transaction monitoring

Fraudulent payroll accounts are a significant issue in the financial and payroll services market. About 75 percent of employees have stolen from their employer once, 20 percent have stolen at least twice, and half of this figure has stolen at least three times or more. The increased use of direct deposit has made it easier to steal the identity of a small payroll customer. A fraudulent account is set up with apparently legitimate credentials, and the account’s data set is changed after the customer’s login authentication returns an all-is-ok status. The direct deposits are then quickly withdrawn. It’s an ongoing game of cat-and-mouse: after a bank improves or alters its fraud detection system, criminals quickly adapt to avoid it. Small-scale business contract fraud is accepted and paid as normal business expenses, and there is extremely little fraud detection technology in place to pick up the kickback schemes. This type of fraud can bring a company to its knees. (B) Automated fraud detection in payroll and financial management services harnesses sophisticated deep learning architectures. Computers do the heavy lifting for identifying patterns, obtaining insights, making decisions, and taking action. Unless large fraudulent datasets are already commercially available, the models are unable to understand or predict fraud when trained only on legitimate transaction data. Constructing artificially oversampled imbalanced data sets leads to flawed models. A self-training active learning ensemble stack of models using transaction authentication data is described. It directly leverages fraud patterns in a mostly-unlabeled data set and requires minimal retraining when a pattern changes. Model performance is measured with area under the ROC curve, and this method outperforms existing techniques.

Real-time traffic congestion is a challenging problem in smartcities. An Intelligent Transportation System (ITS) is a big data application integrating sensor hardware and network technologies, which can intelligently capture traffic information, efficiently transform data into knowledge, and organize and manage transportation resources. Urban Traffic Control (UTC) is a critical component of ITS, analyzing real-time traffic information and coordinating traffic signal timing plans to optimize traffic network performance and improve vehicle travel speeds. However, traditional UTC based on centralized architecture would be challenged in data transmission, architecture malfunctions, system bottlenecks, etc. As a solution, the multi-Agent based RTC (ATC) with smart intersections is proposed. Additionally, more comprehensive traffic data can be captured with advanced detection techniques, and Regional Traffic Control (RTC) systems can be designed with advanced optimal control algorithms. Digital Banking (DB) Infrastructures powered with AI can be trained on historical data to simulate human understanding of patterns and trends when leveraging custom models tuned to understand banking transactions. Integrating artificial intelligence (AI) and automation into the digital banking infrastructure can result in a stable pipeline implementation of sorting transactions data for anomalies per the bank thresholds. Moreover, with the integration of AI systems, captured data can be analysed to study the traffic characteristics of the bank and determine how efficiently it is working. AI can also be used to examine this data, identify problematic data, perform risk prediction, timely tracking, and further determine whether it fits the standards of bank transactions. AI can warn of difficulties in bank transactions. When an unauthorised transaction is detected, that transaction can be prohibited in real-time. Furthermore, it can help to significantly increase banks' risk management levels, improved efficiency and a near-zero error output requirement of regular activity.