AI search engines have evolved beyond knowledge retrieval, now providing real-time financial guidance in response to growing demands for personalisation. The financial industry’s AI spending is projected to grow substantially, with estimates reaching US$97 billion by 2027, reflecting a compound annual growth rate (CAGR) of 29 per cent.
The early role of AI search engines
Whether users are seeking basic scientific facts or complex legal definitions, AI has provided an accessible, efficient resource for information in the context of search queries. However, early tools had clear limitations, delivering static information without the depth or adaptability needed for critical decision-making.
The rapid and sometimes unpredictable nature of financial markets revealed the limitations of AI’s initial approach, which struggled to keep pace with market demands. This led to a push for more adaptable AI capabilities, marking its progression into a specialised tool that responds to unique needs for timely, actionable information.
Wharton professor Chris Geczy emphasised this dual-edged potential of AI, saying, “The current state of artificial intelligence puts us at the edge of something wonderful, something terrible, or both. Developers, regulators, and other stakeholders are responsible for guiding the further development of AI in socially and economically beneficial ways.”
Transition to personalised AI for financial decision-making
AI search engines have evolved to cater to nuanced use cases such as tailored financial guidance. From delivering market forecasts to investment strategies, AI has transitioned into a role once filled by human advisors. In 2023, around 40 per cent of financial institutions used generative AI to enhance efficiency, customer service, and decision-making processes. As of November 2024, approximately 43 per cent of financial institutions have integrated generative AI into their operations, reflecting a growing trend in the sector.
AI’s capacity to assess dynamic financial data and offer personalised recommendations has significant appeal, including data-driven advice on high-potential assets, optimal portfolio distribution, or hypothetical investment outcomes.
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By incorporating real-time event monitoring, natural language processing (NLP), and multi-source data integration, AI enhances its ability to deliver timely, comprehensive, and user-friendly insights, making it an invaluable tool for financial planning and decision-making.
Real-world examples of AI-driven financial assistance
ChatGPT
Known for its advanced conversational abilities, ChatGPT can be an insightful tool for finance. Users can ask complex financial questions, such as finding the high-potential tech stocks for 2025 or understanding how inflation trends affect their investment portfolio. While ChatGPT has not yet integrated real-time data, it excels in scenario-based analysis based on provided numbers and uploaded images of market price action movements.
According to a report from Demand Sage, ChatGPT boasts over 200 million weekly active users as of November 2024, reflecting its widespread adoption. The report also says that more than 92 per cent of Fortune 500 companies utilise ChatGPT.
Perplexity
An AI search engine designed to answer queries with a high degree of contextual relevance, Perplexity offers immediate and relevant responses that aid in financial decision-making. Its large language model (LLM) provides targeted answers that combine context-based insights with a wide data scope. Users can ask for insights on economic trends and shifts or sector-specific market dynamics.
Perplexity can then provide a response that combines current and historical data. Furthermore, it allows users to probe deeper into financial topics, offering follow-up answers and clarifying explanations.
It is finalising a US$500 million funding round led by Institutional Venture Partners, valuing the AI-powered search company at US$9 billion.
Node Search
Node Search is a decentralised search engine designed by Nodepay to deliver highly accurate, real-time results tailored to the Web3 domain. Powered by a Retrieval-Augmented Generation (RAG) infrastructure, it combines data from web crawling, real-time updates, and indexed Web3 knowledge bases to ensure users receive the most relevant and up-to-date information. By integrating LLMs, Node Search handles complex queries, providing precise insights on token prices, sentiment analysis, project updates, and blockchain advancements.
Node Search processes queries through a multi-step system: web crawling via bandwidth nodes and APIs, integration of real-time data, and enhancement with vectorised knowledge bases. Advanced AI models analyse the information to deliver precise, data-backed insights.
With Nodepay having over 885,000 active participants, 750,000 nodes across 180 countries, and partnerships with firms like Mirana Capital, NGC Ventures, and Animoca Brands, Node Search supports blockchain investors by providing secure, transparent information tailored for digital asset management. This setup appeals to users who want to prioritise reliability and innovation when navigating the Web3 landscape.
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Users actively contribute to powering Node Search by operating bandwidth nodes, which facilitate web crawling and process real-time data. These participants are rewarded with digital assets, creating a decentralised ecosystem that emphasises user ownership and community-driven innovation.
Conclusion
AI search engines have rapidly evolved from basic repositories of general information into sophisticated financial advisors. Today’s tools offer real-time insights, personalised financial advice, and enhanced user engagement.
This progression aligns with the growing demand for individualised decision-making support. AI search engines are becoming invaluable in personal financial planning, offering tools that align with the needs of both novice and advanced users’ financial objectives.
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