As the implementation of large language models (LLMs) accelerates, enterprise-level application technologies are maturing rapidly. Across various industries, the feasibility of reforming existing business processes and production methods using AI to improve operational efficiency has significantly increased. The financial industry, as one of the representative data-intensive and fast-updating sectors, is often strongly tied to cutting-edge IT technologies and is at the forefront of enterprise-level technological updates.
Taking the currently popular enterprise knowledge base Q&A products as an example, major banks and securities firms have gradually begun to introduce AI technology to improve work efficiency. In early 2024, Postal Savings Bank of China started providing AI Q&A systems for front-line counter staff and plans to integrate it with credit platforms and business frontends within the year, expanding the system's scope of application. Agricultural Bank of China has applied for an intelligent Q&A method patent, enabling precise automatic learning to answer questions. Meanwhile, more small and medium-sized institutions are seeing the potential for business efficiency gains brought by AI, with enterprise digitalization needs being realized in a low-cost and convenient manner.
The winds of AI are blowing through the financial industry. Beyond generalities, we want to explore what role knowledge base Q&A products can play in actual business scenarios. To implement enterprise-level applications, what kind of product performance can current technology achieve?
Financial Knowledge Base Q&A in the LLM Era: More Than Just a "Knowledge Collection"
A knowledge base is a system for storing, organizing, and providing knowledge information, typically characterized by structured organization, easy access, dynamic updates, and multi-source integration.
The application of knowledge bases in the financial industry originated in the early stages of financial informatization. With the development of information technology, financial institutions began to realize the importance of effectively managing and utilizing information assets. Initially, the application of knowledge bases focused mainly on information collection and storage, aiming to improve retrieval efficiency and accuracy. Traditional knowledge bases achieved knowledge classification and retrieval through means such as keyword matching, but their construction and maintenance often required significant human and time costs, and still faced issues such as rigid rules and difficulties in knowledge extraction.
As financial business became more complex and financial products diversified, financial institutions needed to seek more advanced knowledge management and analysis tools. Knowledge bases began to integrate more complex information processing technologies, such as data mining, machine learning, and natural language processing.
In the LLM era, knowledge bases have become an essential component of the financial sector. Financial institutions use knowledge bases for research report interpretation, product recommendations, risk control, compliance checks, and more, helping professionals improve the accuracy and efficiency of decision-making. Currently, financial institutions are using LLM technology to build more complex and dynamic knowledge management systems to achieve in-depth mining and real-time analysis of industry information.
From the perspective of financial institutions and enterprise needs, businesses need to achieve: 1) Knowledge asset management: Automatically classify and manage document information for existing and newly acquired knowledge, reducing manual information organization costs; 2) Improved search efficiency: Quickly and accurately obtain and utilize specific domain knowledge and information, intelligently filtering out redundant information based on traditional search engines, integrating high-quality information to improve decision-making efficiency and quality; meanwhile, knowledge base Q&A products can proactively provide suggestions and related materials, helping professionals obtain effective information in a timely manner; 3) Communication assistance: Facing diverse questions in the process of communicating with customers and partners, knowledge base Q&A can provide powerful information support for front-line staff.
In actual business scenarios, knowledge base Q&A products can play the role of intelligent assistants, helping financial professionals obtain needed information and resources promptly. On one hand, the system can quickly query databases, retrieving detailed product information, saving time on manual searches and confirmations; on the other hand, it can also instantly access regulatory guidelines and policy documents, providing real-time support for risk and compliance aspects. Therefore, well-performing knowledge base products can significantly improve work efficiency, allowing professionals to devote more time and energy to business development and customer service, while enhancing work quality and compliance.
Is Document Parsing Capability Important for Knowledge Base Q&A Products?
In highly professional and knowledge-intensive fields like finance, the information sources for knowledge base Q&A products are diverse: information comes from real-time open internet sources, industry knowledge graphs, and enterprise closed-source knowledge bases. Announcements, financial reports, and research reports come in formats including PDF, Word, web pages, and images, including a large number of scanned documents that need to be processed by parsing tools for input into knowledge base storage for extraction and use.
On this issue, enterprise-level knowledge base Q&A products face the same challenges as current consumer-oriented large model Q&A products: how to achieve fast and accurate document parsing?
In the composition of financial knowledge base documents, institutional research reports, corporate financial reports, and annual reports account for a considerable proportion. These files often have complex layouts, making them difficult for machines to read. The document parsing process involves many technical challenges, including complex page structures, multiple document elements, headers and footers, multi-column layouts, borderless tables, and merged cells. Taking annual reports and research reports as examples:
- Bordered, borderless tables, and merged cells: Various complex table forms in scanned files pose recognition and reconstruction challenges for document parsing.
2. Multi-column layouts: Common in research reports and web page scraping results, requiring document parsing to restore the correct reading order.
3. Header and footer formats: Headers and footers may contain various forms and content, and in some cases, may include a large number of annotations that need to be accurately identified and distinguished from the main text.
So, how do current consumer products perform in this area? And how do differences in document parsing tools affect the performance of Q&A products?
We conducted a simple test.
First, we uploaded a PDF version of a corporate annual report to a popular domestic general-purpose large model Q&A consumer product and asked a common question in the field of financial analysis: Please introduce the company's asset structure.
The large model provided introductions from multiple aspects, but the answer was relatively general and did not include specific data information.
We then tried replacing the document parsing tool, uploading the PDF file to the TextIn platform for parsing, and uploading the parsed Markdown file to ask the large model the same question. This time, the large model provided data information such as asset scale and net assets.
We returned to the original annual report document for verification to rule out hallucinations. In the following table, we can see that after changing the parsing tool, the large model's answer came from the table data in the annual report, and the information was accurate.
In such cases, the performance of document parsing tools has a significant visible impact on the performance of Q&A products. Compared to current products, enterprise-level financial knowledge base products require higher efficiency and accuracy, with lower fault tolerance, which also means that from parsing to retrieval and recall, the technical requirements for each module of the product will be further increased.
TextIn document parsing is characterized by speed, accuracy, and strong compatibility, providing powerful support for enterprise knowledge base product development, ensuring the important links of knowledge base construction and data updates, allowing development work to proceed without "worries."
Easy Trial of TextIn Document Parsing
TextIn's document parser has been launched on the TextIn platform, and any developer can register an account and activate it for use.
Our PDF to Markdown Paser now offers a free trial quota of 1000 pages of PDF, which can be claimed by joining our discord group. We welcome everyone to communicate more with our team and provide opinions or suggestions.
