Credit-Card Engagement Segmentation for Embedded FinTech

Product Strategy: Evidence from Expenditure Microdata

Samandarboy Sulaymanov1,* Olimjonov Abbosjon Olimjonovich1

1 Tashkent State University of Economics, Tashkent, Uzbekistan

Received: January 03, 2025 Revised: March 01, 2025 Accepted: June 04, 2025 ⋆ Corresponding author

ABSTRACT

Digital card products create business value only when issued accounts translate into sustained, responsible use.

This paper develops an expenditure-based segmentation model for embedded FinTech card strategy using a real

credit-card micro-dataset. Average monthly card expenditure is treated as an observable engagement outcome and is

examined alongside income, age, and home-ownership status. The empirical design combines descriptive portfolio

profiling, robust regression, cross-validated prediction, and product-action mapping. The results show that income is

the strongest observed driver of monthly spend, but the relationship is nonlinear and does not fully explain customer

heterogeneity. A quartile-based segmentation separates low-use, developing-use, active-use, and premium-use

customers, with mean monthly expenditure increasing from 39.92 to 666.35 across the four operating segments.

The analysis argues that card engagement should be managed as a portfolio state rather than as a simple activation

metric. The study contributes a transparent business-analytics framework that links observed card expenditure to

embedded-finance decisions, including activation support, limit calibration, reward design, and repayment-aware

engagement monitoring.

Keywords: Financial technology Credit-card analytics Embedded finance Customer engagement Behavioral segmentation

1. INTRODUCTION

Card-based financial technology is no longer defined only by

the issuance of payment instruments. Digital card providers,

marketplace lenders, banking-as-a-service platforms, and

embedded-finance vendors increasingly compete by translating

transaction and profile data into product decisions. A

card platform must decide which customers require activation

support, which customers can be offered additional services,

and which customers may be approaching a level of engagement

that should be monitored carefully. These decisions

are commercial, but they are also risk-sensitive because card

engagement combines payment convenience, credit exposure,

and household liquidity behaviour.

This paper studies card engagement as a measurable business

phenomenon. Instead of modelling a binary approval

decision or a default event, it focuses on the level of monthly

credit-card expenditure. This perspective is useful for Fin-

Tech product strategy because digital card value is created

after issuance. Issuing a card with no use has little strategic

value; encouraging excessive use may create repayment pressure;

and treating all active customers alike misses important

differences in income capacity and usage intensity.

The analysis uses Bill Greene’s credit-scoring data as distributed

in the statsmodels datasets module [1]. The dataset is

modest in size but valuable for method development because

it contains observed monthly card expenditure together with

income, age, and home ownership. These variables allow