“Generative AI is the most powerful tool for creativity that has ever been created. It has the potential to unleash a new era of human innovation.” ~Elon Musk
Generative AI, or GenAI, is playing a crucial role in the finance and banking industry. From streamlining operations to predicting financial trends, the potential is limitless.
Leading banks and financial institutions worldwide are embracing this technology and have already implemented it into their systems to harness the benefits of GenAI.
In this blog, Quytech has explained the role of generative AI in finance and banking. From genAI use cases to its integration process, we have covered it all.
So, let’s start the conversation with some interesting statistics related to generative AI in finance and banking.
Source: Statista
- The financial services industry is expected to see a CAGR of 28.1% for generative AI between 2022 and 2032. Based on this CAGR, the market for generative AI is expected to reach $9.4 billion in 2032, up from approximately $0.85 billion in 2022.
- McKinsey report shows that AI has the potential to raise the global banking sector’s total value by up to $1 trillion every year.
- According to a survey, 78% of financial institutions are implementing or planning for Gen AI integration.
- The Economist Intelligence Unit states that 77% of bank executives believe that GenAI is helpful in gaining a competitive advantage.
- North America is expected to dominate the generative AI in the banking and finance market size from 2023 to 2032.
Generative AI is at the initial stage in terms of usage in the finance and banking sectors. However, the following statistics show that more finance businesses will leverage generative AI in the coming years.
Read further to learn about the role of generative AI in the finance and banking industry.
Role of Generative AI in Finance and Banking
Since its inception, generative AI has showcased its potential in various industry verticals. In finance and banking, it is used for multiple purposes to improve the efficiency of employees, save time, minimize errors, and more.
Generative AI, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and other generative models, transform the financial and banking sector by helping bankers and financial institutions with:
- Synthetic Financial Data Creation
Generative AI plays a crucial role in creating synthetic financial data. In simple words, mock data is used for training generative AI models in situations when real-world data is unavailable or limited.
Moreover, this synthetic data is used for understanding upcoming financial trends, stock market movements, finding investment opportunities, and more.
- Detecting and Preventing Financial Frauds
Last year, 3 men were arrested for defrauding approximately $10 million from US banks and financial institutions. Generative AI has a significant role in tackling and preventing such situations, and AML (anti-money laundering).
Trained on normal transaction behaviors, genAI easily detects abnormalities in transactions and alerts banks and financial institutions about potential fraud and unauthorized transactions.
- Credit Risk Assessment and Underwriting
Another role of generative AI in the banking and financial sector is credit risk assessment and underwriting. Trained genAI models analyze the credit history of the loan seekers and based on their insights, provide detailed reports.
Using these detailed reports, finance firms and banks evaluate the creditworthiness of the borrowers, and make better and more accurate lending decisions.
- Financial Reports Generation
Generative AI also helps bank executives with generating financial reports, presentations, client proposals, etc. This speeds up the internal operations of banks and finance firms, resulting in more productivity and saving time.
- Personalized Financial Products and Services
Personalization is the new tactic of all banking and financial institutions to retain their clients.
Hence, they leverage the best generative AI tools to understand client’s needs and create personalized financial products, such as mutual funds, loans, and investments, tailored to their specific needs.
Also, bankers and finance professionals use technology to offer financial services, such as asset management and insurance, fulfilling the needs of their clients, and providing them with enhanced banking experience.
- Targeted Marketing
Leading finance firms and banks use generative AI for target marketing. GenAI helps marketers to analyze client preferences, and help them create and promote financial products and services tailored to their specific needs.
Generative AI helps finance executives understand the demand and accordingly, they supply the particular product or service in the market.
- Chatbots and Virtual Assistants
Last but not least, generative AI empowers chatbots and virtual assistants in the finance and banking sectors.
Custom genAI-powered chatbots and virtual assistants help finance manpower by enhancing customer support, streamlining account management, and providing accurate data-backed financial pieces of advice.
These are the roles generative AI plays in the finance and banking industry. In coming years, it is expected that we witness genAI in more finance-related operations, enhancing client satisfaction and financial services provider efficiency.
Top Banks and Financial Institutions Leveraging Generative AI
The following are the top finance firms and banks that are using generative AI in their daily operations.
- JPMorgan Chase
JPMorgan Chase is one of the leading financial services providers worldwide. The firm is leveraging custom generative AI, particularly in investment selection processes.
This custom genAI helps Chase’s employees select the best investments for their clients.
- CitiGroup
CitiGroup, popularly known as Citi, is another banking and finance firm that is leveraging generative AI for various purposes.
The bank uses genAI internally for credit assessment, analysis of client behaviors, prediction of market trends, and more.
- OCBC
Oversea-Chinese Banking Corporation, Limited, or OCBC, is also embracing generative artificial intelligence in its internal operations.
The company has created a custom chatbot powered by genAI to automate daily tasks and address customer queries.
- Goldman Sachs
Goldman Sachs, another well-known financial institution, is leveraging generative AI to help its internal team to code software and apps.
The management is also planning to extend its use to other operations, such as risk assessment, customer support, market analysis, and more.
- Bank of America
Bank of America is also exploring generative AI for customer relationship management (CRM) applications.
Moreover, it is also using the technology for creating personalized marketing campaigns to enhance customer engagement and retention.
- Morgan Stanley
Morgan Stanley launched a generative AI chatbot to enhance its wealth management services.
The chatbot offers financial advice tailored to the client’s preferences and risk-taking abilities.
- Wells Fargo
Wells Fargo leverages generative AI for various purposes. The firm uses the technology to automate customer service, fraud prevention, and mortgage underwriting.
Wells Fargo streamlines internal processes and improves operational efficiency leveraging generative artificial intelligence.
Therefore, these are some of the top financial institutions and banks that are using generative AI in their daily operations to streamline processes, enhance productivity, increase customer satisfaction, and more.
How to Integrate Generative AI in Finance and Banking Systems
In this section, you will read about the process of integrating generative AI in the finance and banking systems.
- Identify Use Cases
The first step to integrating genAI in banking and finance systems is to identify the specific use cases where you want to use generative AI to enhance operations.
It might be internal operations, such as assistance with creating financial reports and detecting fraud, or external operations, such as providing financial advice to clients via genAI-enabled chatbots.
- Data Collection and Preparation
After identifying the use case, you need to collect relevant data from internal and external sources.
Generative AI models are trained on data, thus, it is necessary to use high-quality and refined data to train them, otherwise, the results might not be up to the expectations.
- Model Selection and Development
Once you refine the data to ensure it is high-quality, you need to choose the appropriate generative AI models that are ideal for the use case and data requirements.
There are various generative AI models like GANs (generative adversarial networks), VAEs (variational autoencoders), or transformer models like GPT (Generative Pre-trained Transformer).
If you aren’t sure which one to pick, you can consult with a top generative AI development company to choose and develop the ideal genAI model tailored to your specific needs.
- Integration with Existing Systems
Post-development, you can integrate generative AI models into your existing finance and banking systems seamlessly.
Moreover, you can also use APIs and SDKs to make integration more smooth and hassle-free.
- Testing and Validation
Once you integrate generative AI into your existing systems, test it thoroughly to ensure it is functioning well, and providing accurate and desired results.
Manually cross-check the results with real-world data to ensure their accuracy and reliability.
- Monitoring and Maintenance
Lastly, you must monitor the performance of the generative AI and keep updating it with fresh data to improve its performance and accuracy.
Also, you need to detect and fix issues, such as model degradation and adversarial attacks to ensure genAI’s relevancy for a long time.
Therefore, by following the above-mentioned steps, you can implement generative AI into your finance and banking systems.
Challenges of Implementing Generative AI in Banking and Finance
Numerous challenges arise while implementing generative artificial intelligence in finance and banking. The common challenges are as follows:
- Data Quality and Privacy
Data is the core element in generative AI models. These models are trained on data, extracted from internal and external sources.
However, it is crucial to use refined and high-quality data to get accurate results. Also, it is important to have users’ consent to use their sensitive financial data for training generative AI models.
- Compatibility with Existing Systems
The existing banking and finance systems may not support the emerging generative AI models. It is another major challenges that usually arise while integrating genAI into systems.
Therefore, it is important to ensure that existing systems are updated so that they can support generative AI models.
- Compliance With Necessary Regulations
The finance industry is highly regulated. Government and other regulatory boards have strict compliance requirements addressing data privacy, security, and transparency.
Hence, it becomes crucial that generative AI models in finance and banking should be compliant with the necessary regulations, such as standards like PCI-DSS.
- Security Risks
Implementation of generative artificial intelligence in finance and banking systems also brings security risks, such as online hacks, manipulation of models, data breaches, etc.
Thus, the best AI developers implement high-end security measures and practices to protect financial data and reduce security risks.
- Ethical Considerations
Generative AI generates realistic data, but it is still synthetic data, which may be misused and compromise users’s privacy and consent.
Hence banks and finance firms must follow guidelines, governance frameworks, and ethical oversight to protect users from unintended consequences or potential harm.
Therefore, these are the potential challenges that usually arise when implementing generative artificial intelligence into finance and banking systems.
Summing Up
Generative AI in finance and banking is at its initial stage. Still, there are numerous leading banks and financial institutions, such as JPMorgan Chase, Citi, Goldman Sachs, and more, that have already initiated leveraging genAI into their operations.
The emerging subset of artificial intelligence helps financial institutions in various ways. From generating synthetic data to training models to preparing reports and enhancing customer support, generative AI has unlimited potential to transform the financial industry.
In the coming years, the world will witness a significant rise in the usage of generative AI in banks and finance firms. If you also want to transform your finance business with generative AI, feel free to reach out to Quytech.
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Frequently Asked Questions
Yes, you can develop a custom generative AI for finance and banking. However, you must possess deep expertise and experience in developing generative AI and the finance sector.
Leading finance firms, like JPMorgan Chase, Bank of America, Morgan Stanley, and others, are leveraging generative AI for creating synthetic data, detecting and preventing frauds, credit risk assessment and underwriting, generating financial reports, creating personalized financial products and services, target marketing, and enhancing customer support.
We need to know your requirements to quote you the exact cost of developing generative AI for finance and banking. Thus, we encourage you to contact our team and share your requirements to learn about the development cost.