Anandaganesh Balakrishnan

Blockchain is considered a key technology in the financial sector with significant prospects like internet development and Financial Technology (FinTech). The deployment of technology to automate financial services become the primary role for industries and economic organizations due to digital age has seen a transition period in terms of personalization and consumers. However, the banking sector in FinTech faces difficulties associated with protecting customer data and ensuring cybersecurity… Show more

Blockchain is considered a key technology in the financial sector with significant prospects like internet development and Financial Technology (FinTech). The deployment of technology to automate financial services become the primary role for industries and economic organizations due to digital age has seen a transition period in terms of personalization and consumers. However, the banking sector in FinTech faces difficulties associated with protecting customer data and ensuring cybersecurity against evolving threats due to increasing financial service digitization. In this research, the Weighted Fuzzy K-Nearest Neighbor (WFKNN) is proposed with blockchain-based FinTech for the banking sector to enhance data protection and improve threat identification. Show less

In the rapidly evolving field of design, the integration of Artificial Intelligence (AI) and data-driven approaches is revolutionizing traditional methodologies, enhancing creativity, and optimizing design solutions. This paper explores the intersection of AI and solution architecture, focusing on how AI-powered creativity and data-driven design can synergize to produce innovative and efficient outcomes.

The paper discusses how AI technologies are transforming regulatory compliance in financial institutions by improving efficiency and accuracy. It examines the use of AI in monitoring, detecting, and preventing regulatory breaches, particularly in sanctions and Anti-Money Laundering (AML) efforts.

This paper discusses the challenges of sanctions and Anti-Money Laundering (AML) compliance in the global financial system and suggests that blockchain technology could revolutionize regulatory frameworks. It highlights the key features of blockchain—decentralization, immutability, and transparency—and explains how these can help prevent financial crimes like sanctions evasion and terrorist financing. Through case studies, it demonstrates how blockchain can improve transaction traceability and… Show more

This paper discusses the challenges of sanctions and Anti-Money Laundering (AML) compliance in the global financial system and suggests that blockchain technology could revolutionize regulatory frameworks. It highlights the key features of blockchain—decentralization, immutability, and transparency—and explains how these can help prevent financial crimes like sanctions evasion and terrorist financing. Through case studies, it demonstrates how blockchain can improve transaction traceability and automate compliance with smart contracts. The paper also covers the difficulties of integrating blockchain into existing legal and regulatory environments and stresses that achieving its full potential requires cooperation among regulators, industry participants, and tech providers. Show less

This study examines the evolving landscape of cybersecurity in the context of Generative Artificial Intelligence (AI), highlighting the dual-edged nature of technological advancements that offer significant potential for innovation while posing new threats to user security and privacy.

The article elucidates pivotal patterns, features, and strategies in contemporary Python programming and offers insights into practical code development techniques.

This path outlines the evolutionary steps organizations take to harness the full potential of their data, transforming it from a passive asset into a strategic driver of decision-making, innovation, and competitive advantage.

Performance optimization in Python makes Python code run faster and more efficiently. This involves identifying and eliminating bottlenecks, reducing memory usage, and improving execution speed. Optimization can be crucial when performance is critical, such as in large-scale data processing, high-traffic web applications, or scientific computing.

This article outlines the optimization methods and capabilities within Snowflake to enhance performance and lower expenses.

This article emphasizes the revolutionary effect of open table formats on data management practices in distributed computing landscapes.

This paper explores how RAG, RLHF, and fine-tuning can synergistically optimize and streamline data engineering processes, resulting in quicker and more efficient data pipeline generation. It examines methodologies across the data engineering lifecycle, including data collection, processing, storage, quality, analytics, and security, and demonstrates how these AI techniques can automate and improve data engineering tasks.

In the current landscape of Artificial Intelligence and Machine Learning innovation, the imperative to aggregate data from varied sources and derive real-time insights is more pronounced than ever. This necessity gives rise to a Multi-Engine Data Virtualization Framework, a novel approach designed to refine data virtualization and management strategies. Distinct from conventional data virtualization systems, which often falter in processing complex and voluminous data, this innovative framework… Show more

In the current landscape of Artificial Intelligence and Machine Learning innovation, the imperative to aggregate data from varied sources and derive real-time insights is more pronounced than ever. This necessity gives rise to a Multi-Engine Data Virtualization Framework, a novel approach designed to refine data virtualization and management strategies. Distinct from conventional data virtualization systems, which often falter in processing complex and voluminous data, this innovative framework aims to capitalize on the diverse strengths of various data platforms, thereby elevating efficiency and efficacy in data virtualization. Show less

Understanding the structures within a Relational Database Management System (RDBMS) is critical to optimizing performance and managing data effectively.

Deep Dive into Druid’s architecture to understand sub-second query response and parallel batch and streaming ingestion.

Take a glimpse into the latest real-time data architecture patterns designed to meet analytical business requirements.

Optimizing the Python code to run faster.

In this article, I have explained what are the best practices that should be followed in Query Federation in Data Virtualization