Key Takeaways:
- Generative AI’s use cases in finance are expanding all the time
- GenAI supports speed, efficiency, accuracy, compliance and more
- Data privacy, ethics and bias concerns should be addressed
- Having the right expertise to guide a GenAI deployment is essential
Top 7 Use Cases of Generative AI in Finance and Banking
Generative AI is continuing to make waves throughout the business world. Its applications in industries where written and visual content are especially important to normal operations, have become well-established.
However, generative AI is also making a real difference in sectors where its use might not be as obvious, including in banking and finance. This blog explores the role of GenAI in finance, and how artificial intelligence is powering innovative financial AI innovations.
The Role of Generative AI in Modern Finance
AI can trace its history in finance back to the 1980s and 90s. Initially, this encompassed the use of computer models and algorithms to perfect trading strategies for investment banks.But as machine learning and big data came to prominence in the early part of the 21st century, the use of AI became gradually more sophisticated, supporting complex tasks like risk management, fraud detection and predictive analytics.
Today, generative AI - the ability to create content based on the requests and commands of human users - is proving instrumental in making repetitive processes faster and more accurate. Among many use cases, it can save staff valuable time in generating the information they need for reports, charts and analysis, time that they can use to add value to their organizations elsewhere
You may like reading: Transformative Effects of Artificial Intelligence on Financial Services
Key Drivers of Generative AI Adoption in Finance
What is behind the major growth in generative AI in finance over the last few years? It’s largely down to a combination of three major factors:
Advances in machine learning
The capabilities of machine learning are both growing at pace, as is the size of its market: Fortune Business Insights estimates the global ML market will rise from $26 billion in 2023 to $226 billion in 2030. Finance firms are increasingly finding that machine learning algorithms can help analyze billing and trading data, spot patterns and suggest the best courses of action.
Larger data volumes
Using big data in finance has already delivered huge results for many finance firms. According to World Metrics, this includes improved operational efficiency by up to 65%, and increased profitability by up to 15%. Generative AI can push these efficiencies even further by automating repetitive processes with a high level of accuracy, saving staff time and reducing the risk of human error.
Scale of potential cost efficiencies
The ability of generative AI to accurately predict financial results, as well as cost and expenditure in the future, can support better decision-making around finances. Analysis of historical data can add vital context to discussions around budget management, reducing unnecessary costs, and optimizing resource allocation.Also read: Does using AI in finance pave the way for other industry adoption?
Top 7 Use Cases of Generative AI in Finance and Banking
As GenAI in finance has gained traction in recent years, more firms in the sector have come to explore different ways to apply the technology including:AI for Fraud Detection
Generative AI can detect patterns of potentially fraudulent activity within financial and transactional data, and continually monitor statistics and metrics to spot anomalies. Not only does this support proactive responses to fraud, but this analysis can also aid compliance with stringent financial and data protection regulations.Customized Financial Guidance
Financial advisors can use generative AI to create personalized analysis and recommendations for individual customers, through processing of their previous transaction data and behavior. Being able to receive detailed, data-driven advice can lead to customers feeling more satisfied, informed and comfortable with their financial affairs and advisors.Automation in Accounting
There are a range of processes that can be automated through generative AI, such as preparing for tax calculations, streamlining bookkeeping and audit processes, and compiling financial reports. All of these can add much-needed speed and accuracy to essential activities that can be time-consuming and prone to human error.Portfolio and Risk Management
Generative AI is useful for managing and assessing risk in every area of a financial organization’s operations. The ability to identify, analyze and respond to risks as they emerge is important for safeguarding profitability, adapting to the challenges of the future, and maintaining compliance in the long term.Financial Forecasting and Analysis
Delving into historical accounting information means GenAI can produce financial predictions very quickly, and with a high degree of accuracy. According to Gartner, two-thirds of finance leaders say GenAI will have a major impact on forecast/budget variance explanations, by making predictions and decision-making more informed.Customer Support through AI Chatbots
At a time of rising customer expectations, chatbots driven by generative AI can enable customer service teams to handle enquiries at much greater speed and scale. Not only can this help customers get quick responses to repetitive queries, but it saves time for service agents to focus on more complex issues.Intelligent Document Processing
Generative AI can also be used to process key financial documents, thanks to its ability to extract invoice data, analyze and summarize legal and financial contracts, and streamline customer onboarding procedures. This can improve accuracy, minimize the risk of non-compliance, and speed up otherwise complicated procedures.
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Benefits of Using Generative AI in Banking
Improved decision-making: deeper analysis of data at large scale can remove the guesswork from important financial and strategic decisions.
Tailored customer experiences: in-depth processing of individual customer data, and focused service and support, enables greater personalization for individual customers.
Improved efficiency: generative AI can complete its processes far faster than even the most talented humans can, saving time and radically boosting productivity.
Stronger security: the ability to spot previously undetectable patterns in data, and inform security teams quickly, can help finance firms keep valuable funds and sensitive data safe.
Enhanced risk management: more detailed analysis of data allows better and more informed decisions to be taken around risk, and for risks to be minimized proactively rather than reactively.
Clearer compliance: cutting down on risk and fraud, and being able to analyze data in greater depth, can help strengthen compliance measures and the ability to demonstrate them.
Advanced data privacy: Generative AI can create synthetic datasets that mimics real data while preserving the original dataset's statistical characteristics. These synthetic datasets, which are entirely made up, contain statistical information but none of the personal identifiable information. This allows organisations to develop and test data protection solutions without exposing sensitive information, and can reduce the likelihood of privacy breaches by up to 75%.
Practical Examples of Generative AI in Finance
Many finance firms have already deployed generative AI to good effect. These include:Wells Fargo's Predictive Banking Features
Wells Fargo has released a user-friendly predictive banking platform within its mobile app, from which consumers can select from a range of 50 AI prompts. The prompts are based on the user’s individual account activity and history, and allows them to gain tailored insights and advice around their financial arrangements.RBC Capital Markets' AI Trading Platform
RBC has launched an electronic trading platform called Aiden, from which users can take advantage of generative AI and machine learning to make faster and more informed trading decisions. Its algorithms help users reduce slippage and market impact, and maximize the profitability of their trades.Generative AI Applications at Goldman Sachs
At the time of writing, Goldman Sachs was on the verge of rolling out its first GenAI-based tool to generate code, improving development productivity within AI applications, and enabling faster processes without sacrificing security or compliance.Challenges and Considerations in Implementing Generative AI
Like any emerging technology, there are always barriers to progress that have to be navigated along the way. When it comes to financial AI applications, these include:
Privacy and security: the vast quantities of data needed by generative AI models to create meaningful output, allied to the sensitivity of financial data, can lead to concerns around data privacy and security.
Data quality: generative AI can be at risk of incorrect output if there are flaws in the data it’s presented with. It’s vital to ensure the model is trained on quality data from the outset.
Bias in AI models: connected to the previous point, models that are trained on biased data can lead to those biases being reflected within output. This can lead to harmful prejudices within society being reinforced, from customer service to recruitment.
Numerical accuracy: according to McKinsey, 44% of organizations have faced negative impacts from inaccurate generative AI output. From a finance perspective, even the smallest numerical error can have severe consequences
In Summary: The Future of Generative AI in Finance and Banking
As GenAI in finance continues to develop, the possibilities for the technology in the sector will continue to expand. For example, generative AI is now starting to be used to create personalised educational content for individual customers, helping them increase their knowledge and awareness about complex financial issues.
But in order to take full advantage, banks and financial institutions will have to become AI-first in their thinking and strategy. Generative AI isn’t as simple as buying technology off-the-shelf: it requires careful planning, expertise and deployment so that it’s used efficiently, successfully and responsibly. The firms that get this right - and use third-party AI services and support to do so if needed - will be the ones that have the most success with AI in the months and years ahead.
