Personalized financial services powered by generative artificial intelligence

Generative Artificial Intelligence (Gen-AI) is making a significant impact in a wide variety of industries. Due to increasing business demands, improved cloud-based services, and advancements in model architecture and algorithms, interest in Gen-AI is surging. There are opportunities to offer personalized financial services using Gen-AI in several ways, including assisting regular investment and tax planning by quantizing a person's whole financial situation, dynamically providing news summaries and question-and-answer content related to investment and taxes, and offering personalized financial advice and templates for financial documents and contracts. The Gen-AI sector landscape includes a variety of tech firms providing significant AI services and solutions. This article aims to describe how Gen-AI can improve personalized financial services and to highlight potential problems and directions in application.

Generative AI (Gen-AI) has been rapidly adopted across various industries, including finance, since its debut as a prominent field in recent years. It has attracted a lot of attention from both individuals and institutions. There are many explanations for this hype of Gen-AI, including rapidly increasing business demands for this service and the convenience of cloud-native services enabling varied enterprises to incorporate large model services easily. Moreover, with the development of guiding model architectures and algorithms, Gen-AI has become more efficient, cheaper, easier to use, and more capable. As a result, current Gen-AI models wield great potential regarding providing personalized financial services. Gen-AI has been applied to a wide variety of financial services that are related to data or insight generations, especially portfolio-related services, which aim to analyze, model, and predict future stock trends or to better manage a person's wealth or risk. However, few efforts have researched how to use Gen-AI in personal financial services that are related to document generations, recommendations, or automatic content analysis. Hence, this article aims to give a relatively comprehensive picture of how Gen-AI works in enhancing personalized financial services.Instead of providing mere lists of candidates with analysis bricks of corresponding advantages and disadvantages, and relevant review articles, etc., a grasp approach to discover hidden financial solutions through personalized and targeted conversations, stepwise interactions with financial institutions, and summary collections of potential products. AI can be used to transform a big firm into a brand like Apple offered it for luxurious products with identities, moods, and feelings. Credit cards considered on-demand consumption scenarios, parsing pre-made queries segment wise are worth the investment. Institutions need to share the concerns of being persuasive and revealing instead of manipulative.

                            Fig 9.1: Generative Artificial Intelligence (AI) in Financial Services

9.2. Overview of Financial Services

                     Financial Services, also referred to as Financial Technology, fintech, or Info-Fin-Services Technology (FST), is a broad interdisciplinary subject. It encompasses a wide range of sectors and subsectors of companies and industries related by the shared use of technology to offer, help deliver, or facilitate financial services. The term typically refers to the company's use of sophisticated technology to carry out banking or trading services and offer financial advice or other information services. Fintech companies offer money transfer services, stock trading, lending, credit ratings, crowdfunding, analytic tools for investors, and even services such as tax preparation. Well-known fintech companies include Intuit, Wealthfront, Funding Circle, and Toast. Companies operating within finance-related sectors that use technology to drive innovation in financial services are also considered fintech, such as chipmakers, Payment Gateways, Blockchains, Crypto Exchanges, and Crypto Wallets. When restricted to companies in the pure software category, fintech refers to software platforms that facilitate banking and trading. Despite the availability of software that helps execute algorithmic trading strategies, this has remained on the periphery of the fintech industry. Major Wall Street players tend to prefer internal, proprietary solutions for seat management and order tracking across trading venues and desks. There are companies developed to help brokerages migrate their trading to suggestions or enable brokers to create automated telco and client contact platforms for regulated markets.

Currently, financial services powered by Generative AI for personalization are officially offering tailored user experience customers in the form of powers of customer care, and product suggestions. The new stretchy way to render financial services is to leverage insights from OpenAi with Google Search Laws and Information invader platforms to help customers find tailored credit cards, brokers, brands, loyalty programs, overall services, etc. in the formats of questions and answers.

9.3. Generative Artificial Intelligence: A Primer

                        This section provides basic information about generative artificial intelligence (GAI): its definitions, historical developments, limitations, and potential future roadmap. GAI is an artificial intelligence (AI) technology that enables today’s machines to create compelling narratives, image designs, songs, videos, and other outputs. Deep learning models are developed using much data from people to imitate their creations with incredibly high quality. As a result, GAI can replace people to program and design, write and brainstorm ideas, entertain and educate, and even love. GAI can disrupt the world as profoundly as the internet, spreadsheets, LLMs, and other past technological breakthroughs have done. This section begins with basic definitions of generative AI systems, LLMs, and web crawlers. It then discusses how GAI dramatically developed over the past years with Turing-like test-like studies. Additionally, this section highlights the limitations of generative AI technologies and details potential solutions for improvement in the future.

GAI is a class of software that learns to create new and novel data from existing training data. This capability encompasses the generation of text, images, audio, program code, and other content forms. There seem to be three essential pieces to GAI technologies. For GAI applications to succeed, they require a large amount of reasonable-quality training data, usually scraped from the web. General web crawlers search multiple websites and documents to obtain it. The larger and more diverse the training data, the better the GAI systems can mimic it. However, there are limits for the world’s most famous GAI systems, such as ChatGPT, to crawl data. To a large extent, GAI systems still deeply depend on data created by humans, especially data from less-known third-party firms, for which they need to pay the owners/creators of data.