Data search and discovery process for financial statement fraud

Financial fraud in the form of significant public interest, media, investors, financial community and legislators to get oneself and for several large companies such as Enron fraud is known, Lucent and WorldCom have occurred in the past years. Fraudulent financial reporting in more than declarative through asset sales and profits and low self liabilities, costs and losses occur (Yue et al., 2007). Fraud, an important reason for the failure of many companies and particularly damaging to capital markets because investors, creditors and financial analysts in their decisions on the financial statements available to the public, they rely on and trust. Detect fraud in financial statements, it is difficult and complex issues discovered (Yue et al., 2007). The main objective of this paper is to provide an overview of data mining processes used to detect financial fraud, particularly fraud in the financial statements. Keywords:Mining, fraud, financial statements, forecasting, regression, artificial neural network

Predicting business failure using artificial intelligence system

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonPredicting business insolvency is considered one of the main supportive sources of information for decision making for financial institutions, investors, creditors, and other participants in the business market. Financial reporting systems provide relevant information that can be used to assess the financial position of firms. It is crucial to have classification and prediction models that can analyse this financial information and provide accurate assurance for users about business health. Recent studies have explored the use of machine learning tools as substitute for traditional statistical methods to develop classification models to classify firm insolvency according to financial statement information. However, these models have no ideal classifier, since each provides a certain percentage of wrong outputs, which is a crucial consideration; every percentage of wrong response can mean massive financial losses for stakeholders. Therefore, this study proposes new insolvency classification and perdition models based on machine learning modelling techniques to develop an improved classifier. Individual modelling techniques using statistical methods and machine learning were used to develop the classification model of business insolvency. The results showed that machine learning method outperformed statistical methods. Deep Learning (DPL) achieved the highest performance based on all performance measurements used in the study, and it was the best individual classifier, with average accuracy of 97.2% using all-years dataset. Ensemble- Boosted Decision Tree classifier ranked second, followed by Decision Tree classifier. Thus, it has been proven that DPL modelling approach is useful for business insolvency classification. A key contribution in enhancing individual classifier outputs is the use of traditional combining methods with two new aggregation methods in business insolvency (Fuzzy Logic and Consensus Approach). The Consensus Approach showed the best improvement in the results of all individual classifiers with average accuracy of 97.7%, and it is considered the best classification method not only in comparison with individual classifiers, but also with traditional combiners. This study pioneers the development of a time series business insolvency prediction model with Big Data for UK businesses. The aim of the model is to provide early prediction about a business health. Three prediction models were developed based on Nonlinear Autoregressive with Exogenous Input models (NARX), Nonlinear Autoregressive Neural Network (NAR), and Deep Learning Time-series model (DPL-SA) and achieved average accuracy rates of 83.6%, 89.5%, and 91.35%, respectively. The results show relatively high performance in comparison with the best individual classifier (deep learning)

Modeling Financial Time Series with Artificial Neural Networks

Financial time series convey the decisions and actions of a population of human actors over time. Econometric and regressive models have been developed in the past decades for analyzing these time series. More recently, biologically inspired artificial neural network models have been shown to overcome some of the main challenges of traditional techniques by better exploiting the non-linear, non-stationary, and oscillatory nature of noisy, chaotic human interactions. This review paper explores the options, benefits, and weaknesses of the various forms of artificial neural networks as compared with regression techniques in the field of financial time series analysis.CELEST, a National Science Foundation Science of Learning Center (SBE-0354378); SyNAPSE program of the Defense Advanced Research Project Agency (HR001109-03-0001

FINANCIAL STATEMENT FRAUD DETECTION USING TEXT MINING: A SYSTEMIC FUNCTIONAL LINGUISTICS THEORY PERSPECTIVE

Fraudulent financial information made by public companies not only cause significant financial loss to broad shareholders but also result in a great loss of confidence to capital market. Conventional auditing practices, which primarily focus on statistical analysis of structured financial ratios in auditing process, work not so well with the presence of misleading financial reports. This research tries to tap the power of huge amount of largely ignored textual contents in financial statements. With the theoretical guidance of Systemic Functional Linguistics theory (SFL), we develop a systematic text analytic framework for financial statement fraud detection. Seven information types, i.e., topics, opinions, emotions, modality, personal pronouns, writing style, and genres are identified based on ideational, interpersonal, and textual metafunctions in SFL. Under the analytic framework, Latent Dirichlet Allocation algorithm, computational linguistics, term frequency-inverse document frequency method, are integrated to create a synergy for extracting both word-level and document-level features. All these features serve as the input of Liblinear Support Vector Machine classifier. Finally, with application to detect fraud in 1610 firm-year samples from U.S. listed companies, the analytic framework makes a classification with average accuracy at 82.36% under ten-fold cross validation, much better than baseline method using financial ratios

Detection of fraudulent financial papers by picking a collection of characteristics using optimization algorithms and classification techniques based on squirrels

To produce important investment decisions, investors require financial records and economic information. However, most companies manipulate investors and financial institutions by inflating their financial statements. Fraudulent Financial Activities exist in any monetary or financial transaction scenario, whether physical or electronic. A challenging problem that arises in this domain is the issue that affects and troubles individuals and institutions. This problem has attracted more attention in the field in part owing to the prevalence of financial fraud and the paucity of previous research. For this purpose, in this study, the main approach to solve this problem, an anomaly detection-based approach based on a combination of feature selection based on squirrel optimization pattern and classification methods have been used. The aim is to develop this method to provide a model for detecting anomalies in financial statements using a combination of selected features with the nearest neighbor classifications, neural networks, support vector machine, and Bayesian. Anomaly samples are then analyzed and compared to recommended techniques using assessment criteria. Squirrel optimization's meta-exploratory capability, along with the approach's ability to identify abnormalities in financial data, has been shown to be effective in implementing the suggested strategy. They discovered fake financial statements because of their expertise

Optimal financial structure, bankruptcy risk and the right to a new beginning

Starting from the need to optimize the financial structure of the enterprise, the article aims to review a few concepts related to financial structure and bankruptcy risk, the presentation of the bankruptcy risk analysis based on assets balance sheet, liquidity ratios and last, but not least, scoring method. It also presents some points of view regarding the current economic crisis and the evolution of national and international level approaches about bankruptcy and the risk of bankruptcy.financial structure, bankruptcy risk, liquidity ratios

Explainable Artificial Intelligence and Causal Inference based ATM Fraud Detection

Gaining the trust of customers and providing them empathy are very critical in the financial domain. Frequent occurrence of fraudulent activities affects these two factors. Hence, financial organizations and banks must take utmost care to mitigate them. Among them, ATM fraudulent transaction is a common problem faced by banks. There following are the critical challenges involved in fraud datasets: the dataset is highly imbalanced, the fraud pattern is changing, etc. Owing to the rarity of fraudulent activities, Fraud detection can be formulated as either a binary classification problem or One class classification (OCC). In this study, we handled these techniques on an ATM transactions dataset collected from India. In binary classification, we investigated the effectiveness of various over-sampling techniques, such as the Synthetic Minority Oversampling Technique (SMOTE) and its variants, Generative Adversarial Networks (GAN), to achieve oversampling. Further, we employed various machine learning techniques viz., Naive Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Gradient Boosting Tree (GBT), Multi-layer perceptron (MLP). GBT outperformed the rest of the models by achieving 0.963 AUC, and DT stands second with 0.958 AUC. DT is the winner if the complexity and interpretability aspects are considered. Among all the oversampling approaches, SMOTE and its variants were observed to perform better. In OCC, IForest attained 0.959 CR, and OCSVM secured second place with 0.947 CR. Further, we incorporated explainable artificial intelligence (XAI) and causal inference (CI) in the fraud detection framework and studied it through various analyses.Comment: 34 pages; 21 Figures; 8 Table

Detecting and Combating Fraudulent Health Insurance Claims Using ANN

This work was funded by the National Nature Science Foundation of China (71774069), 2014 “Six Talent Peaks” Project of Jiangsu Province (2014- JY-004) Abstract While governments and private sector stakeholders are taking steps to improve the access and quality of health care service to its citizenry, a lot of resources are lost every year due to fraudulent health insurance claims. The aim of this paper is to explore a more robust and accurate ways of predicting fraudulent health insurance claims by the use of artificial neural network (ANN). Using the fraud diamond theory (FDT)’s fraud elements as fraud indicators, a fraud prediction model was created to determine whether a claim presented by a subscriber (individual) is fraudulent or non-fraudulent by varying severally the number of epoch, hidden layer number and threshold of the artificial neural network on a 14 input data to obtain an optimal parameter for the model.The model was able to predict accurately 98.98% with an MSE of 0.0086, which outperformed other artificial neural network (ANN) methods used to predict fraudulent health care claims. The incorporation of the capacity indicator of the fraud diamond theory (FDT) makes this model a tool not only for prediction but also pre-empting the occurrence of fraud. This study is the first to adopt the fraud diamond theory’s fraud elements as fraud indicators together with artificial neural network (ANN) in predicting fraudulent health insurance claims. Keywords: health insurance claim, ANN, fraud prediction model, fraud diamond theor