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.

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.…”