Structured finance environments generate large volumes of heterogeneous, dynamic, and time-sensitive financial data, creating significant challenges for investment analytics, risk assessment, and decision-making. Many traditional investment intelligence systems are built on a centralized architecture, which has scalability, transparency, and real-time responsiveness challenges. To overcome these problems, this paper suggests a multi-agent artificial intelligence framework for explainable and low-latency investment analytics in structured finance. Its structure is based on the concept of a community of smart agents responsible for gathering financial information, creating market knowledge, valuing risk, predicting, explaining, and optimizing investments. The orchestration layer helps to coordinate interactions between agents and enables parallel processing of agents, leading to more efficient and responsive analysis. The high-tech machine learning models are integrated with the Explainable Artificial Intelligence (XAI) mechanisms and produce clear, interpretable, and meaningful investment tips. It is an active system that is constantly analyzing financial data and can predict market trends and patterns and give good insights into investments without sacrificing transparency of investment decisions. The performance of the proposed framework on investment analytics is validated experimentally, which shows significant improvement in the performance of the investment analytics and an investment prediction accuracy of 97.3%. Furthermore, the framework reduces the latency of processing and guarantees the transparency, scalability, and reliability of decision-making. The results indicate that the suggested solution has the potential for offering an efficient and reliable solution for future-generation investment analytics using a structured finance environment.
Deepak Saxena, R. Venkata, Sai Kumar Potladurthy et al.· Journal of Intelligent Decis...· 0 citations
The rapid adoption of cloud-native banking platforms and distributed fintech applications has significantly increased the use of stateful financial microservices for payment processing, transaction management, fraud detection, and digital financial operations. However, these distributed financial systems are very sensitive to runtime failures, cascading service failures, transaction inconsistencies, infrastructure instability, and operational risks due to latency under dynamic workloads. Traditional resilience testing and monitoring methods can fail to detect unknown weaknesses and critical failures in a real-time financial landscape. Based on these considerations, the present paper introduces a novel framework, dubbed AI-Driven Chaos Engineering (AI-CE), for enhancing the resilience and operational reliability of stateful financial microservices in cloud-native environments. The framework suggests that these capabilities, including AI-driven anomaly detection, distributed observability, adaptive fault orchestration, predictive failure analysis, and automated self-healing recovery, be centralized into a single resilience engineering architecture. Real-time telemetry data from transaction services, APIs, databases, message brokers, and containerized workloads are constantly monitored with AI-based behavior analytics to detect sensitive operational states and dynamically create intelligent chaos experiments such as latency injection, network partitioning, resource exhaustion, service crashes, and transaction interruption experiments. Experimental assessment shows that the proposed framework attains almost 96.32% classification accuracy and significantly enhances the fault detection capability, recovery efficiency, transaction consistency, service availability, and operational resilience over traditional chaos engineering methods. The proposed research helps in developing the smart, adaptive, and self-healing financial microservices ecosystems that will enable reliable next-generation digital banking and fintech infrastructures.
Rohith Venkata Sai Kumar Potladurthy, Mufaddal Munim, Rajesh Makala· 2026 International Conferenc...· 0 citations
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