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7 results for “Financial Inclusion”
Survey data on financial literacy, financial inclusion, informal financial business practices, and intentions towards formalization of female small vendors in Lima, Peru
<p><span>This dataset encapsulates a comprehensive survey aimed at understanding informal business practices and financial literacy among small business vendors in Peru. The dataset comprises three key components: the survey questionnaire, raw survey data, and a detailed codebook. Researchers interested in the dynamics of financial practices in emerging markets may find this dataset particularly valuable, as it allows for the exploration of factors influencing financial decisions in small enterprises, with potential modifications suggested for adapting the survey to different national or cultural contexts. This dataset not only contributes to empirical research in financial behavior but also supports gender-specific studies by allowing the variable 'sex' to be adapted to 'gender' with multiple response options. </span></p> <p><span>The data and supplementary material is divided in tree files:</span></p> <p><span>The survey, presented in "Survey IFE.docx," includes questions across various domains such as informal business practices, financial literacy, financial inclusion, intentions towards financial formalization, and the formality of business ventures, along with demographic variables like age, sex, business age, and number of employees. </span></p> <p><span>The raw data, stored in "Dataset.csv," records responses from 118 participants, mapped against 31 indicators. </span></p> <p><span>The "Codebook.doc" provides exhaustive details about the survey variables, coding of responses, and the methodology employed, facilitating the replication of the study and application of the dataset in varied research contexts.</span></p>
INNOVATIVE MACHINE LEARNING APPROACHES TO FOSTER FINANCIAL INCLUSION IN MICROFINANCE
<p>This study examines the application of machine learning algorithms to enhance financial inclusion in microfinance, focusing on credit scoring, risk and fraud detection, and customer segmentation. We performed feature engineering and employed models such as Logistic Regression, Decision Trees, Random Forests, Gradient Boosting Machines (XGBoost and LightGBM), Support Vector Machines (SVM), Autoencoders, Isolation Forests, and K-means Clustering. LightGBM achieved the highest accuracy (89.6%) and AUC (0.92) in credit scoring, while Random Forests demonstrated strong performance in both loan approval (86.7% accuracy) and fraud detection (87.6% accuracy, AUC of 0.88). SVM also performed competitively, and unsupervised methods like Autoencoders and Isolation Forests showed potential for anomaly detection but required further refinement.K-means Clustering excelled in customer segmentation with a silhouette score of 0.72, enabling tailored services based on client demographics. Our findings highlight the significant impact of machine learning on improving credit scoring accuracy, reducing fraud risks, and enhancing customer service delivery in microfinance, thereby promoting financial inclusion for underserved populations. Ethical considerations and model interpretability are crucial, particularly for smaller institutions. This study advocates for the broader adoption of machine learning in the microfinance sector.</p>
Financial Inclusion Indicators from IMF and World Bank Datasets
<p>This dataset is a combination of IMF and World Bank financial and financial inclusion data from the years 2019 and 2017 respectively. The data is compiled for countries pursuing central bank digital currencies and is used to compare financial inclusion measures across states.</p>
Financial Inclusion
<p>The dataset is on Financial Inclusion of women in slums. </p>
Financial inclusion and institutions raw data
<p>Financial inclusion data set obtained from International Monetary Fund’s annual Financial Access Survey, institutional data obtained from Worldwide Governance Indicators and control variables data obtained from WDI all corresponding to 125 countries for the period 2004-2015</p>
Impact of the Financial Inclusion Improves Sanitation and Health
ClinicalTrials.gov study NCT06356636. IPD Sharing: YES. Countries: 1. Publications: 0.
WoS and Scopus integration of financial inclusion papers
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.