Agriculture finance is a difficult ballgame. The critical information on which the entire business runs, and which is used by parties like lenders, index insurers, and other agencies is not in front of them and so has to be communicated to them. This covers points like actual existence and location of the field, what was sown and when, how was the growth, was there any infestation or not and if yes, how was the infestation eradicated, how much was the yield, and so on. Due to most of this actually happening away from the lending agencies, there is an asymmetry of information, which leaves a gap which fraudulent parties can exploit, with inflated yield, staged losses, and overstated claims. This is a point in the business, where emerging tools like blockchain and Internet of Things (IoT), remote sensing and different other related technologies need to be used as a combined approach, since neither of them alone can close the gap effectively. Blockchain provides a decentralized ledger of transactions with absolute transparency, where data is immutable and settlements are automated through smart contracts; however, it cannot verify the data itself. This is the “Oracle Problem”. IoT devices can collect and transmit data from the field level, but the devices can be spoofed, and there is no guarantee of the data quality either. Thus, the data captured and transmitted by the IoT devices, through a blockchain-based network needs to be verified thoroughly before any automatic action can be taken on it using the smart contracts. This chapter introduces a multi-layered conceptual framework, handling the tasks of data capture, automatic detection of anomalies and validation of data, ledger-based records using smart contracts and subsequent governance covering data privacy, data ownership and farmer participation. The framework looks at a few possible fraud domains, namely, lending, area-based subsidies, index insurance and finally carbon credits, taking as examples cases on different crops, subsidy monitoring using satellites, crop insurance using blockchain, and tokenized carbon credits. A study of the cases shows that the need for institutional action remains even after technical implementation, and that adoption of the technology depends on different factors including the trust it enjoys from the farmer level, the cost of implementation, interoperability with other technical and non-technical systems, and the benefits of sharing information and resources. The conclusion is that fraud-prevention is a socio-technical design problem where all three out of verified evidence, transparent rules and accountable governance are equally important, and empirical testing, and development of standards and policies to support systems that are dominated by smallholders need to be prioritized.