Applying Simulation to the Problem of Detecting Financial Fraud.
This thesis introduces a financial simulation model covering two related financial domains: Mobile Payments and Retail Stores systems. The problem we address in these domains is different types of fraud. We limit ourselves to isolated cases of relatively straightforward fraud.
openaire +2 more sources
Value creation and capture in artificial intelligence‐enabled ecosystems
Abstract Research Summary We develop an ecosystem perspective on AI‐enabled value creation, grounded in AI's characteristics as a general‐purpose technology. We consider that AI can support multiple value propositions realized through distinct but interconnected ecosystems.
Rahul Kapoor, Manav Raj
wiley +1 more source
Quantum computing in finance: a literature review and future directions for trustworthy financial AI. [PDF]
Muzzioli S +4 more
europepmc +1 more source
Whither business model innovation strategy in the age of AI?
Abstract Research Summary We argue that business models are transformed into auto‐adaptive systems through embedded AI inference and learning activities. The competitive advantage of focal firms may increasingly depend on how effectively their activity systems learn relative to competing business models, with learning serving as a meta value driver ...
Christoph Zott, Raphael Amit
wiley +1 more source
Digital Deception: Survey Bots and the Ethics of Data Integrity. [PDF]
Frank-Ito W, Eldridge LA, Matthews J.
europepmc +1 more source
Abstract Research Summary We examine the effects of category erraticism, that is, having moved across socio‐cognitively distant industry categories over time, on status mobility. We argue that it is not the breadth of the categories that a firm spans or the overall distance among those categories, but whether its movement across categories forms a ...
Danyang Li, Michael Jensen
wiley +1 more source
Research on a financial fraud identification model by fusing a convolutional neural network. [PDF]
Lu H, Zhu S, Zhang Y, Lv R.
europepmc +1 more source
Predicting Runtime in Spark‐Like Systems With Allocation‐Aware Deterministic Models
ABSTRACT Objectives Apache Spark is widely used for distributed data processing, but accurately predicting application execution time remains challenging because performance depends on application structure, resource configuration, and executor‐allocation behavior.
Hina Tariq, Olivia Das
wiley +1 more source
DQN‐Guided Subset‐Induced OCSVM Kernel Approximation for Imbalanced Anomaly Detection
Anomaly detection under limited normal data remains a fundamental challenge due to severe class imbalance and scarcity of anomalies. We propose a novel framework that reformulates support vector selection in One‐Class SVM as a sequential decision‐making problem.
Wenqian Yu, Jiaying Wu, Jinglu Hu
wiley +1 more source
Conservative plausibility-filtered SMOTE for credit card fraud detection under extreme class imbalance. [PDF]
Becerra-Suarez FL +3 more
europepmc +1 more source

