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on Financial Literacy and Education |
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Issue of 2024‒01‒15
five papers chosen by |
| By: | Alan Gelb (Center for Global Development); Anit Mukherjee (Center for Global Development); Brian Webster (Center for Global Development) |
| Abstract: | Bangladesh’s Primary Education Stipend Program (PESP) provides stipends for 13 million primary schoolchildren to 10 million mothers. In 2017 the method of payment changed from cash to mobile money. This study considers the experience of the mothers with the shift to mobile money, and to the change in payments service provider that took place in 2019, through a survey of recipients and a control group. We explore the experience and perception of beneficiaries vis-a-vis the receipt of digital transfers, convenience of transactions and ability to use digital payment systems. We also consider spillovers onto financial inclusion and use, and whether the stipend and the method of payment, has increased women’s economic empowerment. Our analysis indicates that PESP beneficiaries overwhelmingly support the transition to digital payments. While a modest number think that cash-out points are too far away, they are in general, satisfied with the convenience of making withdrawals. We find positive perception of women in terms of their degree of control over the use of the funds following the shift to digital payment, particularly for those who have their own phone. Taking account of the views of the beneficiaries themselves, our survey provides support to the proposition that digital G2P payments have contributed to an improvement in women’s sense of empowerment and their ability to make independent decisions on matters relating to household finances and specifically, for their children. The study also finds positive spillover effects onto financial competition and inclusion more generally, with the rapid growth of the new payment service provider for PESP stipends. Finally, digital transfer of stipends through mobile phone wallets can provide a “nudge” towards the use of digital transactions for other purposes, but this is conditional on personal attributes of beneficiaries. For many mothers, limited digital literacy and capacity to read and write SMS presents a serious barrier to greater uptake of digital financial services, an issue that needs to be addressed by policymakers in Bangladesh as well as globally. |
| Date: | 2023–02–10 |
| URL: | http://d.repec.org/n?u=RePEc:cgd:ppaper:284&r=fle |
| By: | Alan Gelb (Center for Global Development); Anit Mukherjee (Center for Global Development); Brian Webster (Center for Global Development) |
| Abstract: | Kenya moved towards electronic payments of social benefits in 2013. In 2018 the payments system for its premier social protection program, Inua Jamii, was restructured to offer most, but not all, beneficiaries a choice between several payment service providers (PSPs), all commercial banks. This study surveys the payment system from the perspective of recipients, including their views on convenience and the benefits from competition. It also considers whether these digital G2P payments programs have increased financial inclusion more generally – recognizing that this was already high in Kenya due to the market penetration of M-Pesa digital wallets. It finds strong support for making payments through financial accounts. The overwhelming majority of respondents consider this to be a good system, with some favoring the commercial bank channel and others expressing a preference for direct payments through wallets. There is strong support for offering choice where this is feasible, but we find that the single payer G2P model can also be effective depending on local conditions. While social transfers may have enabled poor people to afford cell phones and mobile money accounts, the system can be developed further to enhance financial services access. |
| Date: | 2023–01–19 |
| URL: | http://d.repec.org/n?u=RePEc:cgd:ppaper:282&r=fle |
| By: | Ali, Amjad; Khokhar, Bilal; Sulehri, Fiaz Ahmad |
| Abstract: | This study explores the relationship between inflationary pressure and policy mix in developing countries over the period of 1995 to 2022. Money supply, unemployment rate, regulatory policies, currency rate, remittances, and amount of foreign debt are explanatory factors, whereas inflationary pressure is the dependent variable. To assess the influence of these factors on inflation, panel least squares, and fixed effect models are utilized. The study's findings shed light on the complicated links between financial factors and inflationary pressures in developing nations. The study demonstrates that in developing nations, the money supply has a negative and considerable influence on inflation. The study found that unemployment had a favorable but insignificant influence on inflation pressures in emerging nations. Furthermore, the research demonstrates that regulatory measures have a negative and considerable influence on inflationary pressures. The exchange rate has been proven to have a positive and considerable impact on inflationary pressures in emerging nations, highlighting the necessity of prudent exchange rate management in mitigating the inflationary implications of currency decline. Furthermore, remittances have a negative and considerable influence on inflationary pressures, implying that increasing financial inclusion and investment possibilities for remittance-receiving families might help to stabilize inflation in developing countries. Finally, the study emphasizes that the quantity of foreign debt in emerging nations has a positive and considerable influence on inflationary pressures. According to the study, careful monitoring and control of the money supply, addressing unemployment through labor market reforms and investments, implementing effective regulatory restrictions, prudent exchange rate management, promoting financial inclusion for remittance recipients, and pursuing sustainable debt levels are all important. |
| Keywords: | Money Supply, Unemployment Rate, Regulatory Policies, Currency Rate, Foreign Debt, Remittances |
| JEL: | E24 E51 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:119364&r=fle |
| By: | David Hason Rudd (UTS - University of Technology Sydney); Huan Huo (UTS - University of Technology Sydney); Md. Rafiqul Islam (UTS - University of Technology Sydney); Guandong Xu (UTS - University of Technology Sydney) |
| Abstract: | In today's competitive landscape, businesses grapple with customer retention. Churn prediction models, although beneficial, often lack accuracy due to the reliance on a single data source. The intricate nature of human behavior and highdimensional customer data further complicate these efforts. To address these concerns, this paper proposes a multimodal fusion learning model for identifying customer churn risk levels in financial service providers. Our multimodal approach integrates customer sentiments, financial literacy (FL) level, and financial behavioral data, enabling more accurate and bias-free churn prediction models. The proposed FL model utilizes a SMOGN-COREG supervised model to gauge customer FL levels from their financial data. The baseline churn model applies an ensemble artificial neural network and oversampling techniques to predict churn propensity in high-dimensional financial data. We also incorporate a speech emotion recognition model employing a pretrained CNN-VGG16 to recognize customer emotions based on pitch, energy, and tone. To integrate these diverse features while retaining unique insights, we introduced late and hybrid fusion techniques that complementary boost coordinated multimodal colearning. Robust metrics were utilized to evaluate the proposed multimodal fusion model and hence the approach's validity, including mean average precision and macro-averaged F1 score. Our novel approach demonstrates a marked improvement in churn prediction, achieving a test accuracy of 91.2%, a Mean Average Precision (MAP) score of 66, and a Macro-Averaged F1 score of 54 through the proposed hybrid fusion learning technique compared with late fusion and baseline models. Furthermore, the analysis demonstrates a positive correlation between negative emotions, low FL scores, and high-risk customers. |
| Abstract: | Dans le paysage concurrentiel actuel, les entreprises sont confrontées à des défis en matière de rétention de la clientèle. Bien qu'utiles, les modèles de prédiction du churn manquent souvent de précision en raison de leur dépendance à une seule source de données. La nature complexe du comportement humain et les données clients de haute dimension compliquent davantage ces efforts. Pour répondre à ces préoccupations, cet article propose un modèle d'apprentissage par fusion multimodale pour identifier les niveaux de risque de churn chez les clients des prestataires de services financiers. Notre approche multimodale intègre les sentiments des clients, le niveau de littératie financière (LF) et les données comportementales financières, permettant des modèles de prédiction du churn plus précis et exempts de biais. Le modèle LF proposé utilise un modèle supervisé SMOGN-COREG pour évaluer les niveaux de LF des clients à partir de leurs données financières. Le modèle de base du churn applique un réseau de neurones artificiels en ensemble et des techniques de suréchantillonnage pour prédire la propension au churn dans des données financières de haute dimension. Nous incorporons également un modèle de reconnaissance des émotions vocales utilisant un CNN-VGG16 pré-entraîné pour reconnaître les émotions des clients en fonction de la hauteur, de l'énergie et du ton. Pour intégrer ces caractéristiques diverses tout en conservant des insights uniques, nous avons introduit des techniques de fusion tardive et hybride qui renforcent de manière complémentaire l'apprentissage coordonné multimodal. Des métriques robustes ont été utilisées pour évaluer le modèle de fusion multimodale proposé et donc la validité de l'approche, y compris la précision moyenne et le score F1 macro-moyenné. Notre approche innovante démontre une amélioration significative dans la prédiction du churn, atteignant une précision de test de 91, 2 %, un score de précision moyenne (MAP) de 66 et un score F1 macro-moyenné de 54 grâce à la technique d'apprentissage par fusion hybride proposée, comparée aux modèles de fusion tardive et de base. De plus, l'analyse montre une corrélation positive entre les émotions négatives, les faibles scores de LF et les clients à haut risque. |
| Keywords: | Churn prediction multimodal learning feature fusion financial literacy speech emotion recognition customer behavior, Churn prediction, multimodal learning, feature fusion, financial literacy, speech emotion recognition, customer behavior |
| Date: | 2023–10–30 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-04320145&r=fle |
| By: | Mr. Serhan Cevik |
| Abstract: | Rapid advances in digital technology are revolutionizing the financial landscape. The rise of fintech has the potential to make financial systems more efficient and competitive and broaden financial inclusion. With greater technological complexity, however, fintech also poses potential systemic risks. In this paper, I use a novel dataset to trace the development of fintech (excluding cryptocurrencies) and empirically assess its impact on financial stability in a panel of 198 countries over the period 2012–2020. The analysis provides interesting insights into how fintech correlates with financial stability: (i) the impact magnitude and statistical significance of fintech depend on the type of instrument (digital lending vs. digital capital raising); (ii) the overall effect of all fintech instruments together turns out to be negative because of the overwhelming share of digital lending in total, albeit statistically insignificant; and (iii) while digital capital raising is estimated to have a positive effect on financial stability in advanced economies, its effect is negative in developing countries. Fintech is still small compared to traditional institutions, but rapidly expanding in riskier segments of the financial sector and creating new challenges for policymakers. |
| Keywords: | Fintech; financial innovation; financial stability |
| Date: | 2023–12–08 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:2023/253&r=fle |