Ajit Kumar Jain (9 results)

Condition: New. pp. xx + 182 Illus., Maps.

Condition: New. pp. xx + 182, Maps.

Condition: New. pp. xx + 182.

Taschenbuch. Condition: Neu. Neuware.

Paperback. Condition: new. Paperback. AI in Finance: What Actually Changes and What Does NotAI is entering forecasting, accounting, valuation, credit, reconciliation, tax, risk management, and financial decision-making. Yet one question receives far less attention: Who should sign when the person responsible for the decision cannot personally validate how the system produced the answer?This book addresses that governance gap.AI in Finance: What Actually Changes and What Does Not introduces the Signer/Builder Gap, the growing distance between those who build or configure AI systems and the executives who remain accountable for their outputs. Through practical finance cases, the book examines where automation belongs, where human judgment must remain, and why high model accuracy does not remove the need for verification.The book develops practical frameworks for executives, finance leaders, risk professionals, auditors, tax professionals, and boards. These include the Capability Matrix, Validation Gap Framework, Claims-Testing Protocol, Decision-Cost Lens, Board Translation Script, and Signer's Sign-Off Protocol. Together, they provide a structured way to evaluate AI-supported decisions, challenge vendor claims, identify high-risk automation, test whether outputs remain trustworthy, and establish accountability before a number reaches a board, regulator, customer, investor, or tax authority.The central message is simple. AI changes how financial work gets performed. It does not remove professional responsibility.Executives do not need to become data scientists. They need a stronger discipline for deciding what deserves trust, what requires independent verification, who remains accountable, and what evidence should exist before they sign.For finance leaders navigating AI adoption, this book offers a practical framework for one of the defining governance questions of the coming decade: when machines increasingly produce the numbers, what must humans still verify before putting their name behind them? This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Condition: New. Print on Demand.

PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condition: new. Paperback. AI in Finance: What Actually Changes and What Does NotAI is entering forecasting, accounting, valuation, credit, reconciliation, tax, risk management, and financial decision-making. Yet one question receives far less attention: Who should sign when the person responsible for the decision cannot personally validate how the system produced the answer?This book addresses that governance gap.AI in Finance: What Actually Changes and What Does Not introduces the Signer/Builder Gap, the growing distance between those who build or configure AI systems and the executives who remain accountable for their outputs. Through practical finance cases, the book examines where automation belongs, where human judgment must remain, and why high model accuracy does not remove the need for verification.The book develops practical frameworks for executives, finance leaders, risk professionals, auditors, tax professionals, and boards. These include the Capability Matrix, Validation Gap Framework, Claims-Testing Protocol, Decision-Cost Lens, Board Translation Script, and Signer's Sign-Off Protocol. Together, they provide a structured way to evaluate AI-supported decisions, challenge vendor claims, identify high-risk automation, test whether outputs remain trustworthy, and establish accountability before a number reaches a board, regulator, customer, investor, or tax authority.The central message is simple. AI changes how financial work gets performed. It does not remove professional responsibility.Executives do not need to become data scientists. They need a stronger discipline for deciding what deserves trust, what requires independent verification, who remains accountable, and what evidence should exist before they sign.For finance leaders navigating AI adoption, this book offers a practical framework for one of the defining governance questions of the coming decade: when machines increasingly produce the numbers, what must humans still verify before putting their name behind them? This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…