# Financial analysis {% embed url="" %} DKGCON 2023 {% endembed %} Wes Levitt from Alpha Transform Holdings discussed the development of Chat Analyst, a product designed to revolutionize financial services by leveraging the Decentralized Knowledge Graph (DKG). The team, with a background in traditional finance and cryptocurrency, aims to address challenges in financial analysis related to data access, quality, and analysis, though not personnel expertise. The core issue [**Chat Analyst**](https://www.chatanalyst.ai/) tackles is the provenance and quality of financial data, crucial in avoiding AI-generated inaccuracies or "hallucinations" that can mislead or damage reputations. Levitt emphasized the importance of data reliability, especially in financial services where precision is paramount. Chat Analyst is built as a Web3 application using OriginTrail's DKG to establish data provenance. It allows for the verification of information sources through a unique NFT on a blockchain, ensuring data immutability and traceability. This setup addresses the critical need for accurate, sourceable information in financial analysis by providing a transparent trail of data provenance. The demo presented showcased Chat Analyst's ability to process complex queries, like summarizing Tesla's current risk factors, by canvassing indexed knowledge assets and generating responses with clear source citation. This contrasts sharply with traditional search engines or AI chatbots that often lack source verification, making Chat Analyst a valuable tool for financial analysts and investment professionals seeking dependable data.
Looking forward, Alpha Transform Holdings plans to expand the range of information included in Chat Analyst to encompass all SEC information and eventually all global financial information. This expansion aims to create the most powerful search tool for financial services, supporting a range of applications from investment analysis to robo-advisory services and investment relations. Levitt concluded by highlighting the transformative potential of Chat Analyst in the financial sector, inviting companies interested in piloting the solution to reach out for collaboration. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://tracdeepdive.gitbook.io/deepdive/overview/financial-analysis.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.