by Aryan Khanolkar
In an era where technology is rapidly reshaping the contours of industries, the realm of finance and banking stands at the forefront of transformative change. Enter the realm of Generative Artificial Intelligence (AI), a revolutionary force that is poised to redefine the very essence of financial systems and services.
Let’s first examine the relationship between generative Artificial Intelligence and large language models before exploring the potential of generative AI in the banking sector. Generative AI is essentially an emerging technology that creates content using artificial intelligence, algorithms, and large language models. Generative AI is a broad category of AI models that can create new data, while large language models are a specific subset of generative AI that focuses on natural language processing tasks. LLMs are algorithms that utilize deep learning on different data sets to understand and then generate output. One of the most important benefits of generative AI is its capacity to rapidly digest enormous amounts of data while producing insightful conclusions and predictions.
Are Generative AI and Large Language Models (LLM) the same thing?
Three primary drivers are credited with the growth of generative AI:
- Improvements in deep learning and neural networks
- More data availability for training models
- Potent GPUs that speed up the training.
The growth of computing power as well as improvements in hardware have made it feasible to train complex generative models with ease. The advancements in deep learning architectures such as transformers and GANs and that of deep learning models such as neural networks have been instrumental in the growth of generative AI. To be trained, generative AI models often need big, diverse datasets. The spread of the internet and digital technology has made enormous amounts of data accessible, allowing for the training of more complex generative models.
Given that the potential advantages of generative AI are starting to be understood, it is crucial that it be implemented into the BFSI (Banking, Financial Services, and Insurance) industry. With a predicted market cap of 126.5 billion dollars by 2031 (refer Fig 1), the GAI market is growing at a CAGR of 32% from 2022 to 2031.
A major challenge that the industry faces is the large volume of customer queries and support requests. Secondly, The detection and prevention of fraudulent actions is a persistent concern for the sector. To alleviate these problems, Large Language Models can help in the following ways:
- Fraud detection – Large language models can be trained using examples of fraudulent data and transactions. These trained models can discriminate between genuine and fraudulent patterns in transaction data. Unsupervised Learning Models are used
- Chatbots – Financial organizations can automate mundane and repetitive customer care jobs by using generative AI-powered chatbots, which eliminates the need for manual interaction. This automation reduces the need for human resources and boosts operational effectiveness, which results in cost savings.
- Predictive Analysis – Large language models can be used on large amounts of data for entity extraction and sentiment analysis which can be used further to forecast stock movements as well as portfolio allocation.
- Risk Management – Institutions may speed up decision-making, reduce turnaround times, and streamline risk assessment workflows by utilizing cutting-edge algorithms and artificial data. As a result, organizations can manage higher volumes of risk assessments without sacrificing precision or quality.
Large language models also offer various other potential applications in the finance industry. They are :
- News Summarization: LLMs may swiftly assimilate pertinent information and keep busy professionals up to date with the most recent developments by automatically summarizing financial news items, reports, and market updates.
- Compliance with rules: LLMs can assist financial organizations in keeping up with intricate and changing rules. They can examine legal records and offer information on compliance needs, assisting businesses in avoiding any fines.
- Trading algorithms: By examining past market data and looking for patterns that can point to lucrative trading opportunities, LLMs can help in the development of algorithmic trading techniques.
- Economic forecasting: With the capability of processing large amounts of data LLMs are able to analyze historical patterns and economic data to produce forecasts and projections that assist financial institutions and investors in making fact-based decisions.
Currently however, the use of generative AI in the banking industry is limited. As of now, AI is being used in operations like stress testing, market risks, and compliances on a lower scale. GAI is also being used for Loan decision making. With the help of AI, banks can use risk assessment to decide if a customer qualifies for credit lending. Banks can speed up and lower the cost of processing loans while increasing accuracy by automating the loan underwriting process. Another current application of generative AI is marketing. Generative AI is making it easier for marketers to achieve their objective of creating highly customized content at a larger scale. Banks can use generative AI to create offers that are catered to the specific requirements and preferences of each customer.
Conclusion
Generative AI has already begun to transform different areas of banking through its creative applications, from risk assessment and fraud detection to customized client interactions and investment strategies. But at the same time, it is crucial to recognize and deal with the difficulties raised by the application of generative AI in finance. To guarantee that these cutting-edge technologies are used responsibly and ethically, considerations of data privacy, security, and algorithmic bias must be provided. In conclusion, generative AI can revolutionize the finance sector and has the power to fundamentally alter how financial services are provided, evaluated, and used.
References
https://www.leewayhertz.com/generative-ai-in-finance-and-banking/
