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.

Keywords: Financial Inclusion, Customer Segmentation, Time Series Clustering, Transaction Data, Risk Assessment, Consumer Finance

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 4.0/10
• Quadrant: Lab Rats
• Why: The paper introduces a novel time series clustering algorithm and discusses neural network architectures (autoencoders, LSTMs) with some mathematical formulations, indicating moderate-to-high math complexity. However, the excerpt focuses on theoretical background and methodology without presenting specific backtest results, statistical metrics, or code implementations, placing it in the ‘Lab Rats’ quadrant.

  flowchart TD
    A["Research Goal: Develop a clustering solution for financial time series data<br>to enable financially inclusive credit products without<br>restrictive conventional credit scoring."] --> B["Data Input: Customer Transaction Data"]
    
    B --> C["Methodology: Novel Time Series Clustering Algorithm<br>Applied to transaction history to segment customers"]
    
    C --> D["Computational Process: Pattern Recognition & Homogeneous Grouping<br>Identifies spending behavior clusters based on<br>temporal financial patterns"]
    
    D --> E["Key Outcomes:"]
    E --> F["1. Customer Segmentation:<br>Homogeneous groups based on financial behavior"]
    E --> G["2. Risk Assessment:<br>Understanding financial behavior without<br>restrictive credit scoring"]
    E --> H["3. Inclusive Products:<br>Unique credit, savings & insurance offerings<br>tailored to customer needs"]
    
    style A fill:#e1f5fe
    style B fill:#fff3e0
    style C fill:#f3e5f5
    style D fill:#e8f5e8
    style E fill:#fce4ec
    style F fill:#f1f8e9
    style G fill:#f1f8e9
    style H fill:#f1f8e9