Financial Inclusion

Explainable Federated Learning for U.S. State-Level Financial Distress Modeling ArXiv ID: 2511.08588 “View on arXiv” Authors: Lorenzo Carta, Fernando Spadea, Oshani Seneviratne Abstract We present the first application of federated learning (FL) to the U.S. National Financial Capability Study, introducing an interpretable framework for predicting consumer financial distress across all 50 states and the District of Columbia without centralizing sensitive data. Our cross-silo FL setup treats each state as a distinct data silo, simulating real-world governance in nationwide financial systems. Unlike prior work, our approach integrates two complementary explainable AI techniques to identify both global (nationwide) and local (state-specific) predictors of financial hardship, such as contact from debt collection agencies. We develop a machine learning model specifically suited for highly categorical, imbalanced survey data. This work delivers a scalable, regulation-compliant blueprint for early warning systems in finance, demonstrating how FL can power socially responsible AI applications in consumer credit risk and financial inclusion. ...

Cash Flow Underwriting with Bank Transaction Data: Advancing MSME Financial Inclusion in Malaysia ArXiv ID: 2510.16066 “View on arXiv” Authors: Chun Chet Ng, Wei Zeng Low, Jia Yu Lim, Yin Yin Boon Abstract Despite accounting for 96.1% of all businesses in Malaysia, access to financing remains one of the most persistent challenges faced by Micro, Small, and Medium Enterprises (MSMEs). Newly established businesses are often excluded from formal credit markets as traditional underwriting approaches rely heavily on credit bureau data. This study investigates the potential of bank statement data as an alternative data source for credit assessment to promote financial inclusion in emerging markets. First, we propose a cash flow-based underwriting pipeline where we utilise bank statement data for end-to-end data extraction and machine learning credit scoring. Second, we introduce a novel dataset of 611 loan applicants from a Malaysian lending institution. Third, we develop and evaluate credit scoring models based on application information and bank transaction-derived features. Empirical results show that the use of such data boosts the performance of all models on our dataset, which can improve credit scoring for new-to-lending MSMEs. Finally, we will release the anonymised bank transaction dataset to facilitate further research on MSME financial inclusion within Malaysia’s emerging economy. ...

Towards Financially Inclusive Credit Products Through Financial Time Series Clustering ArXiv ID: 2402.11066 “View on arXiv” Authors: Unknown Abstract Financial inclusion ensures that individuals have access to financial products and services that meet their needs. As a key contributing factor to economic growth and investment opportunity, financial inclusion increases consumer spending and consequently business development. It has been shown that institutions are more profitable when they provide marginalised social groups access to financial services. Customer segmentation based on consumer transaction data is a well-known strategy used to promote financial inclusion. While the required data is available to modern institutions, the challenge remains that segment annotations are usually difficult and/or expensive to obtain. This prevents the usage of time series classification models for customer segmentation based on domain expert knowledge. As a result, clustering is an attractive alternative to partition customers into homogeneous groups based on the spending behaviour encoded within their transaction data. In this paper, we present a solution to one of the key challenges preventing modern financial institutions from providing financially inclusive credit, savings and insurance products: the inability to understand consumer financial behaviour, and hence risk, without the introduction of restrictive conventional credit scoring techniques. We present a novel time series clustering algorithm that allows institutions to understand the financial behaviour of their customers. This enables unique product offerings to be provided based on the needs of the customer, without reliance on restrictive credit practices. ...

Theories of Financial Inclusion ArXiv ID: ssrn-3526548 “View on arXiv” Authors: Unknown Abstract This article presents several theories of financial inclusion. Financial inclusion is defined as the availability of, and the ease of access to, basic formal fi Keywords: Financial Inclusion, Formal Finance, Economic Development, Banking Accessibility, Credit Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is a conceptual review that categorizes existing theories of financial inclusion without presenting new mathematical models or empirical data analysis. It focuses on theoretical frameworks and policy discussions rather than quantitative methods or backtesting. flowchart TD A["Research Goal: Explore Theories of Financial Inclusion"] --> B["Methodology: Literature Review of Key Theories"] B --> C["Data: Academic Papers & Economic Studies"] C --> D["Computational Process: Analysis of Access Barriers & Impacts"] D --> E{"Outcomes"} E --> F["Theory 1: Supply-Side Constraints"] E --> G["Theory 2: Demand-Side Barriers"] E --> H["Theory 3: Institutional Frameworks"] F & G & H --> I["Key Finding: Link between Formal Finance & Economic Development"]

Fintech for Financial Inclusion: A Framework for Digital Financial Transformation ArXiv ID: ssrn-3245287 “View on arXiv” Authors: Unknown Abstract Access to finance, financial inclusion and financial sector development have long been major policy objectives. A series of initiatives have aimed to increase a Keywords: Financial Inclusion, Access to Finance, Financial Sector Development, Microfinance, Credit Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is a policy-oriented framework discussing regulatory strategies and digital infrastructure, lacking mathematical formulas or statistical models; its empirical support relies on high-level case studies (e.g., India, Kenya) and aggregated data from sources like the World Bank, with no backtesting or implementation details. flowchart TD A["Research Goal:<br/>Framework for Digital Financial Transformation"] --> B["Data & Inputs:<br/>Policy Initiatives & Microfinance Data"] B --> C["Methodology:<br/>Thematic Analysis & Synthesis"] C --> D["Computational Process:<br/>Mapping Inclusion to Digital Tech"] D --> E["Key Findings:<br/>Fintech as Catalyst for<br/>Financial Sector Development"]

Financial Inclusion in Africa: An Overview ArXiv ID: ssrn-2084599 “View on arXiv” Authors: Unknown Abstract This paper summarizes financial inclusion across Africa. First, it provides a brief overview of the African financial sector landscape. Second, it uses the Glob Keywords: Financial Inclusion, Microfinance, Emerging Markets, Banking Sector, Emerging Markets Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 6.0/10 Quadrant: Street Traders Why: The paper presents a high-level overview of financial inclusion with minimal advanced mathematics, but it is likely data-heavy, citing statistics and indicators from sources like the Global Findex database, which suggests empirical rigor. flowchart TD A["Research Goal<br>Assess financial inclusion trends<br>in Africa"] --> B{"Methodology"} B --> C["Data Sources<br>GSMA, World Bank, Global Findex"] B --> D["Analysis Framework<br>Cross-country comparison &<br>trend analysis"] C --> E["Computational Process<br>Descriptive statistics &<br>comparative metrics"] D --> E E --> F["Key Findings<br>- Mobile money drives inclusion<br>- Banking sector gaps remain<br>- Policy implications for EMs"]

Measuring Financial Inclusion: The Global Findex Database ArXiv ID: ssrn-2043012 “View on arXiv” Authors: Unknown Abstract This paper provides the first analysis of the Global Financial Inclusion (Global Findex) Database, a new set of indicators that measure how adults in 148 econom Keywords: Financial Inclusion, Global Findex, Banking, Emerging Markets, General (Financial Inclusion) Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 9.0/10 Quadrant: Street Traders Why: The paper is an empirical analysis of a massive, newly collected survey dataset (Global Findex) across 148 economies, focusing on descriptive statistics and policy implications rather than advanced mathematical modeling or derivations. flowchart TD A["Research Goal<br>Measure & analyze global financial inclusion"] --> B["Data Collection<br>Global Findex Database<br>148 economies, ~150k adults"] B --> C["Methodology<br>Define indicators & stratified sampling"] C --> D["Computation<br>Statistical analysis of inclusion patterns"] D --> E["Key Findings<br>Usage gaps, barriers, & policy insights"]