Assessing the informational content of card transactions for nowcasting retail trade: Evidence for Latvia

We use a novel card transaction data maintained at the Central Bank of Latvia to assess their informational content for nowcasting retail trade in Latvia. During the COVID-19 pandemic in Latvia, the retail trade turnover dynamics underwent drastic changes reflecting the various virus containment measures introduced during three separate waves of the pandemic. We show that the nowcasting model augmented with card transaction data successfully captures the turbulence in retail trade turnover induced by the COVID-19 pandemic. The model with card transaction data outperforms all benchmark models in the out-of-sample nowcasting exercise and yields a notable improvement in forecasting metrics. We conduct our nowcasting exercise in forecast-as-you-go manner or in real-time squared;that is, we use real-time data vintages, and we make our nowcasts in real time as soon as card transaction data become available for the target month. © 2023 The Authors. Journal of Forecasting published by John Wiley & Sons Ltd.

A Novel Approach in Credit Card Fraud Detection System Using Machine Learning Techniques

With the rapid expansion of daily life, the use of credit cards for online purchases is steadily increasing and credit card fraud is on the rise. Nowadays, in the social distancing environment, due to covid-1, 9online shopping has become important. Credit card credentials are used to make online payments, and then deduct money which does not involve any contact and makes people's life difficult. Because of this, finding the most effective method of detecting scams in online systems is essential. To prevent customers from being charged for goods they have not purchased, credit card companies must be able to identify fraudulent credit card transactions. Therefore, there are several theories either completed or proceeding to detect these kinds of frauds. This study is an approach to identify non-legitimate transactions using semi-supervised machine learning models by explaining how to deal with imbalanced datasets, using a wide variety of models to better understand which ones work better. © 2021 IEEE.