scholarly journals Experimental Application of Machine Learning on Financial Inclusion Data for Governance in Eswatini

2020 ◽  
pp. 414-425
Author(s):  
Boluwaji A. Akinnuwesi ◽  
Stephen G. Fashoto ◽  
Andile S. Metfula ◽  
Adetutu N. Akinnuwesi
Keyword(s):  
Machine Learning ◽  
Financial Inclusion ◽  
Experimental Application

FinTech Adoption in China

2021 ◽  
pp. 166-173
Author(s):  
Gagan Kukreja
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Financial Services ◽  
New Technologies ◽  
Large Population ◽  
Financial Inclusion ◽  
High Penetration ◽  
Digital Payments ◽  
Almost All

Almost all financial services (especially digital payments) in China are affected by new innovations and technologies. New technologies such as blockchain, artificial intelligence, machine learning, deep learning, and data analytics have immensely influenced all most all aspects of financial services such as deposits, transactions, billings, remittances, credits (B2B and P2P), underwriting, insurance, and so on. Fintech companies are enabling larger financial inclusion, changing in lifestyle and expenditure behavior, better and fast financial services, and lots more. This chapter covers the development, opportunities, and challenges of financial sectors because of new technologies in China. This chapter throws the light on opportunities that emerged because of the large population of 1.4 billion people, high penetration, and access to the latest and affordable technology, affordable cost of smartphones, and government policies and regulations. Lastly, this chapter portrays the untapped potentials of Fintech in China.

The Machine Learning Embedded Method of Parameters Determination in the Constitutive Models and Potential Applications for Hydrogels

2021 ◽  
Vol 13 (01) ◽  
pp. 2150001 ◽  
Author(s):  
Shoujing Zheng ◽  
Zishun Liu
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Chemical Potential ◽  
Constitutive Models ◽  
Experimental Application ◽  
Potential Applications ◽  
The Neural Networks ◽  
Hydrogel Swelling ◽  
Embedded Method ◽  
Fitting Parameters

We propose a machine learning embedded method of parameters determination in the constitutional models of hydrogels. It is found that the developed logistic regression-like algorithm for hydrogel swelling allows us to determine the fitting parameters based on known swelling ratio and chemical potential. We also put forward the neural networks-like algorithm, which, by its own property, can converge faster as the layer deepens. We then develop neural networks-like algorithm for hydrogel under uniaxial load for experimental application purpose. Finally, we propose several machine learning embedded potential applications for hydrogels, which would provide directions for machine learning-based hydrogel research.

Where are the Unbanked in Belize?: Using Machine Learning Small Area Estimation to Improve Financial Inclusion Geographic Targeting

2021 ◽  
Author(s):  
Jonathan Hersh ◽  
Lucia Martin Rivero ◽  
Janelle Leslie
Keyword(s):  
Machine Learning ◽  
Financial Services ◽  
Small Area ◽  
Small Area Estimation ◽  
Geographical Area ◽  
Financial Inclusion ◽  
Area Estimation ◽  
Task Forces ◽  
Effective Implementation ◽  
Financial Characteristics

This study aims to contribute to the efficient and effective implementation of Belize's National Financial Inclusion Strategy (NFIS) that was launched by the Central Bank of Belize in 2019. It employs Machine Learning Based Small Area Estimation to develop granular estimates of Financial Inclusion at the smallest geographical level know as Enumeration Districts (ED) that were previously unavailable for Belize. To gain deeper understanding of the populations financial characteristics at the ED level, we build five measures of access to banking and financial services. Significant clustering of financial inclusion metrics that are not apparent in the district level averages are identified. This study also analyzes the factors that influence the use of financial services and instruments in order to propose appropriate adjustments in the strategies implemented by authorities in each geographical area. Both the spatial distribution of Financial Inclusion indicators and the factors influencing the adoption of financial services shed light on specific recommendations relevant to each of the four Thematic Financial Inclusion Task Forces included in the NFIS.

Accelerating Financial Inclusion in Cambodia Using Artificial Intelligence & Machine Learning

2020 ◽  
Vol 11 (12) ◽  
pp. 79-88
Author(s):  
Anil K. Makhija
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Economic Growth ◽  
Sustainable Development ◽  
United Nations ◽  
Sustainable Development Goals ◽  
Social Needs ◽  
Financial Inclusion ◽  
Development Goals ◽  
Financial Transactions

Promoting prosperity and protecting the planet at the same time requires us to end poverty and simultaneously promote economic growth and address social needs. This is also reflected in the form of 17 sustainable development goals agenda set by United Nations. Financial inclusion has been identified as an enabler for 7 out of those 17 sustainable development goals. Financial inclusion requires I\individuals and businesses to have access and ability to do financial transactions, payments, get credit and insurance.

scholarly journals Financial Inclusion: An Application of Machine Learning in Collaborative Filtering Recommender Systems

2020 ◽  
Vol 8 (6) ◽  
pp. 4243-4247
Keyword(s):  
Machine Learning ◽  
Recommendation System ◽  
Learning Technologies ◽  
Financial Inclusion ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Current Scenario ◽  
Current Algorithm ◽  
The Impact

In the current scenario in finance, data play a major role for predicting stock market as well as verious financial instruments. For the estimation of financial data, the various algorithms and models have been used. The use of the advising method has been used in this paper. The advising programs are one of the main methodologies used in the present market scenario with machine learning technologies. This paper focuses on the impact of financial inclusion in Odisha using a machine learning approach such as the classification of kNearest Neighbors (k-NN). For financial inclusion systems, machine learning has become a commonly used method. The result takes into the ATMs, Banks and BCs ranking in different districts of Odisha. We used the k-Nearest Neighbor's machine learning methodology classification algorithm to characterize the recommendation system based on users of the mentioned populations. Using our approach we equate conventional collective filtering. Our results show that the linear algorithm is more reliable than the current algorithm and is more efficient and stable than current methods

Sign in / Sign up

Export Citation Format

Share Document