6 research outputs found
Deep Generative Models for Reject Inference in Credit Scoring
Credit scoring models based on accepted applications may be biased and their
consequences can have a statistical and economic impact. Reject inference is
the process of attempting to infer the creditworthiness status of the rejected
applications. In this research, we use deep generative models to develop two
new semi-supervised Bayesian models for reject inference in credit scoring, in
which we model the data generating process to be dependent on a Gaussian
mixture. The goal is to improve the classification accuracy in credit scoring
models by adding reject applications. Our proposed models infer the unknown
creditworthiness of the rejected applications by exact enumeration of the two
possible outcomes of the loan (default or non-default). The efficient
stochastic gradient optimization technique used in deep generative models makes
our models suitable for large data sets. Finally, the experiments in this
research show that our proposed models perform better than classical and
alternative machine learning models for reject inference in credit scoring
Alternative profit scorecards for revolving credit
The aim of this PhD project is to design profit scorecards for a revolving credit
using alternative measures of profit that have not been considered in previous
research. The data set consists of customers from a lending institution that
grants credit to those that are usually financially excluded due to the lack of
previous credit records.
The study presents for the first time a relative profit measure (i.e.: returns) for
scoring purposes and compares results with those obtained from usual
monetary profit scores both in cumulative and average terms. Such relative
measure can be interpreted as the productivity per customer in generating cash
flows per monetary unit invested in receivables. Alternatively, it is the coverage
against default if the lender discontinues operations at time t.
At an exploratory level, results show that granting credit to financially excluded
customers is a profitable business. Moreover, defaulters are not necessarily
unprofitable; in average the profits generated by profitable defaulters exceed
the losses generated by certain non-defaulters. Therefore, it makes sense to
design profit (return) scorecards. It is shown through different methods that it
makes a difference to use alternative profit measures for scoring purposes. At a
customer level, using either profits or returns alters the chances of being
accepted for credit. At a portfolio level, in the long term, productivity (coverage
against default) is traded off if profits are used instead of returns. Additionally,
using cumulative or average measures implies a trade off between the scope of
the credit programme and customer productivity (coverage against default).
The study also contributes to the ongoing debate of using direct and indirect
prediction methods to produce not only profit but also return scorecards. Direct
scores were obtained from borrower attributes, whilst indirect scores were
predicted using the estimated probabilities of default and repurchase; OLS was
used in both cases. Direct models outperformed indirect models. Results show
that it is possible to identify customers that are profitable both in monetary and
relative terms. The best performing indirect model used the probabilities of
default at t=12 months and of repurchase in t=12, 30 months as predictors. This
agrees with banking practices and confirms the significance of the long term
perspective for revolving credit. Return scores would be preferred under more
conservative standpoints towards default because of unstable conditions and if
the aim is to penetrate relatively unknown segments. Further ethical
considerations justify their use in an inclusive lending context. Qualitative data
was used to contextualise results from quantitative models, where appropriate.
This is particularly important in the microlending industry, where analysts’
market knowledge is important to complement results from scorecards for credit
granting purposes.
Finally, this is the first study that formally defines time-to-profit and uses it for
scoring purposes. Such event occurs when the cumulative return exceeds one. It
is the point in time when customers are exceedingly productive or alternatively
when they are completely covered against default, regardless of future
payments. A generic time-to-profit application scorecard was obtained by
applying the discrete version of Cox model to borrowers’ attributes. Compared
with OLS results, portfolio coverage against default was improved. A set of
segmented models predicted time-to-profit for different loan durations. Results
show that loan duration has a major effect on time-to-profit. Furthermore,
inclusive lending programmes can generate internal funds to foster their
growth. This provides useful insight for investment planning objectives in
inclusive lending programmes such as the one under analysis