FUTURECENTRAL PRESS
Explore AI, finance, leadership, agribusiness and sustainability through books for postgraduate study and working professionals.
Enterprise AI Transformation
How can an enterprise turn AI investment into business value? This management guide follows opportunity selection and portfolio priorities through data preparation, operating models, adoption, and governance. It brings those decisions together in a practical transformation roadmap.
Stewarding Human Capital Through the AI Transition
AI changes how leaders exercise judgment, develop people, and earn trust. The book examines how to anticipate that change and redesign work while protecting learning, accountability, and long-term organizational capability.
Building and Working in the Enterprise AI Function
What should an enterprise AI function own, and how should it work with the rest of the organization? The chief AI officer’s decisions provide a way to examine the function’s mandate, organizational boundaries, early initiatives, and capability development.
From Machine Learning to Generative and Agentic AI
Predictive, generative, and agentic AI have applications in lending, insurance, investments, financial crime, and operations. This book examines the data, institutional capabilities, and governance needed to put those applications to work.
The Guide for Banking Professionals
For working banking professionals, AI raises practical questions about credit, customer channels, treasury, trade finance, and financial crime. The book examines application choices alongside data architecture, engineering, operational controls, and the consequences of automated customer-facing decisions.
India’s financial technology sector provides the setting for examining payments, credit, banking, wealth, insurance, and regulatory technology. Readers consider how digital infrastructure and AI change business models, unit economics, and the delivery of financial services.
Member-Centred Innovation, Responsible Governance, and the Future of Cooperative Finance
Member ownership and cooperative governance shape the choices institutions face when adopting AI. This developing book examines member value, organizational readiness, and shared-service options, with attention to the responsibilities of cooperative banking leaders.
The Guide for Insurance Professionals
Underwriting, claims, actuarial work, distribution, and operations each place different demands on AI. Across insurance lines, the book examines predictive models, generative tools, and agents alongside the data and governance requirements facing insurance professionals.
The Guide for Retail Banking Professionals
Retail credit, deposits, service, and collections provide the focus for this book. Customer journeys and banking workflows are examined alongside the data, controls, and human judgment needed to use AI responsibly in everyday retail operations.
The Guide for Payments and Financial-Inclusion Professionals
Digital payments depend on infrastructure, routing, trust, and access. AI applications in payments and remittances are considered alongside barriers to inclusion, including language, identity, device constraints, and service design.
The Guide for Corporate and Trade Finance Professionals
Corporate clients and trade flows call for decisions about underwriting, working capital, relationship intelligence, and transaction banking. The planned contents of this developing title examine those decisions alongside data foundations, workflow redesign, and governance.
An Enterprise Transformation Guide for Banking Leaders
Banking leaders must decide where enterprise AI transformation can create value and what their institutions need to deliver it. This developing management book brings segment strategy and investment portfolios together with operating capabilities and governance.
A Three-Wave Perspective — from Statistical Foundations through Machine Learning to Agentic Risk Intelligence
Uncertainty, distributions, and the limits of measurement form the starting point for financial risk management. The book develops these foundations before examining core risk categories, analytics, machine learning, and the governance of generative and agentic risk intelligence.
The finance function of nonfinancial firms is the focus of this developing book. Its planned contents examine the chief financial officer’s decisions, including who holds decision rights, how often choices recur, and how uncertainty and reversibility affect the use of AI.
Startup Financing, Deals, Contracts and Exits
Fund economics, sourcing, and diligence lead into valuation, contracts, governance, and exits in this book on Indian startup financing and investment transactions. Entrepreneurs’ and investors’ perspectives inform the analysis of financing and deal decisions.
The Guide for Investment Professionals
Investment research, advisory services, portfolio decisions, and market operations are changing with AI. The book examines those changes alongside brokerage, infrastructure, and digital assets, with attention to model risk, agentic workflows, and investor protection.
The Guide for Fraud and Financial-Crime Professionals
Account takeover, payment fraud, network analysis, screening, know-your-customer checks, and investigation form the planned coverage of this developing title. Evidence and operational controls guide its treatment of AI in fraud detection and anti-money laundering.
A Guide for Compliance Professionals in Financial Services
Compliance teams interpret obligations, monitor activity, and document decisions. The book examines the data architecture and regulatory workflows behind that work, including the opportunities and limits of generative and agentic AI.
Building Audiences, Brands, and Categories Across B2C, B2B, and the Sustainability Era
Segmentation, customer value, and personalization inform decisions about audiences, brands, and categories. Readers examine how AI affects creative work, business-to-business and consumer marketing, measurement, sustainability, and governance.
Identifying a problem worth solving and testing a viable response are the starting points for entrepreneurship. The book follows venture decisions through technology choices, business models, growth, financing, organization, and governance in an economy increasingly shaped by AI.
A Manager’s Guide to Traceability, System Design, and Governance
When does a shared ledger help solve an agribusiness coordination problem? This managerial introduction examines traceability, participation, data quality, and governance. Readers assess where blockchain fits and when another system may be more suitable.
From Predictive Models to Generative and Agentic AI Across the Agri-Value Chain
The book examines predictive, generative, and agentic AI across the agricultural value chain. Production and enterprise opportunities are assessed against business value, feasibility, data requirements, and governance.
Architecture, Sustainability, and the AI Transformation
Seasonal cash flows, distinctive risks, and institutional constraints shape agricultural finance. Lending and risk-sharing decisions are examined alongside sustainability and the possibilities of AI-assisted financial analysis and delivery.
Digital Finance for Agricultural Value Chains
Agricultural transactions and information can support new approaches to financial services. This book examines the agrifintech ecosystem and its infrastructure, relating value-chain flows to credit products, delivery models, and the agricultural finance gap.
Coordination failures, physical losses, and cost trade-offs make agribusiness supply chains difficult to manage. Procurement, logistics, and traceability decisions provide the starting point for evaluating technology choices and sustainability.
Production, markets, and institutions interact in agribusiness sustainability decisions. The book examines economic, social, and environmental trade-offs alongside operational choices and AI’s contribution to sustainable management.
Weather, production, and markets create agricultural risks that insurance can only partly address. Readers examine risk categories, insurability, and alternative instruments, then consider how data and AI affect product design and basis risk.
Biological cycles, fragmented markets, and institutional constraints shape agricultural ventures. The book follows choices about opportunities, business models, technology, financing, and growth under these conditions.
This concept in development examines AI in agricultural input and output marketing. Proposed coverage links customer identity and seasonality with market intelligence, predictive applications, multilingual content, agents, traceability, and trust.
A Guide for the AI Era
Experienced procurement, logistics, and supply-chain professionals are the intended readers of this concept in development. Proposed coverage focuses on supplier onboarding, contracts, traceability, technology-vendor selection, and other operating decisions in agribusiness.
From ESG Analytics to Agentic Operations
AI can assist sustainability decisions, and its own governance and operating impacts also require attention. Readers examine environmental, social, and governance information, reporting, and physical systems alongside procurement and organizational responsibility.
A Three-Wave Perspective — from Disclosure and ESG Integration through Climate Analytics to Agentic Sustainability Intelligence
Environmental and social outcomes affect capital allocation decisions. The book examines disclosures, climate and nature risks, financing instruments, and portfolios, then considers AI applications and governance in financing the transition.
From Your First Line of Code to a Complete Analytical Project
Starting with a first line of Python code, readers build toward a complete analytical project. Programming foundations, NumPy, pandas, visualization, and exploratory analysis are taught in a finance context.
A Tamil Nadu University Edition
This concept in development supports a broad MBA business analytics course. Proposed coverage combines worked problems in statistics, regression, and forecasting with visualization, functional applications, analytical tools, and ethical data use.
From Foundations to Autonomous Agents
Business problem framing guides the planned contents of this developing book. Classical analytics and machine learning lead into deep learning, language models, retrieval, agents, and responsible implementation.
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