Imperial College London
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ProfessorNiallAdams

Faculty of Natural Sciences, Department of Mathematics

Professor of Statistics
 
 
 
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Contact

 

+44 (0)20 7594 8837n.adams Website

 
 
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Location

 

6M55Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Bakoben:2020:10.1080/01605682.2019.1582586,
author = {Bakoben, M and Bellotti, A and Adams, N},
doi = {10.1080/01605682.2019.1582586},
journal = {Journal of the Operational Research Society},
pages = {775--783},
title = {Identification of credit risk based on cluster analysis of account behaviours},
url = {http://dx.doi.org/10.1080/01605682.2019.1582586},
volume = {71},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Assessment  of  risk  levels  for  existing  credit  accounts  isimportant to the implementation of bank policies and offeringfinancial  products.This  paper  uses  cluster  analysis  of  be-haviour of credit card accounts to help assess credit risk level.Account  behaviour  is  modelled  parametrically  and  we  thenimplement  the  behavioural  cluster  analysis  using  a  recentlyproposed dissimilarity measure of statistical model parameters.The advantage of this new measure is the explicit exploitationof   uncertainty   associated   with   parameters   estimated   fromstatistical  models.Interesting  clusters  of  real  credit  cardbehaviours data are obtained, in addition to superior predictionand  forecasting  of  account  default  based  on  the  clusteringoutcomes.
AU  - Bakoben,M
AU  - Bellotti,A
AU  - Adams,N
DO  - 10.1080/01605682.2019.1582586
EP  - 783
PY  - 2020///
SN  - 0160-5682
SP  - 775
TI  - Identification of credit risk based on cluster analysis of account behaviours
T2  - Journal of the Operational Research Society
UR  - http://dx.doi.org/10.1080/01605682.2019.1582586
UR  - http://hdl.handle.net/10044/1/67308
VL  - 71
ER  -
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