Errors in Detecting Financial Deception: A Cognitive Modeling Approach

Organizational agents often need to determine whether business information provided under conflict of interest is intentionally misleading or false. Detecting strategically manipulated information is in general difficult and prone to failure. This research explores the errors made by 18 loan officers when examining misleading financial information. The process traces of the loan officers are compared with the behavior of a cognitive model of detection success derived from Social Contract Theory and the Theory of the Detection of Deception. The results show that one of the keys to successful detection is the ability to ‘coming to think’ about deception, i.e., the ability to begin interpreting perceived anomalies in the received information as generated by the sender’s malicious manipulations. The findings on the distribution of different error types are consistent with the theory, statistically significant, and pragmatically meaningful

Facilitating the Detection of Strategically Manipulated Information: A Field Test of Social Contract Theory

Individuals and organizations often need to detect whether the information provided by business partners is free from manipulations designed to favorably affect the behavior of the information recipients. The problem of detecting such manipulations is ubiquitous in organizations, has often high stakes, and is generally difficult to solve. A field experiment with 18 loan officers has tested two conditions derived from Social Contract Theory and designed to facilitate successful detection. The results show that knowledge of the information provider’s adversarial intentions and possible manipulations is a determinant of detection success

Looking Without Seeing: Understanding Unsophisticated Consumers\u27 Success and Failure to Detect Internet Deception

Do unsophisticated consumers fall prey to Internet consumer frauds? Why? To answer these questions this paper integrates two streams of empirical research: the process-oriented theory of deception, and the broader deception, trust, and risk (DTR) model of Internet consumer behavior. A laboratory experiment tests several alternative hypotheses about the determinants of failure at detecting Internet deceptions. The findings suggest that Internet consumers process the clues that a site may be deceptive, but are unable to effectively evaluate and combine these clues, i.e., to draw correct conclusions from them. This is good news in the ongoing struggle against Internet fraud because it suggests that consumers lack the knowledge, not the capacity, to detect decep- tions and that consumer education programs might be effective in helping consumers to protect themselves

A New ELISA Using the ANANAS Technology Showing High Sensitivity to diagnose the Bovine Rhinotracheitis from Individual Sera to Pooled Milk

Diagnostic tests for veterinary surveillance programs should be efficient, easy to use and, possibly, economical. In this context, classic Enzyme linked ImmunoSorbent Assay (ELISA) remains the most common analytical platform employed for serological analyses. The analysis of pooled samples instead of individual ones is a common procedure that permits to certify, with one single test, entire herds as "disease-free". However, diagnostic tests for pooled samples need to be particularly sensitive, especially when the levels of disease markers are low, as in the case of anti-BoHV1 antibodies in milk as markers of Infectious Bovine Rhinotracheitis (IBR) disease. The avidin-nucleic-acid-nanoassembly (ANANAS) is a novel kind of signal amplification platform for immunodiagnostics based on colloidal poly-avidin nanoparticles that, using model analytes, was shown to strongly increase ELISA test performance as compared to monomeric avidin. Here, for the first time, we applied the ANANAS reagent integration in a real diagnostic context. The monoclonal 1G10 anti-bovine IgG1 antibody was biotinylated and integrated with the ANANAS reagents for indirect IBR diagnosis from pooled milk mimicking tank samples from herds with IBR prevalence between 1 to 8%. The sensitivity and specificity of the ANANAS integrated method was compared to that of a classic test based on the same 1G10 antibody directly linked to horseradish peroxidase, and a commercial IDEXX kit recently introduced in the market. ANANAS integration increased by 5-fold the sensitivity of the 1G10 mAb-based conventional ELISA without loosing specificity. When compared to the commercial kit, the 1G10-ANANAS integrated method was capable to detect the presence of anti-BHV1 antibodies from bulk milk of gE antibody positive animals with 2-fold higher sensitivity and similar specificity. The results demonstrate the potentials of this new amplification technology, which permits improving current classic ELISA sensitivity limits without the need for new hardware investments

Learning to Generate Facial Depth Maps

In this paper, an adversarial architecture for facial depth map estimation from monocular intensity images is presented. By following an image-to-image approach, we combine the advantages of supervised learning and adversarial training, proposing a conditional Generative Adversarial Network that effectively learns to translate intensity face images into the corresponding depth maps. Two public datasets, namely Biwi database and Pandora dataset, are exploited to demonstrate that the proposed model generates high-quality synthetic depth images, both in terms of visual appearance and informative content. Furthermore, we show that the model is capable of predicting distinctive facial details by testing the generated depth maps through a deep model trained on authentic depth maps for the face verification task