A Framework for Institutional Risk Identification using Knowledge Graphs and Automated News Profiling

TL;DR

This paper presents an automated framework that leverages knowledge graphs and neural embeddings to identify, characterize, and assess risks from global news for organizations, reducing manual effort.

Contribution

It introduces a novel automated system combining knowledge graphs and multilingual news matching for proactive risk identification and impact assessment.

Findings

Successfully monitors global news for risk detection

Effectively characterizes and assesses risk proximity

Identifies operational areas most impacted by risks

Abstract

Organizations around the world face an array of risks impacting their operations globally. It is imperative to have a robust risk identification process to detect and evaluate the impact of potential risks before they materialize. Given the nature of the task and the current requirements of deep subject matter expertise, most organizations utilize a heavily manual process. In our work, we develop an automated system that (a) continuously monitors global news, (b) is able to autonomously identify and characterize risks, (c) is able to determine the proximity of reaching triggers to determine the distance from the manifestation of the risk impact and (d) identifies organization's operational areas that may be most impacted by the risk. Other contributions also include: (a) a knowledge graph representation of risks and (b) relevant news matching to risks identified by the organization utilizing a neural embedding model to match the textual description of a given risk with multi-lingual news.