Abishek Kumar – författare

The book explores the mathematical and computational techniques used in Decentralized Finance (DeFi) and Financial Technology (FinTech).DeFi operates on smart contracts, which are self-executing programs that automate the necessary actions for conducting transactions on a blockchain.FinTech encompasses a broader range of services, including digital payments, mobile banking, and financial analytics, often powered by machine learning and data science.This book integrates mathematical concepts such as cryptographic algorithms, stochastic modeling, optimization, and game theory with computer science principles of Consensus mechanisms in cryptocurrency networks that include:Proof-of-Work (PoW): Users solve complex computational puzzles to add new blocks to the blockchain.Proof-of-Stake (PoS): Users validate transactions based on the number of coins they hold and are willing to 'stake' for network security.The aim is to use such a combination to enhance the security, stability, and efficiency of automated systems for decentralized exchanges, lending protocols, and automated markets. Furthermore, advancements in computation—such as artificial intelligence, big data, and cloud computing—improve DeFi by enabling the prediction of asset movements, analyzing user behavior, and supporting scalable decentralized applications. Despite rapid advancements, challenges remain, particularly in areas like smart contract vulnerabilities, market volatility, and regulatory compliance. Ongoing interdisciplinary research is focused on addressing these issues through improved composability and integration of mathematics with computation. These innovations promote global participation, transparency, and financial inclusion.

In today’s data-driven era, the convergence of mathematics, computing, artificial intelligence, and blockchain is emerging as a significant area at the intersection of applied mathematics and computer science, particularly in decision-making. This book explores the applications of advanced mathematical models and computational algorithms to AI-driven strategies and blockchain technologies.It covers advanced linear algebra techniques, probability theory, optimization methods, game theory, cryptography, and statistical learning, providing deep mathematical insights into AI, blockchain, and data-driven decision-making. The book delves into matrix computations and eigenvalue problems relevant to deep learning, Bayesian inference for predictive modeling, and reinforcement learning for dynamic decision-making.Additionally, optimization methods such as convex programming and Lagrangian multipliers enhance resource allocation, while cryptographic protocols ensure the security of blockchain systems. By integrating these mathematical frameworks, this book provides researchers, professionals, and students with practical tools for addressing complex business challenges ranging from fraud detection to automated contract execution.