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Rural credit is one of the most critical inputs for farm production across the globe. Despite so many advances in digitalization in emerging and developing economies, still a large part of society like small farm holders, rural youth, and women farmers are untouched by the mainstream of banking transactions. Machine learning-based technology is giving a new hope to these individuals. However, it is the banking or non-banking institutions that decide how they will adopt this advanced technology, to have reduced human biases in loan decision making. Therefore, the scope of this study is to highlight the various AI-ML- based methods for credit scoring and their gaps currently in practice by banking or non-banking institutions. For this study, systematic literature review methods have been applied; existing research articles have been empirically reviewed with an attempt to identify and compare the best fit AI-ML-based model adopted by various financial institutions worldwide. The main purpose of this study is to present the various ML algorithms highlighted by earlier researchers that could be fit for a credit assessment of rural borrowers, particularly those who have no or inadequate loan history. However, it would be interesting to recognize further how the financial institutions could be able to blend the traditional and digital methods successfully without any ethical challenges.

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Cloud computing provides a set of resources and services for customers on the Internet on demand and based on a pay as you go model. Cloud providers are looking to decrease costs and increase profits. Therefore, resource management and provisioning are very important for cloud providers. Automated scaling can be used to provide resources for user requests. Auto‐scaling can decrease the total operational costs for providers, although it does have its own cost and time overheads. In this paper, a new solution is presented for resource provisioning on multi‐layered cloud applications based on MAPE‐K loop. A weighted ensemble prediction model is proposed to estimate the resources utilization in each cloud layer. In addition, accuracy of the model and a regularization technique are used to regulate the weights of the models in the proposed hybrid prediction model. Furthermore, a decision tree‐based algorithm is presented to analyze status of the resources to make scaling decision. In addition, we propose a resource allocation algorithm that is based on Virtual Machine priority and request deadline in order to allocate requests on suitable resources. The experimental results indicate that the proposed algorithm has the best performance among its counterparts.