Optimizing Digital Finance and Regulatory Systems Through Intelligent Automation, Secure Data Architectures, and Advanced Analytical Technologies
DOI:
https://doi.org/10.53555/ks.v10i2.3786Keywords:
Finance and regulatory automatization - financial sector, digital transformation, cloud and on-perm. Graph databases, data lakes, blockchain, analytics, deep learning, AI, ML. Auto-Machine Learning (Auto-ML). Public, private, hybrid cloud. BI, data marts, data warehousing. API, SQL, artificial intelligence, on-premises, ETL, cloud functions, RDBMS.Abstract
The increasing ubiquity of digital financial products and services represents a potential game changer for the fight for the financial inclusion of smallholder farmers in developing countries. Digital financial services are generally more convenient, potentially less costly, and have a greater footprint than other financial services. They can enable smallholders to engage more easily with agriculture value chains and, so, adopt more economically productive behavior. As a result, smallholder finance is increasingly regarded as the engine of rural development and poverty reduction. However, digital finance also presents challenges for some smallholders. Formal banking institutions may rely on credit scores and other risk-mitigating modes of evaluation that are not applicable to many, if not most, agriculture producers, particularly in developing countries. Millions of smallholders might, therefore, be left out of emerging smallholder finance value chain relationships. In Wage Guzma, only 17.6% of households have access to digital financial services. Made agriculture payments were received by only 11.7% of households.
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Copyright (c) 2022 Abhishek Dodda, Phanish Lakkarasu, Jeevani Singireddy, Kishore Challa, Vamsee Pamisetty

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