25,674 research outputs found
Consumer finance: challenges for operational research
Consumer finance has become one of the most important areas of banking, both because of the amount of money being lent and the impact of such credit on global economy and the realisation that the credit crunch of 2008 was partly due to incorrect modelling of the risks in such lending. This paper reviews the development of credit scoring—the way of assessing risk in consumer finance—and what is meant by a credit score. It then outlines 10 challenges for Operational Research to support modelling in consumer finance. Some of these involve developing more robust risk assessment systems, whereas others are to expand the use of such modelling to deal with the current objectives of lenders and the new decisions they have to make in consumer finance. <br/
Operations research in consumer finance: challenges for operational research
Consumer finance has become one of the most important areas of banking both because of the amount of money being lent and the impact of such credit on the global economy and the realisation that the credit crunch of 2008 was partly due to incorrect modelling of the risks in such lending. This paper reviews the development of credit scoring,-the way of assessing risk in consumer finance- and what is meant by a credit score. It then outlines ten challenges for Operational Research to support modelling in consumer finance. Some of these are to developing more robust risk assessment systems while others are to expand the use of such modelling to deal with the current objectives of lenders and the new decisions they have to make in consumer financ
On orthogonal projections for dimension reduction and applications in augmented target loss functions for learning problems
The use of orthogonal projections on high-dimensional input and target data
in learning frameworks is studied. First, we investigate the relations between
two standard objectives in dimension reduction, preservation of variance and of
pairwise relative distances. Investigations of their asymptotic correlation as
well as numerical experiments show that a projection does usually not satisfy
both objectives at once. In a standard classification problem we determine
projections on the input data that balance the objectives and compare
subsequent results. Next, we extend our application of orthogonal projections
to deep learning tasks and introduce a general framework of augmented target
loss functions. These loss functions integrate additional information via
transformations and projections of the target data. In two supervised learning
problems, clinical image segmentation and music information classification, the
application of our proposed augmented target loss functions increase the
accuracy
Advanced analytical methods for fraud detection: a systematic literature review
The developments of the digital era demand new ways of producing goods and rendering
services. This fast-paced evolution in the companies implies a new approach from the
auditors, who must keep up with the constant transformation. With the dynamic
dimensions of data, it is important to seize the opportunity to add value to the companies.
The need to apply more robust methods to detect fraud is evident.
In this thesis the use of advanced analytical methods for fraud detection will be
investigated, through the analysis of the existent literature on this topic.
Both a systematic review of the literature and a bibliometric approach will be applied to
the most appropriate database to measure the scientific production and current trends.
This study intends to contribute to the academic research that have been conducted, in
order to centralize the existing information on this topic
Special Libraries, October 1963
Volume 54, Issue 8https://scholarworks.sjsu.edu/sla_sl_1963/1007/thumbnail.jp
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