AI-Based Decision Intelligence (DI) Platforms are transforming digital business operations by combining artificial intelligence, machine learning, predictive analytics, natural language processing, and business analytics to support intelligent decision-making. Unlike traditional Business Intelligence systems that mainly provide historical insights, DI platforms deliver predictive and prescriptive recommendations that improve decision accuracy, operational efficiency, business agility, and customer satisfaction. The proposed framework integrates data collection, intelligent analytics, predictive modeling, decision optimization, and continuous learning within a scalable cloud-based ecosystem. The study demonstrates that AI-driven decision intelligence significantly enhances organizational performance and supports successful digital transformation across various industries. Furthermore, emerging technologies such as Explainable AI, federated learning, autonomous decision systems, and ethical AI governance will further strengthen enterprise decision ecosystems. Overall, AI-Based Decision Intelligence Platforms serve as a critical enabler for sustainable growth, competitive advantage, and intelligent enterprise transformation in the digital era.
Vladimir Glushkov, V. Glushkov· International Journal of Art...· 0 citations
Digital transformation has been a game-changer for financial institutions, driving the bank industry's evolution into a more real-time, cloud-based, mobile, and AI-enabled financial landscape. While all these technological developments have boosted operational efficiency and customer service, they have also created new opportunities for cyber criminals to attack financial organizations with powerful and sophisticated means like ransomware, phishing, insider threats, Advanced Persistent Threats (APTs), Distributed Denial-of-Service (DDoS) attacks, identity theft, and financial fraud. This has given rise to the need for extensive cyber security systems to be a strategic necessity and not a technical necessity. In this paper, the authors explore modern approaches to cybersecurity frameworks for digital financial institutions, reviewing methodologies for assessing risk, governance of security, regulatory compliance, and innovations like Artificial Intelligence, Machine Learning, Blockchain, and Zero Trust Architecture. The study provides an overview of existing cybersecurity frameworks such as the NIST Cybersecurity Framework, ISO/IEC 27001, COBIT and PCI DSS, along with financial regulatory frameworks. In addition, a conceptual framework is suggested for enhancing cyber-resilience based on continuous monitoring, threat intelligence, automated incident response, and adaptive security controls. The findings have shown that the combination of intelligent security solutions and a standardized governance model not only increases the resilience of the organisation, but also reduces cyber risks, facilitates compliance with regulatory requirements and safeguard the trust of customers. The proposed framework offers a scalable and proactive strategy for financial institutions to address the escalating cyber threats in the ever-engaging digital landscape.
Vladimir Glushkov, Victor Glushkov· International Journal of Com...· 0 citations
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