Financial Knowledge Graph Based Financial Report Query System

Abstract

Annual Financial Reports are the core in the Banking Sector to publish its financial statistics. Extracting useful information from these complex and lengthy reports involves manual process to resolve the financial queries, resulting in delays and ambiguity in investment decisions. One of the major reasons is the lack of any standardization in the format and vocabulary used in the reports. An automated system for resolution of intelligent financial queries is therefore difficult to design.Several works have been proposed to overcome these problems using Information Extraction; however, they do not address the semantic interoperability of the reports across different institutions. This work proposed an automated querying engine to answer the financial queries using Ontology based Information Extraction. For Semantic modeling of financial reports, a Financial Knowledge Graph, assisted by Financial Ontology, has been proposed. The nodes are populated with entities, while links are populated with relationships using Information Extraction applied on annual reports. Two benefits have been provided by this system to stakeholders through automation: decision making through queries and generation of custom financial stories. The work can further be extended to other domains including healthcare and academia where physical reports are used for communication.

Automatic Generation Method for Tables with Merged Cells

“…Tables are a structured form used for organizing, presenting, and comparing data, widely used in business, academia, science, and daily life. A table is a form of data organization used for collecting, organizing, organizing, and analyzing data [5] . Cells are the basic elements that make up a table, which can contain text, numbers, and other content.…”

From data to insights: the application and challenges of knowledge graphs in intelligent audit

“…Hou et al [86] proposed an intelligent financial accounting and financial risk monitoring and early warning model based on knowledge graph and deep learning technologies, aiming to address the inefficiency, time consumption, and low level of intelligence in existing computerized financial data prediction systems. Additionally, Zehra et al [123] explored the construction of domain knowledge graphs and their application in financial auditing, elucidating the practical application of knowledge graphs in the auditing process. Therefore, knowledge graphs play an increasingly important role in identifying audit risks, as they help auditors effectively identify potential audit risks and abnormal behaviors In auditing, abnormal behavior refers to actions that significantly deviate from normal business or accounting processes, which may indicate errors or fraud.…”

“…Given the complexity and verbosity of financial reporting information, manual information extraction could diminish the accuracy and timeliness of investment decisions. Zehra et al [123] proposed a financial knowledge graph through semantic modeling of annual financial reports, using the mined information from the financial knowledge graph for decision-making. Hou et al [86] combined relevant financial reports and financial risk characteristics to construct an intelligent financial accounting model based on knowledge graphs.…”

Financial data analysis application via multi-strategy text processing

“…Cheng et al (Cheng et al 2020) propose to use KG-based event embeddings for quantitative investment by jointly training FinKGs, events, and relations and feeding them into models to derive investment strategies. Zehra et al (Zehra et al 2021) build a financial report query system to query annual financial reports to help investors in the banking sector. They extract information from annual reports to build KGs and use ontologies to help understand user queries.…”