Imperial College London

Dr Tony Bellotti

Faculty of Natural SciencesDepartment of Mathematics

Senior Lecturer
 
 
 
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Contact

 

a.bellotti

 
 
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Location

 

522Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
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29 results found

Ye H, Bellotti A, 2019, Modelling Recovery Rates for Non-Performing Loans, RISKS, Vol: 7, ISSN: 2227-9091

JOURNAL ARTICLE

Bakoben M, Bellotti A, Adams N, Identification of credit risk based on cluster analysis of account behaviours, Journal of the Operational Research Society, ISSN: 0160-5682

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.

JOURNAL ARTICLE

Adamskiy D, Bellotti A, Dzhamtyrova R, Kalnishkan Yet al., 2019, Aggregating Algorithm for prediction of packs, Machine Learning, ISSN: 0885-6125

JOURNAL ARTICLE

Dirick L, Bellotti T, Claeskens G, Baesens Bet al., 2019, Macro-Economic Factors in Credit Risk Calculations: Including Time-Varying Covariates in Mixture Cure Models, JOURNAL OF BUSINESS & ECONOMIC STATISTICS, Vol: 37, Pages: 40-53, ISSN: 0735-0015

JOURNAL ARTICLE

Tobback E, Bellotti T, Moeyersoms J, Stankova M, Martens Det al., 2017, Bankruptcy prediction for SMEs using relational data, Decision Support Systems, Vol: 102, Pages: 69-81, ISSN: 0167-9236

Bankruptcy prediction has been a popular and challenging research area for decades. Most prediction models are built using financial figures, stock market data and firm specific variables. We complement such traditional low-dimensional data with high-dimensional data on the company's directors and managers in the prediction models. This information is used to build a network between small and medium-sized enterprises (SMEs), where two companies are related if they share a director or high-level manager. A smoothed version of the weighted-vote relational neighbour classifier is applied on the network and transforms the relationships between companies into bankruptcy prediction scores, thereby assuming that a company is more likely to file for bankruptcy if one of the related companies in its network has already failed. An ensemble model is built that combines the relational model's output scores with structured data and is applied on two data sets of Belgian and UK SMEs. We find that the relational model gives improved predictions over a simple financial model when detecting the riskiest firms. The largest performance increase is found when the relational and financial data are combined, confirming the complementary nature of both data types.

JOURNAL ARTICLE

Bellotti A, 2017, Reliable region predictions for automated valuation models, ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE, Vol: 81, Pages: 71-84, ISSN: 1012-2443

JOURNAL ARTICLE

Bakoben M, Bellotti A, Adams N, 2016, Improving clustering performance by incorporating uncertainty, PATTERN RECOGNITION LETTERS, Vol: 77, Pages: 28-34, ISSN: 0167-8655

JOURNAL ARTICLE

Crook J, Bellotti T, Mues C, 2016, Feature Cluster: New Developments in Credit Risk Modelling, EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, Vol: 249, Pages: 395-396, ISSN: 0377-2217

JOURNAL ARTICLE

Hon PS, Bellotti T, 2016, Models and forecasts of credit card balance, EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, Vol: 249, Pages: 498-505, ISSN: 0377-2217

JOURNAL ARTICLE

Bakoben M, Adams N, Bellotti A, 2016, Uncertainty aware clustering for behaviour in enterprise networks, 16th IEEE International Conference on Data Mining (ICDM), Publisher: IEEE, Pages: 269-272, ISSN: 2375-9232

CONFERENCE PAPER

Damian MS, Howard RS, Ben-Shlomo Y, Bellotti T, Harrison D, Griggs K, Rowan Ket al., 2014, ICNARC STUDY OF MORTALITY IN NEUROLOGICAL PATIENTS ON ICU, Meeting of the Associatiion-of-British-Neurologists, Publisher: BMJ PUBLISHING GROUP, ISSN: 0022-3050

CONFERENCE PAPER

Bellotti T, Crook J, 2014, Retail credit stress testing using a discrete hazard model with macroeconomic factors, JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY, Vol: 65, Pages: 340-350, ISSN: 0160-5682

JOURNAL ARTICLE

Bellotti T, Crook J, 2013, Forecasting and stress testing credit card default using dynamic models, INTERNATIONAL JOURNAL OF FORECASTING, Vol: 29, Pages: 563-574, ISSN: 0169-2070

JOURNAL ARTICLE

Damian MS, Ben-Shlomo Y, Howard R, Bellotti T, Harrison D, Griggs K, Rowan Ket al., 2013, The effect of secular trends and specialist neurocritical care on mortality for patients with intracerebral haemorrhage, myasthenia gravis and Guillain-Barr, syndrome admitted to critical care, INTENSIVE CARE MEDICINE, Vol: 39, Pages: 1405-1412, ISSN: 0342-4642

JOURNAL ARTICLE

Bellotti T, Crook J, 2012, Loss given default models incorporating macroeconomic variables for credit cards, INTERNATIONAL JOURNAL OF FORECASTING, Vol: 28, Pages: 171-182, ISSN: 0169-2070

JOURNAL ARTICLE

Crook J, Bellotti AG, 2012, Asset correlations for credit card defaults, Applied Financial Economics, Pages: 87-95

The capital requirements formula within the Basel II Accord is based on a Merton one-factor model and in the case of credit cards an asset correlation of 4% is assumed. In this article we estimate the asset correlation for two datasets assuming the one-factor model. We find that the asset correlations assumed by Basel II are much higher than those observed in the datasets we analyse. We show the reduction in capital requirements that a typical lender would have if the values we estimated were implemented in the Basel Accord in place of the current values.

JOURNAL ARTICLE

Bellotti T, Matousek R, Stewart C, 2011, A note comparing support vector machines and ordered choice models' predictions of international banks' ratings, DECISION SUPPORT SYSTEMS, Vol: 51, Pages: 682-687, ISSN: 0167-9236

JOURNAL ARTICLE

Crook J, Bellotti T, 2010, Time varying and dynamic models for default risk in consumer loans, JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY, Vol: 173, Pages: 283-305, ISSN: 0964-1998

JOURNAL ARTICLE

Bellotti T, 2010, A simulation study of Basel II expected loss distributions for a portfolio of credit cards, Journal of Financial Services Marketing, Vol: 14, Pages: 268-277, ISSN: 1363-0539

JOURNAL ARTICLE

Bellotti T, Crook J, 2009, Credit scoring with macroeconomic variables using survival analysis, JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY, Vol: 60, Pages: 1699-1707, ISSN: 0160-5682

JOURNAL ARTICLE

Bellotti T, Crook J, 2009, Support vector machines for credit scoring and discovery of significant features, EXPERT SYSTEMS WITH APPLICATIONS, Vol: 36, Pages: 3302-3308, ISSN: 0957-4174

JOURNAL ARTICLE

Chervonenkis A, Long PM, Liu X, Mcclean S, Luo Z, Bellotti T, Bell D, Dowe DL, Shafer G, Indjic D, Hawe G, Vapnik V, Papadopoulos H, Hutchinson Aet al., 2007, Discussion on Hedging predictions in machine learning by A. Gammerman and V. Vovk, COMPUTER JOURNAL, Vol: 50, Pages: 164-172, ISSN: 0010-4620

JOURNAL ARTICLE

Strefford JC, van Delft FW, Robinson HM, Worley H, Yiannikouris O, Selzer R, Richmond T, Hann I, Bellotti T, Raghavan M, Young BD, Saha V, Harrison CJet al., 2006, Complex genomic alterations and gene expression in acute lymphoblastic leukemia with intrachromosomal amplification of chromosome 21., Proc Natl Acad Sci U S A, Vol: 103, Pages: 8167-8172, ISSN: 0027-8424

We have previously identified a unique subtype of acute lymphoblastic leukemia (ALL) associated with a poor outcome and characterized by intrachromosomal amplification of chromosome 21 including the RUNX1 gene (iAMP21). In this study, array-based comparative genomic hybridization (aCGH) (n = 10) detected a common region of amplification (CRA) between 33.192 and 39.796 Mb and a common region of deletion (CRD) between 43.7 and 47 Mb in 100% and 70% of iAMP21 patients, respectively. High-resolution genotypic analysis (n = 3) identified allelic imbalances in the CRA. Supervised gene expression analysis showed a distinct signature for eight patients with iAMP21, with 10% of overexpressed genes located within the CRA. The mean expression of these genes was significantly higher in iAMP21 when compared to other ALL samples (n = 45). Although genomic copy number correlated with overall gene expression levels within areas of loss or gain, there was considerable individual variation. A unique subset of differentially expressed genes, outside the CRA and CRD, were identified when gene expression signatures of iAMP21 were compared to ALL samples with ETV6-RUNX1 fusion (n = 21) or high hyperdiploidy with additional chromosomes 21 (n = 23). From this analysis, LGMN was shown to be overexpressed in patients with iAMP21 (P = 0.0012). Genomic and expression data has further characterized this ALL subtype, demonstrating high levels of 21q instability in these patients leading to proposals for mechanisms underlying this clinical phenotype and plausible alternative treatments.

JOURNAL ARTICLE

Bellotti T, Luo Z, Gammerman A, 2006, Reliable classification of childhood acute leukaemia from gene expression data using confidence machines., Publisher: IEEE, Pages: 148-153

CONFERENCE PAPER

Bellotti T, Luo Z, Gammerman A, 2006, Strangeness Minimisation Feature Selection with Confidence Machines., Publisher: Springer, Pages: 978-985

CONFERENCE PAPER

Bellotti T, Luo Z, Gammerman A, Van Delft FW, Saha Vet al., 2005, Qualified predictions for microarray and proteomics pattern diagnostics with confidence machines., Int J Neural Syst, Vol: 15, Pages: 247-258, ISSN: 0129-0657

We focus on the problem of prediction with confidence and describe a recently developed learning algorithm called transductive confidence machine for making qualified region predictions. Its main advantage, in comparison with other classifiers, is that it is well-calibrated, with number of prediction errors strictly controlled by a given predefined confidence level. We apply the transductive confidence machine to the problems of acute leukaemia and ovarian cancer prediction using microarray and proteomics pattern diagnostics, respectively. We demonstrate that the algorithm performs well, yielding well-calibrated and informative predictions whilst maintaining a high level of accuracy.

JOURNAL ARTICLE

van Delft FW, Bellotti T, Luo Z, Jones LK, Patel N, Yiannikouris O, Hill AS, Hubank M, Kempski H, Fletcher D, Chaplin T, Foot N, Young BD, Hann IM, Gammerman A, Saha Vet al., 2005, Prospective gene expression analysis accurately subtypes acute leukaemia in children and establishes a commonality between hyperdiploidy and t(12;21) in acute lymphoblastic leukaemia., Br J Haematol, Vol: 130, Pages: 26-35, ISSN: 0007-1048

We have prospectively analysed and correlated the gene expression profiles of children presenting with acute leukaemia to the Royal London and Great Ormond Street Hospitals with morphological diagnosis, immunophenotype and karyotype. Total RNA extracted from freshly sorted blast cells was obtained from 84 lymphoblastic [acute lymphoblastic leukaemia (ALL)], 20 myeloid [acute myeloid leukaemia (AML)] and three unclassified acute leukaemias and hybridised to the high density Affymetrix U133A oligonucleotide array. Analysis of variance and significance analysis of microarrays was used to identify discriminatory genes. A novel 50-gene set accurately identified all patients with ALL and AML and predicted for a diagnosis of AML in three patients with unclassified acute leukaemia. A unique gene set was derived for each of eight subtypes of acute leukaemia within our data set. A common profile for children with ALL with an ETV6-RUNX1 fusion, amplification or deletion of ETV6, amplification of RUNX1 or hyperdiploidy with an additional chromosome 21 was identified. This suggests that these rearrangements share a commonality in biological pathways that maintains the leukaemic state. The gene TERF2 was most highly expressed in this group of patients. Our analyses demonstrate that not only is microarray analysis the single most effective tool for the diagnosis of acute leukaemias of childhood but it has the ability to identify unique biological pathways. To further evaluate its prognostic value it needs to be incorporated into the routine diagnostic analysis for large-scale clinical trials in childhood acute leukaemias.

JOURNAL ARTICLE

Luo Z, Bellotti T, Gammerman A, 2004, Qualified Predictions for Proteomics Pattern Diagnostics with Confidence Machines., Publisher: Springer, Pages: 46-51

CONFERENCE PAPER

Gammerman A, Bellotti T, 1992, Experiments Using Minimal-Length Encoding to Solve Machine Learning Problems., Publisher: IEEE Computer Society, Pages: 359-367

CONFERENCE PAPER

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