally are already beginning to utilize Generative AI in the ways described above. They are adopting generative AI techniques to leverage the potential of AI in areas of marketing, loan applications, credit analysis, loan underwriting, loan servicing, and debt collection.

  1. Marketing and Lead generation: Banks like JP Morgan Chase and Citigroup use generative AI to tailor marketing and lead generation efforts based on customer profiles. AI models process customer data to identify potential leads and make personalized product recommendations.
  2. Loan Application: Banks like Wells Fargo employ AI-powered chatbots to assist customers through the loan application process. The chatbots can guide users, helping them fill out forms and answer questions, making the process more user-friendly and efficient.
  3. Credit Analysis: Generative AI models help in assessing creditworthiness of customers. The AI models process various data points including credit reports, income statements, and bank statements to evaluate the risk level associated with a loan application. An example of this is Goldman Sachs' use of machine learning models for credit underwriting.
  4. Loan Underwriting: Generative AI is used in automating parts of the underwriting process. For instance, the AI can generate sections of credit memos, such as executive summaries, business descriptions, and sector analysis. This streamlines the underwriting process, making it faster and more efficient.
  5. Loan Servicing: AI can also assist in managing and servicing loans. For example, Bank of America's AI assistant, Erica, helps customers with payment reminders, billing inquiries, and account management.
  6. Debt Collection: Generative AI can interact with customers to provide customized repayment options and recommend collection strategies. Banks like HSBC use AI to analyze customer data and predict potential delinquencies before they occur.

Here are the top use cases:

  1. Fraud Detection: Banks can use Generative Adversarial Networks (GANs), a type of generative AI, to create synthetic fraudulent transactions. By training AI models to distinguish these transactions from genuine ones, banks can improve their ability to detect actual fraudulent activities.
  2. Data Protection: Banks can use generative AI to generate synthetic versions of sensitive customer data, like credit card numbers, for testing and analysis. This preserves data privacy while allowing the institutions to enhance their systems.
  3. Personalized Product Recommendation: Banks can use generative AI to create customized product recommendations for their clients based on individual spending patterns, income, and other financial information. This helps in improving customer engagement and satisfaction.
  4. Risk Management: Banks can use GANs to generate various economic scenarios based on vast amounts of financial data. These scenarios can help banks predict market trends, manage market risk, and maintain appropriate levels of risk exposure.
  5. Loan Processing: Generative AI can help in automating and enhancing the loan processing experience. For instance, if a loan application is denied, AI can generate personalized explanations for the client, highlighting the specific factors that led to the denial and providing recommendations for improving their creditworthiness in the future.

While these use cases represent significant opportunities, banks should also be mindful of the potential drawbacks and ethical considerations related to AI use, such as ensuring the transparency, auditability, and security of AI systems and taking measures to prevent potential bias in AI decision-making. Responsible and ethical use of AI technologies is crucial for maintaining customer trust and regulatory compliance.


How are fintech and finance institutions adopting Generative AI. Top use cases for them to use generative AI?

Fintech and finance institutions are making increasingly sophisticated use of generative AI in their operations. Here are some of the main ways they're doing so:

  1. Improved Risk Management: Using generative AI models, these institutions are able to generate vast amounts of synthetic data that reflects a range of possible market conditions. This data can be used to train other AI systems to react optimally under different scenarios, significantly improving the institution's risk management capabilities.
  2. Fraud Detection and Prevention: Generative AI can be used to create synthetic datasets of fraudulent transactions, which can then be used to train machine learning models to better identify and prevent fraud.
  3. Algorithmic Trading: Generative AI can be used to build trading models that generate profitable trading strategies based on historical and real-time market data. This allows for rapid, automated decision making that can take advantage of small market movements that might be missed by human traders.
  4. Personalized Services: Using generative AI, fintech companies can analyze user behavior and provide personalized financial advice, tailored product recommendations, and targeted marketing campaigns. This allows them to enhance customer engagement and increase customer retention.
  5. Credit Scoring: Generative AI can be used to create more comprehensive and accurate credit scores. These systems take into account a wider range of data than traditional credit scoring methods, potentially including unconventional data points like online behavior or purchasing habits, leading to more accurate assessments of creditworthiness.
  6. Automated Customer Service: Many fintech companies are using generative AI to create sophisticated chatbots and virtual assistants. These can provide customer service 24/7, guiding users through complex processes and answering a wide range of questions.
  7. Data Protection: Generative AI can be used to generate synthetic data that closely mirrors real customer data, but without any of the associated privacy risks. This synthetic data can be used for various purposes, such as training AI systems or testing new products and services, without exposing sensitive customer information.
  8. Predictive Analytics: By leveraging generative AI, financial institutions can forecast future trends, enabling them to make strategic decisions based on those predictions. This can include predicting stock market trends, forecasting customer behavior, or anticipating market risks.

These use cases all show how generative AI can offer valuable tools for fintech and finance institutions to optimize their operations, innovate their services, and offer better value to their customers.


How is generative AI being used in stock markets. Top stock exchanges being impacted and how?

The use of generative AI in the stock market is becoming increasingly widespread. By generating sophisticated algorithms and models to analyze and predict market trends, these technologies are changing the way trades are conducted and decisions are made. Here are some of the primary ways generative AI is being utilized in the stock markets:

  1. Automated Trading: Generative AI models are used to create automated trading systems that can respond to market changes in real-time. These algorithms can analyze vast amounts of data, identify patterns, and generate trading strategies. Notable stock exchanges that have incorporated AI in trading include the New York Stock Exchange and the NASDAQ.
  2. Market Simulation: Generative AI can generate synthetic data that simulates various market conditions. This data can be used to test trading strategies and models, reducing the risk of actual losses.
  3. Predictive Analysis: Generative AI models can predict future market trends based on historical and real-time data. This allows traders and investors to make more informed decisions and potentially gain a competitive advantage.
  4. Portfolio Management: Generative AI algorithms are used to create and manage investment portfolios. These algorithms can analyze an investor's risk tolerance and investment goals and generate a tailored portfolio.
  5. Risk Assessment: Generative AI can also be used to identify and assess potential market risks. For instance, these algorithms can predict how different factors, such as changes in market regulations or economic conditions, might affect the market.

Top stock exchanges being impacted:

  1. New York Stock Exchange (NYSE): The NYSE has been actively exploring AI technologies to enhance its trading mechanisms. This includes using AI for market surveillance to detect and prevent fraudulent activities, as well as using AI for automated trading and risk management.
  2. NASDAQ: NASDAQ uses AI for various purposes, such as market surveillance, predictive analytics, and algorithmic trading. They also leverage AI for improving operational efficiency and providing better services to their clients.
  3. London Stock Exchange (LSE): LSE has also been incorporating AI technologies into its operations. This includes using AI for risk management, regulatory compliance, and enhancing its trading mechanisms.
  4. Tokyo Stock Exchange (TSE): TSE is another exchange that's actively exploring AI technologies. They use AI for market surveillance, automated trading, and improving the efficiency of their operations.
  5. Shanghai Stock Exchange (SSE): SSE has been making use of AI for market surveillance, risk management, and improving operational efficiency.

While the use of generative AI in stock markets holds a lot of promise, it's not without its challenges. Issues such as data privacy, the potential for algorithmic bias, and the risk of overreliance on AI systems are all areas that need to be carefully considered and addressed. Furthermore, regulatory bodies worldwide need to develop and enforce guidelines for the responsible use of AI in financial markets.


What are the top finance related generative AI courses

The adoption of artificial intelligence (AI), including Generative AI, in finance has seen a rapid increase, leading to a surge in demand for relevant courses. These courses help individuals understand the application of AI in finance, how to leverage AI for financial decision-making, risk management, trading, and more.

Below are some of the top finance-related generative AI courses:

  1. Artificial Intelligence for Trading by Udacity: This course provides a comprehensive overview of how AI is used in trading, including how to use machine learning algorithms to analyze market trends and make trading decisions.
  2. AI in Finance by the New York University (NYU Tandon School of Engineering): This is an advanced course aimed at professionals and students interested in understanding the applications of AI and Machine Learning in Finance. The course covers essential aspects such as AI modeling, prediction, and decision-making in finance.
  3. AI in Finance by Coursera (offered by University of Michigan): This course helps students understand the fundamental concepts of AI, machine learning, and how they can be applied to financial services. The program covers topics such as credit scoring, algorithmic trading, and customer service.
  4. Machine Learning and Reinforcement Learning in Finance by Coursera (offered by New York University): This specialized course focuses on applying machine learning and reinforcement learning algorithms in finance. It covers topics such as predictive analytics, modeling, and decision-making.
  5. Artificial Intelligence Applications: FinTech by edX (offered by University of Oxford): This course explores the opportunities and challenges of AI in the financial sector, including robo-advising, algorithmic trading, and fraud detection.
  6. Advanced AI: Deep Reinforcement Learning in Python by Udemy: This course is aimed at teaching students about reinforcement learning, a branch of AI that's widely used in finance for decision-making processes.
  7. Professional Certificate in Machine Learning and Finance by edX (offered by Columbia University): This program combines machine learning principles with practical financial applications. The course emphasizes hands-on learning and uses real-world case studies to illustrate how machine learning can be used in finance.

What they teach in generative AI courses for Banking, Finance and Fintech

Generative AI courses for Banking, Finance, and FinTech typically focus on how artificial intelligence, and more specifically generative models, can be used to address challenges and create opportunities in these sectors. The curriculum generally covers a broad range of topics, including but not limited to the following:

Here are the countries that are leading the way in adopting AI in banking and finance:

  1. United States: The U.S. is home to a large number of financial institutions and tech companies that are at the forefront of AI adoption in finance. Many top-tier banks like JPMorgan Chase, Bank of America, and Citigroup have integrated AI technologies for fraud detection, customer service, and investment strategies. There are also numerous fintech startups developing innovative AI solutions for the finance sector.
  2. United Kingdom: The UK, particularly London, is known for being a major financial hub with a vibrant fintech scene. Banks like Barclays and HSBC, as well as numerous fintech companies, are incorporating AI into their operations to improve customer service, streamline operations, and improve risk management.
  3. China: Chinese financial institutions and tech giants like Alibaba and Tencent are heavily investing in AI. They're using AI technologies for credit scoring, personal finance management, and risk management. Furthermore, China's mobile and digital payment ecosystem is one of the most advanced in the world, which also leverages AI.
  4. Singapore: The city-state is a major financial hub for Asia and has been actively encouraging the use of AI in finance. The Monetary Authority of Singapore (MAS) has provided clear guidelines on the use of AI in finance and has launched initiatives to foster innovation in fintech.
  5. Canada: Canada is home to advanced AI research and has a strong financial sector that is increasingly adopting AI. Canadian banks are using AI for a range of applications, from enhancing customer service with chatbots to improving risk assessments and detecting fraudulent transactions.
  6. Australia: Australian banks, such as ANZ and Commonwealth Bank, are integrating AI in their operations for improved customer experience, efficient risk management, and enhanced fraud detection.
  7. Germany: As the leading economy in the European Union, Germany houses many banks and financial institutions that are applying AI technologies. Deutsche Bank and Commerzbank, for example, are using AI for operations such as predicting market trends and automating routine tasks.
  8. India: With a rapidly digitizing economy and a growing fintech sector, Indian financial institutions are increasingly adopting AI for customer service, operational efficiency, risk management, and fraud detection.

These countries have strong financial sectors and robust technology ecosystems, which makes them well-positioned to lead in the adoption of AI in banking and finance. However, the global nature of financial services and the rapid pace of AI development means that many other countries are also actively exploring and adopting AI in finance.

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