SPot: A tool for identifying operating segments in financial tables

TL;DR

SPot is an automated tool that uses a Bidirectional RNN classifier to identify operating segments and their performance indicators in financial reports, aiding credit analysis and benchmarking.

Contribution

The paper introduces SPot, a novel machine learning-based system for detecting operating segments in financial tables without relying on taxonomy-based methods.

Findings

Successfully identifies operating segments in earnings reports

Provides an interactive interface for analysis and adjustment

Enhances credit monitoring and benchmarking capabilities

Abstract

In this paper we present SPot, an automated tool for detecting operating segments and their related performance indicators from earnings reports. Due to their company-specific nature, operating segments cannot be detected using taxonomy-based approaches. Instead, we train a Bidirectional RNN classifier that can distinguish between common metrics such as "revenue" and company-specific metrics that are likely to be operating segments, such as "iPhone" or "cloud services". SPot surfaces the results in an interactive web interface that allows users to trace and adjust performance metrics for each operating segment. This facilitates credit monitoring, enables them to perform competitive benchmarking more effectively, and can be used for trend analysis at company and sector levels.