Abstract Internal audit functions in U.S. manufacturing and retail face a growing disconnect between increasingly sophisticated fraud schemes and legacy detection methods that rely on static rules and manual sampling. The Association of Certified Fraud Examiners estimates that organizations lose approximately five percent of annual revenue to fraud, with manufacturing and retail sectors particularly vulnerable due to complex supply chains, high transaction volumes, and decentralized operations. Modern fraud schemes have evolved well beyond simple expense manipulation; they now involve multi-party collusion, cyber-enabled invoice fraud, supply chain manipulation through fictitious vendors, and coordinated point-of-sale skimming networks. Traditional audit approaches typically cover only three to five percent of transactions through periodic sampling, leaving the vast majority of activities unexamined and creating significant windows of exposure. Learningter demonstrates how applied AI (machine learning anomaly detection, natural language processing, and agentic AI) transforms fraud detection from reactive forensics into proactive, continuous assurance. Drawing on five anonymized case studies from active industry engagements, the presentation illustrates measurable outcomes: false-positive rates reduced by up to 70 percent, detection time compressed from months to minutes, and coverage expanded from sample-based testing to full-population analysis. Each case maps legacy controls against AI-augmented alternatives, providing a clear migration pathway. In particular, Agentic AI enables autonomous and continuous monitoring through self-correcting feedback loops that recalibrate detection models in real time without requiring manual intervention, adapting dynamically to emerging fraud patterns and shifting transaction behaviors. The presentation addresses practical adoption challenges (data quality, algorithmic bias, SOX/ICFR compliance, and change management) and offers a structured readiness framework for consulting engagements or dissertation research. Grounded in Boyer's Scholarship of Application, this work connects data science and auditing to real-world problems, demonstrating how cross-disciplinary collaboration produces actionable improvements in governance and risk management. The research is directly relevant to doctoral candidates seeking applied dissertation topics with measurable industry impact and to faculty developing curricula that bridge theoretical foundations with practitioner-oriented pedagogy.
B. G. Anand kumar, M. Nikhil Kumar, T. Sravan Kumar, G. Janaki Ram · 6 authors
Explore the article titled A Trustworthy Voting Framework Using Aadhaar and Distributed Ledger Technology from IJIRT Volume 12, Issue 10. This study evaluates the effectiveness of teaching programs on waste management knowledge among women.
Built environments face significant challenges in promoting material reuse due to the lack of trustworthy and transparent trading mechanisms. Existing digital platforms often fail to ensure fairness, bid confidentiality, and auditability. To address this gap, this paper presents the design and implementation of a verifiable auction system for trading reusable construction materials. Built on a Polkadot-based platform, the system supports sealed reverse auctions through a hybrid on-chain/off-chain architecture: encrypted and signed bids are stored in InterPlanetary File System (IPFS), while a Merkle root of valid bids is committed on-chain. Zero-knowledge proofs (ZKPs) enable verification of bid correctness without revealing bid values. Stress testing showed moderate latency from proof generation and IPFS operations, yet performance remained suitable for auctions lasting minutes or hours. The results demonstrate a scalable, privacy-preserving, and auditable auction mechanism that leverages decentralised technologies to advance circular economy goals.
The Zupply framework introduces an anonymous authentication protocol that utilizes zero-knowledge proofs to ensure data integrity, participant anonymity, and unlinkability within supply chains. Zupply employed the Groth16 zkSNARK, which requires a trusted setup. This paper explores the integration of Aurora, a transparent setup post-quantum secure zkSNARK, to the Zupply framework. This paper presents the core Zupply arithmetic circuits, including Auth, Trans, Merge, and Div, which allows succinct zero-knowledge proofs without exposing sensitive data. We evaluate the performance of the Zupply Aurora-based implementation across varying Merkle hash tree depths, comparing it to the original Groth16-based setup for BN254 and BLS12-381 elliptic curves.
A large portion of the software side of our information technology infrastructure, including web search, email, social media, transportation information, and much more, is provided "free" to the end users, although the corporations that provide this are often enormously profitable. The business model involves customized advertising and behavior manipulation, powered by intensive gathering and cross-correlation of personal information. Significant other parts of our IT infrastructure use fees-for-service but still involve intensive information gathering and behavior manipulation. There are significant indirect costs of these business models, including loss of privacy, supporting surveillance by both corporations and the state, automated manipulations of behavior, undermining the democratic process, and consumerism with its attendant environmental costs. In a recent book, Shoshana Zuboff terms this "surveillance capitalism." Our primary focus in this essay is how we could develop new models for providing these services. We describe some intermediate steps toward those models: education, regulation, and resistance. Following that, we discuss a partial solution, involving for-profit corporations that provide these services without tracking personal information. Finally, we describe desired characteristics for more comprehensive solutions, and outline a range of such solutions for different portions of the IT infrastructure that more truly return control to the end users. A common feature of several is the use of highly decentralized storage of information (either on the end user's own personal devices or on small servers), a modular architecture and interface to allow for customization of what information is to be shared, and a distributed ledger mechanism for authentication.