Payment Architecture Meets AI: Vijay Kumar Soni’s Work Translates Engineering into Security and Scalability

Today, engineering excellence in financial technology does not only concern infrastructure scalability or software performance but also how a system rating protects against threats, secures identity, and drives digital transformation. Sitting on the very edifice of applied AI and payment architecture, Vijay Kumar Soni, with over seventeen years of experience in digital payments, open banking, and identity security, has extensively worked on developing secure and scalable platforms capable of answer such challenges facing the present-day financial ecosystem. Vijay has deep technical knowledge in cloud-native engineering, cryptographic systems, and enterprise-scale architecture. With a career in mission-critical system building for emerging payments, this foundation has become a major part of the research he has contributed to in recent times.

Drawing from his practical experience in designing secure REST APIs, SDKs, and mobile payment architectures in regulatory compliance, Vijay seems to have branched out his research interests to include fraud analytics, anomaly detection, and cryptographic transparency. The published pieces are a testament to his skills to analyze security threats and, in turn, propose and solve engineering solutions to counter these threats. Here follows three examples of how Vijay’s industry know-how is applied in recent advances in secure authentication, fraud modeling, and explainable cryptography.

Securing Wallets Through Anomaly Detection

The paper titled “Self-Supervised Session-Anomaly Detection for Password-less Wallet Logins, Vol 5, 2025,” published in the Newark Journal of Human-Centric AI and Robotics Interaction, dwells on the security gaps opening up in frictionless login systems, most notably the password-free flows, as their translations. The paper suggests a self-supervised learning framework to detect session anomalies backed by behavioural patterns without the need for pre-labelled data.

The solution straightforwardly draws on Vijay’s domain expertise in secure mobile app design and risk-based authentication systems for payment APIs. He explains he was the lead in platform security design, which shaped the architecture of the solution, which includes session-state embeddings and temporal feature extraction.

Vijay states in the paper, “Session integrity must be preserved without making assumptions about user intent. Our approach continuously learns session context and flags deviations before any compromise escalates.” Such an insight demonstrates awareness of practical scenarios where even passive session exploits lead to fraudulent wallet access. The system introduced a mechanism allowing continuous learning, enabling the model to adapt as interaction patterns evolve.

Graph-Based Fraud Scoring for Tokenized Payments

With the article “Hybrid Graph-Transformer Fraud Scoring in Tokenized Card-on-File Ecosystems, Vol 3, 2023” appearing in Essex Journal of AI Ethics and Responsible Innovation, an urgent need is certainly identified for the early creation of intelligent fraud scoring systems in an ecosystem wherein card tokens are engaged across platforms. This model developed by Vijay tries to combine graph neural networks with transformer layers to detect fraud patterns based on how tokens relate to one another and the transactions’ temporal features.

And so it should build further upon Vijay’s work in designing tokenized payment networks and cross-platform integrations for secure commerce. Ventura has intimate knowledge of ISO 8583 message parsing, token lifecycle, and acquirer integration. This gave him the advantage to spot what traditional models cannot—the blind spots that emerge when synthetic identities or replay attacks circumvent merchants.

“Fraud patterns emerge not just from frequency, but from how transaction sequences shift across merchant networks,” Vijay notes in the paper. The graph-transformer architecture he helped to build models these shifts dynamically, thus gaining more contextual awareness.

What makes Vijay’s intervention unique is his embedding of business-specific constraints into the model training itself, including token expiry behaviors, device fingerprints, and issuer rules. By embedding these constraints within the graph structures, he ensured the model was relevant in operational settings, far from mere theoretical correctness. It is the combination of system semantics with AI modeling that defines much of his work toward producing fraud detection systems that possess both explainability and practical deployment in enterprises.

Making Cryptographic Systems Transparent and Explainable

In 2020, Vijay penned “Explainable Cryptographic Key-Lifecycle Management via Knowledge Graphs, Vol 2, Issue 2, 2022,” published in Vol. 4 of the Journal of Artificial Intelligence & Machine Learning Studies. The paper addressed a common security-engineering challenge of understanding and auditing use of cryptographic keys in systems operating in multi-tenant modes and with frequent certificate rotations.

Drawing upon his deep knowledge of TLS, JWT, RSA, and ECC cryptography, Vijay proposed a novel framework mapping key issuance, renewal, and revocation events on a structured knowledge graph which not only tracked use but reasoned about inconsistencies or suspicious key behaviors. This allows the auditor and system engineer to visualize anomalies in the lifecycle and validate compliance posture without parsing verbose logs or key-value stores.

Vijay notes in the paper, “Cryptographic transparency should not end with secure key storage—it must extend to operational accountability and interpretability.” His framework extrapolates metadata from a plurality of key management platforms, providing a unified view conformant with industry standard and enterprise audit requirements.

Grounded in Real-World Practice and Strategic Engineering

Across these three studies, Vijay has consistently shown his strengths in bringing real-world problems into the research domain—not through abstract models, but through concrete, deployable frameworks. Be it securing wallet sessions using adaptive learning methods, improving fraud scoring accuracy via hybrid AI models, or developing explainable graphs to expose key-lifecycle shortcomings, Vijay’s research is firmly attached to systems thinking and operational precision.

Vijay grounds his approach in three principles: build with constraints in mind, align with enterprise standards, and design for transparency. It is an engineering mindset and is evident in every study published by Vijay; it is prevalent in the series of leadership positions he’s assumed during the various payment modernization programs. His research and career embody a resilient system design approach meant to work at scale while intelligently adapting when faced with adverse conditions.

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