Predicting merchant future performance using privacy-safe network-based features.

Small and Medium-sized Enterprises play a significant role in most economies by contributing to job creation and economic growth. A majority of such merchants rely on business financing, and thus, financial institutions and investors need to assess their performance before making decisions on business loans. However, current methods of predicting merchants' future performance involve their private internal information, such as revenue and customer base, which cannot be shared without potentially exposing critical information. To address this problem, we first propose a novel approach to predicting merchants' future performance using credit card transaction data. Specifically, we construct a merchant network, regarding customers as bridges between merchants, and extract features from the constructed network structure for prediction purposes. Our study results demonstrate that the performance of machine learning models with features extracted from our proposed network is comparable to those with conventional revenue- and customer-based features, while maintaining higher privacy levels when shared with third-party organizations. Our approach offers a practical solution to privacy concerns over data and information required for merchants' performance prediction, enabling safe data-sharing among financial institutions and investors, helping them make more informed decisions on allocating their financial resources while ensuring that merchants' sensitive information is kept confidential.

A link prediction-based recommendation system using transactional data.

Recommending relevant items to users has become an important task in many systems due to the increased amount of data produced. For this purpose, transaction datasets such as credit card transactions and e-commerce purchase histories can be used in recommendation systems to understand underlying user interests by exploiting user-item interactions, which can be a powerful signal to perform this task. This study proposes a link prediction-based recommendation system combining graph representation learning algorithms and gradient boosting classifiers for transaction datasets. The proposed system generates a network where nodes correspond to users and items, and links represent their interactions. A use case scenario is examined on a credit card transaction dataset as a merchant prediction task that predicts the merchants where users can make purchases in the next month. Performances of common network embedding extraction techniques and classifier models are evaluated via various experiments conducted and based on these evaluations, a novel system is proposed, and a matrix factorization-based alternative recommendation method is compared with the proposed model. The proposed method has shown superior performance to the alternative method in terms of receiver operating characteristic curves, area under the curve, and mean average precision metrics. The use of transactional data for a recommendation system is found to be a powerful approach to making relevant recommendations.

Changes in EEG complexity with neurofeedback and multi-sensory learning in children with dyslexia: A multiscale entropy analysis.

Multiscale entropy analysis (MSE) is a novel entropy-based approach for measuring dynamical complexity in physiological systems over a range of temporal scales. MSE has been successfully applied in the literature when measuring autism traits, Alzheimer's, and schizophrenia. However, until now, there has been no research on MSE applied to children with dyslexia. In this study, we have applied MSE analysis to the EEG data of an experimental group consisting of children with dyslexia as well as a control group consisting of typically developing children and compared the results. The experimental group comprised 16 participants with dyslexia who visited Ankara University Medical Faculty Child Neurology Department, and the control group comprised 20 age-matched typically developing children with no reading or writing problems. MSE was calculated for one continuous 60-s epoch for each experimental and control group's EEG session data. The experimental group showed significantly lower complexity at the lowest temporal scale and the medium temporal scales than the typically developing group. Moreover, the experimental group received 60 neurofeedback and multi-sensory learning sessions, each lasting 30 min, with Auto Train Brain. Post-treatment, the experimental group's lower complexity increased to the typically developing group's levels at lower and medium temporal scales in all channels.

A mobile app that uses neurofeedback and multi-sensory learning methods improves reading abilities in dyslexia: A pilot study.

Reading comprehension is difficult to improve for children with dyslexia because of the continuing demands of orthographic decoding in combination with limited working memory capacity. Children with dyslexia get special education that improves spelling, phonemic and vocabulary awareness, however the latest research indicated that special education does not improve reading comprehension. With the aim of improving reading comprehension, reading speed and all other reading abilities of children with dyslexia, Auto Train Brain that is a novel mobile app using neurofeedback and multi-sensory learning methods was developed. With a clinical study, we wanted to demonstrate the effectiveness of Auto Train Brain on reading abilities. We compared the cognitive improvements obtained with Auto Train Brain with the improvements obtained with special dyslexia training. Auto Train Brain was applied to 16 children with dyslexia 60 times for 30 minutes. The control group consisted of 14 children with dyslexia who did not have remedial training with Auto Train Brain, but who did continue special education. The TILLS test was applied to both the experimental and the control group at the beginning of the experiment and after a 6-month duration from the first TILLS test. Comparison of the pre- and post- TILLS test results indicated that applying neurofeedback and multi-sensory learning method improved reading comprehension of the experimental group more than that of the control group statistically significantly. Both Auto Train Brain and special education improved phonemic awareness and nonword spelling.

Quantifying Insurance Agency Channel Dynamics Using Premium Sales Big Data and External Factors.

In insurance business, product sales can be realized over a variety of channels such as independent agencies, or bank branches. In 2017, 55% of premium production was generated over insurance agencies in Turkey making independent agency evaluation prominent in the domain. Unfortunately lacking attention from the scientific community, agency evaluation problem is usually tackled in the industry by utilizing internal business dynamics data. To incorporate the external facts to the agency evaluation process, we propose a computational approach to model behavior traits reflecting insurance agency channel dynamics based on not only premium sales big data but also external facts. We demonstrate how we translate these behavior traits into useful features, namely, utilization, response, and governance, so that each agency can be positioned in a space whose dimensions are determined by these features allowing easy visual detection of segments. Utilization model suggests that each agency has a potential based on its location, determined by several local socioeconomic factors, and it explains the capability of converting potential to profit. To compute utilization scores, we adapt point-of-interest data as a parameter to the segmentation model, a novel approach not only in the insurance business but also in the literature. The response model suggests that a responsive agency must follow overall profit trends of the company. Finally, the governance model explains agency/company cooperation in terms of premium production. All together, we propose a segmentation-based agency evaluation model providing understanding of insurance agency behavior that could be explained and formulated along these three dimensions. Based on the findings from a year-long case study and a proceeding implementation period of our models on an actual analytic system of the insurance company donating the data, we reflect on the performance and usability of our behavioral models that were fit on premium sales big data comprising 127 million transactions. Our results suggest that (1) our approach is quite efficient in extracting features from production logs, (2) behavioral models are quite intuitive resulting in straightforward application steps, and (3) the adoption of behavior models in agency segmentation and evaluation processes is an improvement over commonplace approaches in which premium production is used as the sole metric.

Using Behavioral Analytics to Predict Customer Invoice Payment.

Experiences from various industries show that companies may have problems collecting customer invoice payments. Studies report that almost half of the small- and medium-sized enterprise and business-to-business invoices in the United States and United Kingdom are paid late. In this study, our aim is to understand customer behavior regarding invoice payments, and propose an analytical approach to learning and predicting payment behavior. Our logic can then be embedded into a decision support system where decision makers can make predictions regarding future payments, and take actions as necessary toward the collection of potentially unpaid debt, or adjust their financial plans based on the expected invoice-to-cash amount. In our analysis, we utilize a large data set with more than 1.6 million customers and their invoice and payment history, as well as various actions (e.g., e-mail, short message service, phone call) performed by the invoice-issuing company toward customers to encourage payment. We use supervised and unsupervised learning techniques to help predict whether a customer will pay the invoice or outstanding balance by the next due date based on the actions generated by the company and the customer's response. We propose a novel behavioral scoring model used as an input variable to our predictive models. Among the three machine learning approaches tested, we report the results of logistic regression that provides up to 97% accuracy with or without preclustering of customers. Such a model has a high potential to help decision makers in generating actions that contribute to the financial stability of the company in terms of cash flow management and avoiding unnecessary corporate lines of credit.

Designing Progressive and Interactive Analytics Processes for High-Dimensional Data Analysis.

In interactive data analysis processes, the dialogue between the human and the computer is the enabling mechanism that can lead to actionable observations about the phenomena being investigated. It is of paramount importance that this dialogue is not interrupted by slow computational mechanisms that do not consider any known temporal human-computer interaction characteristics that prioritize the perceptual and cognitive capabilities of the users. In cases where the analysis involves an integrated computational method, for instance to reduce the dimensionality of the data or to perform clustering, such non-optimal processes are often likely. To remedy this, progressive computations, where results are iteratively improved, are getting increasing interest in visual analytics. In this paper, we present techniques and design considerations to incorporate progressive methods within interactive analysis processes that involve high-dimensional data. We define methodologies to facilitate processes that adhere to the perceptual characteristics of users and describe how online algorithms can be incorporated within these. A set of design recommendations and according methods to support analysts in accomplishing high-dimensional data analysis tasks are then presented. Our arguments and decisions here are informed by observations gathered over a series of analysis sessions with analysts from finance. We document observations and recommendations from this study and present evidence on how our approach contribute to the efficiency and productivity of interactive visual analysis sessions involving high-dimensional data.