Cases In Financial Reporting 8th Edition Solutions Pdf
Michelle Benitone
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For example, Banco Bilbao Vizcaya Argentaria, S.A. (BBVA), a global banking leader, announced plans to explore the potential of advanced technologies, like Amazon Bedrock, a new service that makes FMs from Amazon and leading AI startups accessible via an API, to create innovative financial solutions.
Earlier this year, Goldman Sachs started experimenting with generative AI use cases, like classification and categorization for millions of documents, including legal contracts. While traditional AI tools can help solve for these use cases, the organization sees an opportunity to use LLMs to take these processes to the next level. JPMorgan also recently announced that it is developing a ChatGPT-like software service that helps selecting the right investment plans for the customers.
We are truly at an exciting inflection point in the widespread adoption of ML, but as leaders in the financial services industry move forward, they will need to define the problems they want to solve using generative AI and establish a cloud strategy to enable generative AI opportunities.
In this blog, we focus on a handful of generative AI use cases for the financial services industry, how AWS enables customers to quickly build and deploy generative AI applications at scale, and how to get started with generative AI at AWS.
Across banking, capital markets, insurance, and payments, executives are eager to understand generative AI and applicable use cases, and developers want to experiment with generative AI tools that are easy to use, secure, and scalable. Below we explore four use case categories where generative AI can be applied in the financial services industry.
LLMs can improve employee productivity through more intuitive and human-like accurate responses to employee queries, for example an HR-bot that can answer HR related questions. They also can create more capable and compelling conversational AI experiences for external customer service applications, such as call center assist functionality that provides agents with automated assistance, contextual recommendations, and next best actions. Without LLMs, questions would typically have to be anticipated and a fixed set of answers would have to be created in advance by human authors. Whereas, with LLMs, answers can be generated on the fly and, as new information becomes available, it can be incorporated automatically into the answers provided.
Today, financial services institutions leverage ML in the form of computer vision, optical character recognition, and NLP to streamline the customer onboarding and know-your-customer (KYC) processes. Generative AI can help firms deliver flexible and relevant conversations that improve the overall customer experience, like adapting the conversational style to match that of the customer (for example, casual conversation mode or formal conversation mode).
With LLMs, firms can automatically translate complex questions from internal users and external customers into their semantic meaning, analyze for context, and then generate highly accurate and conversational responses. Specifically, LLMs enable long-form answers to open-ended questions (e.g., search thousands of pages of legal or technical documentation and summarize the key points that answer the question).
Data captured from customer interactions, such as call transcriptions and chatlogs, can also be summarized and analyzed for sentiment to more easily understand the themes associated with positive or negative customer experiences. Similarly, themes of interest to individual customers and context of prior conversations can be summarized and incorporated to enhance an omni-channel approach and deliver unified brand experience for customers.
Knowledge workers will evolve their focus from searching for, aggregating, and summarizing key sections of text and images to checking the accuracy and completeness of answers provided by generative AI models.
This use case has application for many job roles, including financial advisors and analysts preparing investment recommendations, compliance analysts responding to the impact of new regulations, loan officers drafting loan documentation, underwriters crafting insurance policies, and salespeople preparing RFI responses. In all these cases, the human professional can retain edit rights and final say, and be able to shift focus to other more value-add activities.
The ability to track event-driven news exists today, and many hedge funds and quants have developed ways to trade the markets based on signals from news and social media sentiment, confidence, and story counts.
However, traditional event-driven investment strategies and surveillance methodologies rely on mining for known behavior and patterns. Generative AI has potential to surface new themes and associated sentiment without direction. For instance, LLMs can identify new trends in consumer behavior from social media content by clustering posts with similar meaning and assigning the clusters an aggregate measure of sentiment. Similarly, negative sentiment associated with specific content, such as a new advertising campaign, can quickly be identified and summarized. Investors and enterprises can then respond promptly to this information.
Generative AI can also rapidly and efficiently produce data products from textual data sources that are only lightly used today. For instance, annual reports and filings (such as 10-Ks filed with the SEC in the United States) are primarily used as a source for financial statements. Buried in text of these documents is data that could power a product catalog or a customer and supply-chain relationship map across all or most public companies globally. Generative AI can create these types of data products at a fraction of the cost that it would take to extract this information manually or with traditional NLP processes. In past blogs, we have described how LLMs can be fine-tuned for optimal performance on specific document types, such as SEC filings.
Annual reports are just one, albeit an important, source that can feed data products. Unstructured data (mostly text) is estimated to account for 80%-90% of all data in existence. Generative AI is well suited to transform these large repositories of written and spoken word into on-demand structured or semi-structured information that can power investment processes and retail investor interactions. Investment research, investor presentations, earnings call transcripts, broadcast news and interviews, newspapers, trade journals, and websites are examples of content sources which, when searched comprehensively and appropriately summarized, can provide targeted intelligence of value to investors, such as pricing trends or consumer preferences for particular products or product areas.
AI and ML have been a focus for Amazon for over 20 years, and many aspects of the Amazon customer experience are informed or driven by ML, including our eCommerce recommendations engine; the paths that optimize robotic picking routes in our fulfillment centers; and our supply chain, forecasting, and capacity planning.
For example, we developed Amazon SageMaker, an easy way for all developers to build, train, and deploy models. We also offer access to a wide range of artificial intelligence (AI) and ML services that enable the financial services industry to add AI capabilities like image recognition, forecasting, and intelligent search to applications with a simple API call. Today, financial services leaders like NatWest, Vanguard, and PennyMac, as well as thousands of startups and government agencies around the world, use our tools to help them leverage AI and ML to transform and advance their organizations, industries, and missions.
We take the same democratizing approach to generative AI in financial services, making it easy, practical, and cost-effective for customers to use in their business across all the three layers of the ML stack, including: infrastructure, tools, and purpose-built AI services. Our approach to generative AI is to invest and innovate across the ML stack to take this technology out of the realm of research and make it available to customers of any size and developers of all skill levels.
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