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Generative AI is a hot topic in tech, with applications like ChatGPT capturing the public’s attention. Simply put, Generative AI is a powerful tool that can create new content and ideas, from conversations to narratives, visuals, videos, and music.
At its core, generative AI relies on machine learning models and large language models (LLMs). LLMs are pre-trained on massive amounts of data and are often called foundation models (FMs).
In this blog, we’ll explore the impact of Generative AI on the financial services sector, shed light on some typical use cases of Generative AI in the financial sector, and learn how AWS services/tools are helping financial sector players to implement generative AI applications.
The General Trend in the Financial Industry
The financial services industry is rapidly ramping up its use of generative AI. Generative AI is being used in the financial services industry for a variety of purposes, including:
- Fraud detection: Generative AI can be used to create synthetic data that can be used to train fraud detection models. This feature can help to improve the accuracy of fraud detection systems and reduce the number of false positives.
- Risk management:
Generative AI can create simulations of different financial scenarios. This feature can help financial institutions to assess and manage their risks more effectively.
- Product development:
Generative AI can be used to develop new financial products and services. For example, generative AI can create personalized investment portfolios or develop new insurance products.
- Customer service:
Generative AI can be used to develop chatbots that can provide customer support 24/7. This feature can help financial institutions to improve their customer service experience and reduce costs.
Some of the leading financial institutions that are already using generative AI include:
- JPMorgan Chase:
JPMorgan Chase uses generative AI to develop new fraud detection models and improve its risk management capabilities.
- Bank of America:
Bank of America uses generative AI to develop new financial products and services, such as personalized investment portfolios.
- Goldman Sachs:
Goldman Sachs is using generative AI to develop new trading strategies and to improve its customer service capabilities.
The use of generative AI in the financial services industry is still in its early stages but is rapidly growing. As generative AI technology develops, we can expect to see even more innovative and groundbreaking applications in this industry.
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Generative AI in financial services: four use cases
Generative AI (LLMs) is a powerful new technology with the potential to revolutionize the financial services industry. Here are four specific use cases where Generative AI has a role:
1. Employees can focus on productive tasks
LLMs can help knowledge workers, such as financial analysts and legal professionals, become more efficient and effective. For example, LLMs can be used to summarize large amounts of text or data, identify key trends, and generate reports. This use case helps free knowledge workers from mundane tasks and focus on more strategic tasks.
2. Gain insights into market and customer behavior
LLMs can be used to analyze large amounts of data, such as social media posts and news articles, to identify market trends and customer sentiment. Such information is helpful to make better investment decisions and develop more targeted marketing campaigns.
3. Enhancing customer engagement
LLMs can be used to create more natural and engaging conversational AI experiences for customers. For example, LLMs can be used to power chatbots that can answer customer questions more comprehensively and informally. LLMs can also personalize the customer experience by tailoring the conversation to the customer’s needs and preferences.
4. Impetus for product innovation and business automation processes
LLMs can be used to generate new product ideas and automate business processes. For example, LLMs can be used to create personalized investment portfolios or automate insurance policies’ underwriting process.
These are just a few examples of generative AI employed in the financial services industry. As the technology continues to develop, we can expect to see even more innovative and groundbreaking applications in this industry.
How AWS Services Can Speed up Building Generative AI Applications for Financial Services
The following AWS services play a vital role in helping businesses and the financial services sector build generative AI applications.
Amazon SageMaker proves to be an asset for the advancement of generative AI. Developed by Amazon, this platform provides developers with a user-friendly solution for effortlessly constructing, training, and deploying models. In addition to this, Amazon offers access to an extensive array of artificial intelligence (AI) and machine learning (ML) services, which empower the financial services sector to seamlessly integrate AI functionalities such as image recognition, forecasting, and intelligent search into their applications through a straightforward API call.
Presently, pioneers in the financial services domain and numerous startups and government agencies worldwide rely on Amazon’s tools. These tools play an instrumental role in harnessing the potential of AI and ML, driving phenomenal change within their organizations, industries, and missions. Interestingly, AWS SageMaker Studio is a tool that provides multiple services under a single User Interface, helping the Machine Learning model build and fine-tune parameters. You can explore AWS SageMaker Studio more from one of our blogs.
AWS Bedrock significantly eases the journey of generative AI application development by simplifying scalability, offering API-driven development, enabling data customization, and seamlessly integrating with familiar AWS tools. As a result, developers can focus more on innovation and less on infrastructure management, ultimately accelerating the deployment of scalable, reliable, and secure generative AI applications. Those of you who are eager to learn more about AWS Bedrock can read one of our earlier blog posts for insightful information.
AWS CodeWhisperer tool helps developers write code for generative AI applications more quickly and easily. It provides real-time code suggestions based on a massive code database. This can help developers to overcome coding challenges, navigate unfamiliar APIs, and speed up the development process.
With CodeWhisperer, developers can focus on creating innovative and groundbreaking generative AI solutions without worrying about the nitty-gritty of coding. Learn more about this interesting AWS tool called CodeWhisperer and its abundant potential from an exclusive blog featuring it.
AWS Trainium represents the next generation of machine learning (ML) accelerators custom-designed by AWS to train deep learning models with over 100 billion parameters. Despite the increasing prevalence of deep learning, numerous development teams face budget constraints, which limit the extent and frequency of necessary training to enhance their models and applications. EC2 Trn1 instances, powered by Trainium, tackle this obstacle by significantly reducing the time required for training and offering up to a 50% reduction in training costs compared to equivalent Amazon EC2 instances. Trainium is meticulously fine-tuned for training in natural language processing, computer vision, and recommender models, all of which play pivotal roles in a wide array of applications, including text summarization, code generation, question answering, image and video generation, recommendation systems, and fraud detection.
AWS Inferentia accelerators, developed by AWS, offer exceptional performance at the most competitive prices for deep learning (DL) inference tasks. The initial AWS Inferentia accelerator, featured in Amazon Elastic Compute Cloud (Amazon EC2) Inf1 instances, provides up to 2.3 times greater throughput and reduces inference costs by up to 70% compared to similar Amazon EC2 instances.
Amazon EC2 Inf2 instances, powered by Inferentia2, are tailored to deliver outstanding performance while maintaining cost-efficiency for DL inference and generative artificial intelligence applications. These instances are optimized to efficiently handle increasingly complex models, including large language models and vision transformers, even at scale.
Financial services institutions must understand generative AI, compare different foundation models, and experiment with domain adaptation and model customization. CloudThat makes it easy and practical for customers to explore and use generative AI in their businesses.
You can always consult our experts for all your AI/ML service needs. Also, you can upskill your workforce on AWS SageMaker and AWS Machine Learning capabilities through our AWS-authorized certification training programmes.
Hope this helps you to get an insight on use cases of Generative AI & Role of AWS tools in implementing Financial Services.
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WRITTEN BY Lakhan Kriplani