A credit card as a banking product has a dual nature both as a convenient loan and a
payment tool. Credit card profitability prediction is a complex problem because of the
variety of the card holders’ behaviour patterns, a fluctuating balance, and different
sources of interest and transactional income. The state of a credit card account depends
on the type of card usage and payments delinquency, and can be defined as inactive,
transactor, revolver, delinquent, and default. The proposed credit cards profit
prediction model consists of four stages: i) utilisation rate and interest rate income
prediction, ii) non-interest rate income prediction, iii) account state prediction with
conditional transition probabilities, and iv) the aggregation of the partial models into
total income estimation.
This thesis describes an approach to credit card account-level profitability prediction
based on multistate and multistage conditional probabilities models with different
types of income and compares methods for the most accurate predictions. We use
application, behavioural, card state, and macroeconomic characteristics as predictors.
This thesis contains nine chapters: Introduction, Literature Review, six chapters giving
descriptions of the data, methodologies and discussions of the results of the empirical
investigation, and Conclusion.
Introduction gives the key points and main aims of the current research and describes
the general schema of the total income prediction model. Literature Review proposes
a systematic analysis of academic work on loan profit modelling and highlights the
gaps in the application of profit scoring to credit cards income prediction. Chapter 3
describes the data sample and gives the overview of characteristics.
Chapter 4 is dedicated to the prediction of the credit limit utilisation and contains the
comparative analysis of the predictive accuracy of different regression models. We
apply five methods such as i) linear regression, ii) fractional regression, iii) beta-regression,
iv) beta-transformation, and v) weighted logistic regression with data
binary transformation for utilisation rate prediction for one- and two-stage models.
Chapters 5 and 6 are dedicated to modelling the transition probabilities between credit
card states. Chapter 5 describes the general model setups, model building methodology
such as transition probability prediction with conditional binary logistic, ordinal, and
multinomial regressions, the data sample description, the univariate analysis of
predictors. Chapter 6 discusses regression estimation results for all types of regression
and a comparative analysis of the models.
Chapter 7 describes an approach to the non-interest rate income prediction and
contains a comparative analysis of panel data regression techniques such as pooled and
four random effect methods. We consider two sources of non-interest income
generation: i) interchange fees and foreign exchange fees from transactions via pointof-
sales (POS) and ii) ATM fees from cash withdrawals. We compare the predictive
accuracy of a one-stage approach, which means the usage of a single linear model for
the income amount estimation, and a two-stage approach, which means that the income
amount conditional on the probability of POS and ATM transaction.
Chapter 8 aggregates the results from the partial models into a single model for total
income estimation. We assume that a credit card account does not have a single
particular state and a single behavioural type in the future, but has a chance to move
to any of possible states. The income prediction model is selected according to these
states, and the transition probabilities are used as weights for the particular interest rate
and non-interest rate income prediction models.
Conclusion highlights the contributions of this research. We propose an innovative
methodological approach for credit card income prediction as a system of models,
which considers the estimation of the income from different sources and then
aggregates the income estimations weighted by the states transition probabilities. The
results of comparative analysis of regression methods for: i) utilization rate of credit
limit and ii) non-interest income prediction, iii) the use of panel data with pooled and
random effect for profit scoring, and iv) account level non-binary target transition
probabilities estimation for credit cards can be used as benchmarks for further research
and fill the gaps of empirical investigations in the literature. The estimation of the
transition probability between states at the account level helps to avoid the
memorylessness property of the Markov Chains approach. We have investigated the
significance of predictors for models of this type. The proposed modelling approach
can be applied for the development of business strategies such as credit limit
management, customer segmentation by the profitability and behavioural type.