Mega Trend in Banking: Global workforce of 100,000 helps bring AI to market

This News Covers

• How is Generative AI being used by Fintech and Banking stakeholders?
• How will banks empower their teams through this adoption of Generative AI?
• How is banking sector being transformed by AI?
• How are Fintech companies using Generative AI?
• Can AI be used for consumer education by Banks and Fintech?

In the race for AI talent within the banking sector, JPMorgan Chase & Co. has taken a significant lead. Between February and April 2023, the company advertised 3,651 AI-related jobs, significantly outpacing its competitors such as Citigroup, Barclays, and Deutsche Bank, who advertised 1,754, 1,374, and 1,268 roles respectively.

Despite widespread layoffs in other divisions across the banking sector, the market for AI-related roles within banks saw a growth of 4% from October 2022 to April 2023, according to the AI Talent Report for Banks by Evident. Currently, the top 60 North American and European banks employ approximately 46,000 individuals in AI development, data engineering, and governance & ethics roles. Collectively, as many as 100,000 global banking roles contribute to bringing AI to market. Notably, a substantial 40% of AI staff within these banks embarked on their current roles since the start of 2022, underlining the rapidly growing demand for AI talent within the banking industry.[1]

MarketsandMarkets welcomes this development, and our editors share their views.

How is Generative AI being used by Fintech and Banking stakeholders?

AI and machine learning are becoming key tools in financial services, enhancing cost optimization, growth models, and customer experience. As banks face pressure from fintech companies, neobanks, and Big Tech, the adoption of technology, data, and AI is essential. Generative AI technology and the use of machine learning in banking apps, online loan applications, and post-trade processing signify this digital transformation. However, due to data sensitivity, banks have been slow to migrate to the cloud, often opting for a hybrid IT asset deployment model.

Morgan Stanley's collaboration with OpenAI to develop a financial advisor solution on the GPT-4 platform is a trailblazing example of how AI can be effectively harnessed in banking. The solution taps into the firm’s large repository of research and data, improving client service and efficiency. Their wealth management division aims to use this technology to synthesize large amounts of data, creating insights for financial advisors to better serve their clients.

Generative AI's potential impact on the labor market could be significant, with studies from OpenAI and Goldman Sachs indicating that it could affect occupations like financial quantitative analysts and even replace up to 25% of current jobs. However, historical trends suggest that job displacement from automation may be balanced by new job creation, allowing workers to apply their skills to more productivity-enhancing activities.

Banks like JPMorgan Chase, Bank of America, and Wells Fargo, with large customer bases and a wealth of data, could also potentially leverage ChatGPT and generative AI. They could use these technologies to automate customer support, manage customer grievances, and reduce operating costs. For instance, automated customer support via AI could handle routine queries, leaving complex issues to human operators, thereby increasing efficiency. Automated analysis of customer data could also identify common pain points and help banks tailor their services more effectively. Furthermore, the potential for AI to replace manual and repetitive tasks could lead to significant cost savings.

AI is a game-changer in the banking industry, offering unprecedented opportunities for cost-saving, efficiency, and enhanced customer service. Morgan Stanley's innovative use of the GPT-4 platform serves as a model for other banks, showcasing the transformative potential of AI. As AI technology advances, its impact on the banking sector will likely continue to grow, potentially disrupting labor markets but also creating new opportunities for increased productivity and growth.

How will banks empower their teams through this adoption of Generative AI?

Generative AI can automate many repetitive and mundane tasks that are traditionally done by humans. This includes tasks like customer inquiries, document processing, transaction verifications, and more. By automating these tasks, employees can focus more on strategic and complex tasks, increasing overall productivity and efficiency. Improved Decision-Making: Generative AI is capable of analyzing vast amounts of data and delivering insights that humans might overlook. These insights can help in making informed decisions, be it for risk assessment, investment strategies, or identifying market opportunities. This ability to leverage data-driven insights will significantly empower banking teams to make better, more strategic decisions.

From the Training and Skill Development perspective, Generative AI models can be used to create realistic training data and scenarios, which can be used for training and upskilling banking teams. This can lead to improved service delivery, risk management, and compliance adherence.

The ability of Generative AI to provide personalized customer interactions can help customer service teams in banks to provide better and more efficient service. By understanding customer behavior and preferences, AI can aid in providing tailored financial advice and services, increasing customer satisfaction. Generative AI can play a pivotal role in risk management. By creating synthetic data reflecting new fraud patterns, it can help teams develop and implement robust fraud detection mechanisms. This proactive approach towards risk management can greatly empower teams to handle and mitigate potential threats.

As the banking industry increasingly adopts generative AI, new job roles are likely to be created. Here are a few possibilities:

1. AI Specialist/Data Scientists: Banks would need professionals who understand AI and machine learning algorithms to build, maintain, and improve their AI tools. These experts could play a crucial role in refining AI models, addressing biases, and ensuring the technology is achieving its intended goals.
2. AI Ethics Officer: Given the potential risks and ethical implications of AI (like bias in decision-making or data privacy issues), banks may need individuals focused on ensuring AI tools are used responsibly and ethically. These roles could involve setting guidelines for AI use, monitoring its application, and ensuring compliance with regulations.
3. AI Translator: As AI becomes more integrated into the banking sector, there will be a need for individuals who can bridge the gap between the technical aspects of AI and its business applications. These roles would require a mix of skills, including knowledge of AI and an understanding of the bank's strategic goals.
4. AI-Enhanced Customer Support: While AI can handle routine queries, there's also a need for human operators to handle more complex issues. These roles would not be traditional customer service roles, but ones enhanced by AI tools which can quickly retrieve relevant information, making the support process more efficient.
5. AI Training Specialist: As AI tools like Chatbots become more prevalent, roles will emerge that focus on 'training' these systems. This involves feeding them the right information, refining their responses, and ensuring they are providing the most helpful and accurate information possible.
6. Data Protection Officer: With the increased use of AI and data analytics, banks would need professionals who can manage and safeguard data, ensuring compliance with data protection laws.
7. Change Management Specialists: These professionals will be crucial in assisting organizations and employees to transition smoothly into an AI-integrated banking environment. This may include training staff, managing organizational change, and mitigating any disruption that may arise during the transition.

How is banking sector being transformed by AI?

Artificial intelligence is paving the way for transformative cost-saving measures in the banking sector. Primarily, the technology is leveraged to automate repetitive and mundane tasks, thereby reducing operational costs. Extraco Banks, a Texas-based bank, exemplifies this trend through the use of its AI virtual assistant, Gabby. The assistant is designed to address 75-80% of customer queries without the need for a banker's intervention. This automation allows the bank's employees to focus their attention on more complex issues, thereby improving operational efficiency and cost-effectiveness.

AI is also utilized to provide more comprehensive and cost-effective customer service. For example, Bank of America's chatbot, Erica, integrates services across spending insights, duplicate charges, refunds, and more, all from one interface. This integration results in reduced overhead costs associated with customer service.

Moreover, AI can help banks predict market trends, assess risks, and make informed decisions, reducing potential losses. By analyzing vast amounts of data, AI can anticipate market fluctuations, provide insights into customer behavior, and even detect fraudulent activities, which can prevent costly financial mishaps.

Nvidia, a leading chipmaker, reveals that banks are capitalizing on artificial intelligence (AI) to improve customer experience, enhance operational efficiency, and reduce costs. In a survey involving 500 global financial services professionals, it was discovered that 36% witnessed a more than 10% annual cost reduction due to AI applications, while 46% acknowledged an improvement in customer experience. This was originally reported by Fortune.com and you can read the complete report here [2].

Banks are increasingly employing generative AI for tasks like document extraction from insurance and mortgage documents and searching internal knowledge bases. One challenge faced by the financial sector is the insufficient size of data for AI model training, which can be addressed using generative AI to produce accurate synthetic financial data. Generative AI can help in areas like customer verification, anti-money laundering, and transaction fraud detection by creating large sample sizes of new threat patterns. Moving forward, generative AI could potentially influence every function in banks, from underwriting and risk assessment to customer service.

How are Fintech companies using Generative AI?

Fintech companies are employing generative AI in creative ways to improve customer experience and increase their service offerings. For instance, AI-powered chatbots like Cleo are used to personalize finance management. Cleo adds a touch of humor to financial interactions, making the process more engaging for users. It helps users manage their finances, track spending, and even advises them on purchases in a fun, conversational manner.

Robo-advisers are another application of generative AI in Fintech. They analyze a customer's financial situation, risk tolerance, and personal financial goals to provide customized investment recommendations. These robo-advisers use machine learning techniques to understand a user's financial behaviors, preferences, market dynamics, and investment analysts' reports, thus delivering more accurate and individualized advice.

Additionally, AI is being employed to save customers money directly. For instance, Trim, an AI money-saving assistant, saved its users an estimated $20 million in 2021. It cancels unused subscriptions and even provides quotes on car insurance to find better deals for users.

Can AI be used for consumer education by Banks and Fintech?

Absolutely. AI plays a crucial role in enhancing financial literacy among consumers. AI-powered platforms like chatbots and virtual assistants provide interactive financial guidance, helping consumers make informed financial decisions.

Chatbots can provide real-time information and insights, help with budgeting, and even teach customers about complex financial topics. By integrating AI with banking services, customers can learn about their spending habits, get advice on savings, and receive customized investment recommendations. The personalized nature of these interactions enhances the educational value for consumers.

Erica, Bank of America's chatbot, exemplifies this by offering a host of features, from spending insights to checking on investments, all while learning from the customer's behavior to evolve and improve its offerings. Similarly, the robo-advisers provided by various Fintech companies analyze a customer's financial situation to offer tailored investment advice, educating the consumer in the process.

References

1. AI Talent Within The Banking Industry
2. A.I. has already helped 36% of financial services execs reduce costs by 10% or more, says an expert at Nvidia
3. How AI Is Being Used for Consumer Education in Banking
4. Smart Banking: How Cloud Computing and Ai Are Transforming the Financial Landscape
5. Artificial Intelligence to take centre stage at the Singapore FinTech Festival 2023
6. Artificial Intelligence in Banking 2023: How Banks Use AI
7. Banking and financial services industry set to become largest investor in AI
8. Morgan Stanley Wealth Management Announces Key Milestone in Innovation Journey with OpenAI
9. GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models