A novel knowledge discovery based approach for supplier risk scoring with application in the HVAC industry

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonThis research has led to a novel methodology for assessment and quantification of supply risks in the supply chain. The research has built on advanced Knowledge Discovery techniques and has resulted to a software implementation to be able to do so. The methodology developed and presented here resembles the well-known consumer credit scoring methods as it leads to a similar metric, or score, for assessing a supplier’s reliability and risk of conducting business with that supplier. However, the focus is on a wide range of operational metrics rather than just financial, which credit scoring techniques typically focus on. The core of the methodology comprises the application of Knowledge Discovery techniques to extract the likelihood of possible risks from within a range of available datasets. In combination with cross-impact analysis, those datasets are examined for establish the inter-relationships and mutual connections among several factors that are likely contribute to risks associated with particular suppliers. This approach is called conjugation analysis. The resulting parameters become the inputs into a logistic regression which leads to a risk scoring model the outcome of the process is the standardized risk score which is analogous to the well-known consumer risk scoring model, better known as FICO score. The proposed methodology has been applied to an Air Conditioning manufacturing company. Two models have been developed. The first identifies the supply risks based on the data about purchase orders and selected risk factors. With this model the likelihoods of delivery failures, quality failures and cost failures are obtained. The second model built on the first one but also used the actual data about the performance of supplier to identify risks of conducting business with particular suppliers. Its target was to provide quantitative measures of an individual supplier’s risk level. The supplier risk scoring model is tested on the data acquired from the company for its performance analysis. The supplier risk scoring model achieved 86.2% accuracy, while the area under curve (AUC) was 0.863. The AUC curve is much higher than required model’s validity threshold value of 0.5. It represents developed model’s validity and reliability for future data. The numerical studies conducted with real-life datasets have demonstrated the effectiveness of the proposed methodology and system as well as its future potential for industrial adoption

A risk scoring model to predict progression of retinopathy of prematurity for Indonesia

Introduction: Retinopathy of prematurity (ROP) is a serious eye disease in preterm infants. Generally, the progression of this disease can be detected by screening infants regularly. In case of progression, treatment can be instituted to stop the progression. In Indonesia, however, not all infants are screened because the number of pediatric ophthalmologists trained to screen for ROP and provide treatment is limited. Therefore, other methods are required to identify infants at risk of developing severe ROP.Objective: To assess a scoring model’s internal and external validity to predict ROP progression in Indonesia.Method: To develop a scoring model and determine its internal validity, we used data on 98 preterm infants with ROP who had undergone one or more serial eye examinations between 2009 and 2014. For external validation, we analyzed data on 62 infants diagnosed with ROP irrespective of the stage between 2017 and 2020. Patients stemmed from one neonatal unit and three eye clinics in Jakarta, Indonesia.Results: We identified the duration of oxygen supplementation, gestational age, socio-economic status, place of birth, and oxygen saturation monitor setting as risk factors for developing ROP. We developed two models—one based on the duration of supplemental oxygen and one on the setting of the oxygen saturation monitor. The ROP risk and probabilistic models obtained the same sensitivity and specificity for progression to Type 1 ROP. The agreement, determined with the Kappa statistic, between the ROP risk model’s suitability and the probabilistic model was excellent. The external validity of the ROP risk model showed 100% sensitivity, 73% specificity, 76% positive predictive value, 100% negative predictive value, positive LR +3.7, negative LR 0, 47% pre-test probability, and 77% post-test probability.Conclusion: The ROP risk scoring model can help to predict which infants with first-stage ROP might show progression to severe ROP and may identify infants who require referral to a pediatric ophthalmologist for treatment.</p

Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation

Black-box risk scoring models permeate our lives, yet are typically proprietary or opaque. We propose Distill-and-Compare, a model distillation and comparison approach to audit such models. To gain insight into black-box models, we treat them as teachers, training transparent student models to mimic the risk scores assigned by black-box models. We compare the student model trained with distillation to a second un-distilled transparent model trained on ground-truth outcomes, and use differences between the two models to gain insight into the black-box model. Our approach can be applied in a realistic setting, without probing the black-box model API. We demonstrate the approach on four public data sets: COMPAS, Stop-and-Frisk, Chicago Police, and Lending Club. We also propose a statistical test to determine if a data set is missing key features used to train the black-box model. Our test finds that the ProPublica data is likely missing key feature(s) used in COMPAS.Comment: Camera-ready version for AAAI/ACM AIES 2018. Data and pseudocode at https://github.com/shftan/auditblackbox. Previously titled "Detecting Bias in Black-Box Models Using Transparent Model Distillation". A short version was presented at NIPS 2017 Symposium on Interpretable Machine Learnin

Machine Learning-Based Analysis in the Management of Iatrogenic Bile Duct Injury During Cholecystectomy: a Nationwide Multicenter Study

Background Iatrogenic bile duct injury (IBDI) is a challenging surgical complication. IBDI management can be guided by artificial intelligence models. Our study identified the factors associated with successful initial repair of IBDI and predicted the success of definitive repair based on patient risk levels. Methods This is a retrospective multi-institution cohort of patients with IBDI after cholecystectomy conducted between 1990 and 2020. We implemented a decision tree analysis to determine the factors that contribute to successful initial repair and developed a risk-scoring model based on the Comprehensive Complication Index. Results We analyzed 748 patients across 22 hospitals. Our decision tree model was 82.8% accurate in predicting the success of the initial repair. Non-type E (p < 0.01), treatment in specialized centers (p < 0.01), and surgical repair (p < 0.001) were associated with better prognosis. The risk-scoring model was 82.3% (79.0-85.3%, 95% confidence interval [CI]) and 71.7% (63.8-78.7%, 95% CI) accurate in predicting success in the development and validation cohorts, respectively. Surgical repair, successful initial repair, and repair between 2 and 6 weeks were associated with better outcomes. Discussion Machine learning algorithms for IBDI are a novel tool may help to improve the decision-making process and guide management of these patients

Development and validation study of a non-alcoholic fatty liver disease risk scoring model among adults in China

Background: Non-alcoholic fatty liver disease (NAFLD) is one of the most common liver diseases in China. It is usually asymptomatic and transabdominal ultrasound (USS) is the usual means for diagnosis, but it may not be feasible to have USS screening of the whole population. Objective: To develop a risk scoring model for predicting the presence of NAFLD using parameters that can be easily obtain in clinical settings. Methods: A retrospective study on the data of 672 adults who had general health check including a transabdominal ultrasound. Fractional polynomial and multivariable logistic regressions of sociodemographic and biochemical variables on NAFLD were used to identify the predictors. A risk score was assigned to each predictor using the scaled standardized β-coefficient to create a risk prediction algorithm. The accuracy for NAFLD detection by each cut-off score in the risk algorithm was evaluated. Results: The prevalence of NAFLD in our study population was 33.0% (222/672). Six significant factors were selected in the final prediction model. The areas under the curve (AUC) was 0.82 (95% CI: 0.78–0.85). The optimal cut-off score, based on the ROC was 35, with a sensitivity of 76.58% (95% CI: 70.44–81.98%) and specificity of 74.89% (95% CI: 70.62–78.83%). Conclusion: A NAFLD risk scoring model can be used to identify asymptomatic Chinese people who are at risk of NAFLD for further USS investigation.published_or_final_versio

“Story of a Bank” Basel II accreditation through university-industry collaboration-case study

This paper deals with a case study of credit risk scoring models at Industrial Bank. The aim of this research is to investigate how a Malaysian financial institution developed and integrated credit risk scoring models with current organisational needs and evaluation of best practices for university-industry collaboration on this initiative. Attempts were made to categorise the credit risk scoring models initiative according to a variety of statistical techniques from modeling. This is an exploratory study which uses qualitative research methodology. Analysis of document from company annual reports as well as articles from journal, Bank Negara Malaysia, (BNM) regulatory reports as well as working papers and semistructured interviews were conducted to identify the organisational needs as a result of context and task. A company-wide development system for credit risk scoring model was effectively integrated to provide a direct support to competence management endeavor. The company’s credit risk scoring models initiatives have also resulted in managerial implications such as increased effectiveness of risk management through measuring the riskiness of each customer and automated the whole process, thereby leading to significant efficiency improvements. Thus, scoring models help banks to control credit risks. Going forward, credit risk scoring model is to become the best practice approach of the receivables management process and is essential to effective credit risk management

Risk-based supervision of pension funds : a review of international experience and preliminary assessment of the first outcomes

This paper provides a review of the design and experience of risk-based pension fund supervision in several countries that have been leaders in the development of these methods. The utilization of risk-based methods originates primarily in the supervision of banks. In recent years it has increasingly been extended to other types of financial intermediaries including pension funds and insurers. The trend toward risk-based supervision of pensions is closely associated with movement toward the integration of pension supervision with that of banking and other financial services into a single national authority. Although similar in concept to the techniques developed in banking, the application to pension funds has required modifications, particularly for defined contribution funds that transfer investment risk to fund members. The countries examined provide a range of experiences that illustrate both the diversity of pension systems and approaches to risk-based supervision, but also a commonality of the focus on sound risk management and effective supervisory outcomes. The paper provides a description of pension supervision in Australia, Denmark, Mexico and the Netherlands, and an initial evaluation of the results achieved in relation to the underlying objectives.Debt Markets,,Insurance&Risk Mitigation,Emerging Markets,Banks&Banking Reform

Construction of a prognostic assessment model for colon cancer patients based on immune-related genes and exploration of related immune characteristics

Objectives: To establish a novel risk score model that could predict the survival and immune response of patients with colon cancer.Methods: We used The Cancer Genome Atlas (TCGA) database to get mRNA expression profile data, corresponding clinical information and somatic mutation data of patients with colon cancer. Limma R software package and univariate Cox regression were performed to screen out immune-related prognostic genes. GO (Gene ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) were used for gene function enrichment analysis. The risk scoring model was established by Lasso regression and multivariate Cox regression. CIBERSORT was conducted to estimate 22 types of tumor-infiltrating immune cells and immune cell functions in tumors. Correlation analysis was used to demonstrate the relationship between the risk score and immune escape potential.Results: 679 immune-related genes were selected from 7846 differentially expressed genes (DEGs). GO and KEGG analysis found that immune-related DEGs were mainly enriched in immune response, complement activation, cytokine-cytokine receptor interaction and so on. Finally, we established a 3 immune-related genes risk scoring model, which was the accurate independent predictor of overall survival (OS) in colon cancer. Correlation analysis indicated that there were significant differences in T cell exclusion potential in low-risk and high-risk groups.Conclusion: The immune-related gene risk scoring model could contribute to predicting the clinical outcome of patients with colon cancer