Automatic detection of potentially illegal online sales of elephant ivory via data mining

In this work, we developed an automated system to detect potentially illegal elephant ivory items for sale on eBay. Two law enforcement experts, with specific knowledge of elephant ivory identification, manually classified items on sale in the Antiques section of eBay UK over an 8 week period. This set the “Gold Standard” that we aim to emulate using data-mining. We achieved close to 93% accuracy with less data than the experts, as we relied entirely on metadata, but did not employ item descriptions or associated images, thus proving the potential and generality of our approach. The reported accuracy may be improved with the addition of text mining techniques for the analysis of the item description, and by applying image classification for the detection of Schreger lines, indicative of elephant ivory. However, any solution relying on images or text description could not be employed on other wildlife illegal markets where pictures can be missing or misleading and text absent (e.g., Instagram). In our setting, we gave human experts all available information while only using minimal information for our analysis. Despite this, we succeeded at achieving a very high accuracy. This work is an important first step in speeding up the laborious, tedious and expensive task of expert discovery of illegal trade over the internet. It will also allow for faster reporting to law enforcement and better accountability. We hope this will also contribute to reducing poaching, by making this illegal trade harder and riskier for those involved

Predicting Auditors’ Opinions Using Financial Ratios and Non-Financial Metrics: Evidence from Iran

Purpose- The purpose of the paper is to investigate the extent to which a model based on financial and non-financial variables predicts auditors' decisions to issue qualified audit reports in the case of companies listed on the Tehran Stock Exchange (TSE). Design/methodology/approach- The authors utilized data from the financial statements of 96 Iranian firms as the sample over a period of five years (2012-2016). A total of 480 observations were analysed using a probit model through 11 primary financial ratios accompanying non-financial variables, including the type of audit firm, auditor turnover and corporate performance, which affect the issuance of audit reports. Findings- The results demonstrated high explanatory power of financial ratios and type of audit firm (the national audit organization vs. other local audit firms) in explaining qualifications through audit reports. The predictive accuracy of the estimated model is evaluated using a regression model for the probabilities of qualified and clean opinions. The model is reliable, with 72.9 percent accuracy in classifying the total sample correctly to explain changes in the auditor's opinion. Practical implications- The paper has practical implications and can assist auditors in identifying factors motivating audit report qualifications, mainly in emerging economies. Originality/value- The paper contributes to auditing research, since very little is known about the determinants of audit opinion in emerging markets including Iran; it also constitutes an addition to previous knowledge about audit opinion in the context of TSE. The paper is one of the rare studies predicting auditor opinions using both financial variables and non-financial metrics

Numeral Understanding in Financial Tweets for Fine-grained Crowd-based Forecasting

Numerals that contain much information in financial documents are crucial for financial decision making. They play different roles in financial analysis processes. This paper is aimed at understanding the meanings of numerals in financial tweets for fine-grained crowd-based forecasting. We propose a taxonomy that classifies the numerals in financial tweets into 7 categories, and further extend some of these categories into several subcategories. Neural network-based models with word and character-level encoders are proposed for 7-way classification and 17-way classification. We perform backtest to confirm the effectiveness of the numeric opinions made by the crowd. This work is the first attempt to understand numerals in financial social media data, and we provide the first comparison of fine-grained opinion of individual investors and analysts based on their forecast price. The numeral corpus used in our experiments, called FinNum 1.0 , is available for research purposes.Comment: Accepted by the 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2018), Santiago, Chil

Genetic Algorithm-based Feature Selection for Auditing Decisions

When examining a firm’s financial statements, independent auditors seek to render opinions on their fairness, accuracy, presence of fraud, and going concern, among others. This research focuses on the going concern, and the ability to predict when the going concern is flagged based on an array of accounting measures. It seeks to determine a parsimonious set of measures that can accurately predict when the going concern is raised, when using a linear kernel support vector machine for prediction. A genetic algorithm is employed to effectively reduce the set of measures without compromising accuracy of prediction. Using data from audits of public firms, a parsimonious model is created utilizing only 8 measures from a set of 35 available measures. The model exhibits 98.6% accuracy, and outperforms several other machine learning techniques