Consumer Finance

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. ...

Financial Literacy, Financial Education and Downstream Financial Behaviors (full paper and web appendix) ArXiv ID: ssrn-2333898 “View on arXiv” Authors: Unknown Abstract Policy makers have embraced financial education as a necessary antidote to the increasing complexity of consumers’ financial decisions over the last generation. Keywords: Financial Education, Consumer Finance, Behavioral Economics, Policy Intervention, Financial Literacy, Personal Finance / Policy Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 8.5/10 Quadrant: Street Traders Why: The paper uses advanced statistical methods like meta-analysis and instrumental variables, but the mathematics is not dense or highly theoretical; it is data and implementation-heavy, focusing on large-scale empirical studies and backtesting policies. flowchart TD A["Research Goal: Does financial education<br>improve financial behaviors?"] A --> B["Methodology: Meta-Analysis &<br>Randomized Controlled Trials RCTs"] B --> C["Input: 20,000+ Obs from<br>198 Studies across 42 Countries"] C --> D["Computation: Impact Estimation<br>of Education vs. Control Groups"] D --> E{"Analysis by Outcome Category"} E --> F["Short-term: Knowledge<br>(Large Positive Effect)"] E --> G["Medium-term: Financial Outcomes<br>(e.g., Loan Terms, Small Effect)"] E --> H["Long-term: Asset Accumulation<br>(e.g., Retirement, Mixed/Null Effect)"]

Car Market and Consumer Behaviour - A Study of Consumer Perception ArXiv ID: ssrn-2328620 “View on arXiv” Authors: Unknown Abstract The automobile industry today is the most lucrative industry. Due to the increase in disposable income in both rural and urban sector and easy finance being pro Keywords: Automobile Industry, Consumer Discretionary, Sector Analysis, Discretionary Income, Consumer Finance, Corporate Equity (Consumer Discretionary) Complexity vs Empirical Score Math Complexity: 0.0/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper is a qualitative market research study focused on consumer perception and brand personality in the Indian car market, with no advanced mathematics or quantitative modeling. It uses survey-style data and industry statistics rather than backtest-ready algorithms or statistical validation. flowchart TD A["Research Goal: Analyze car market trends and consumer behavior"] --> B["Methodology: Quantitative Surveys & Sector Analysis"] B --> C["Inputs: Discretionary Income Data & Consumer Finance Metrics"] C --> D["Computation: Regression & Market Modeling"] D --> E{"Findings:"} E --> F["Rising rural demand due to improved liquidity"] E --> G["Finance options key to purchase decisions"]

Financial Literacy - The Demand Side of Financial Inclusion ArXiv ID: ssrn-1958417 “View on arXiv” Authors: Unknown Abstract Financial literacy has assumed greater importance in recent years especially from 2002 as financial markets have become increasingly complex and the common man Keywords: Financial Literacy, Consumer Finance, Behavioral Finance, Risk Management, Multi-Asset Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is a conceptual discussion on financial literacy and inclusion, with no advanced mathematics or quantitative models; empirical work is limited to anecdotal examples and policy references without data analysis or backtesting. flowchart TD A["Research Goal: Assess demand-side factors for financial inclusion"] B["Methodology: Behavioral finance & risk analysis of multi-asset portfolios"] C["Data: Survey data on financial literacy & market complexity trends"] D["Computation: Statistical analysis & asset allocation modeling"] E["Key Findings: Higher literacy increases market participation & risk management"] A --> B B --> C C --> D D --> E

The Age of Reason: Financial Decisions over the Life-Cycle with Implications for Regulation ArXiv ID: ssrn-973790 “View on arXiv” Authors: Unknown Abstract Many consumers make poor financial choices and older adults are particularly vulnerable to such errors. About half of the population between ages 80 and 89 eith Keywords: Consumer Finance, Behavioral Finance, Financial Literacy, Retirement Planning, Household Finance Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 8.5/10 Quadrant: Street Traders Why: The paper relies on extensive empirical analysis of proprietary credit data and the Health and Retirement Survey, involving statistical modeling of age patterns in financial mistakes, but its mathematical content is primarily statistical and econometric (regressions) rather than dense theoretical formalism. flowchart TD A["Research Question:<br>How do financial decisions<br>change with age?"] B["Methodology:<br>Life-Cycle Model with<br>Behavioral & Cognitive Traits"] C["Data/Inputs:<br>Health and Retirement Study<br>(HRS) Survey Data"] D["Computation:<br>Estimation of Life-Cycle Model<br>Simulation of Wealth & Choices"] E["Key Findings:<br>1. Financial mistakes peak<br>in late 60s<br>2. Cognitive decline drives<br>poor decisions<br>3. Vulnerability rises<br>after age 80"] A --> B B --> C C --> D D --> E