Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring

By N. Siddiqi. 2005

435 citations recorded by the source corpus.

Citations in the corpus

• Learning Fair Rule Lists (2019, neutral): “tion integrating both fairness, accuracy and interpretability. 1 INTRODUCTION Machine learning models are now becoming more and more common in high stake decision-making systems (e.g., credit scoring [56], predictivejustice [37] andautomaticrecruiting [47]). Thesesystems can have an important impact”
• Actionable Recourse in Linear Classification (2018, neutral): “machine learning. In this work, we argue that it should be. A model should provide an individual with recourse when it is used to allocate a good that should be universally accessible, such as credit [29], employment [2] and public services [8, 28]. However, recourse should also be considered more b”
• The Financial Stress Index: Identification of Systemic Risk Conditions (2011, unknown): “Siddiqi [62] provides a heuristic guide that an CCVV less than 0.”
• A Bayesian Approach to Learning Scoring Systems (2015, unknown): “, 2001), bioinformatics (Freitas, Wieser, and Apweiler, 2010), credit scoring (Siddiqi, 2005; Martens et al., 2007; Martens, Baesens, and Van Gestel, 2009), churn analysis (Lima, Mues, and Baesens, 2009; Verbeke et al.”
• The Principles and Limits of Algorithm-in-the-Loop Decision Making (2019, unknown): “We explored these decisions in two settings where risk assessments are increasingly being deployed in practice—pretrial release hearings and nancial loan applications [15, 50, 64]—and under several conditions for presenting the risk assessment or structuring the human-algorithm interaction.”
• Inferring the outcomes of rejected loans: an application of semisupervised clustering (2019, unknown): “In this paper, we follow Siddiqi (2006, 2017) and propose a new RI method using clustering.”
• Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems (2019, unknown): “Machine learning (ML) algorithms have become widely deployed in domains that directly impact humans, including the criminal justice system (Angwin et al., 2016), clinical healthcare (Callahan and Shah, 2017), credit approval (Siddiqi, 2012), etc.”
• Adaptive Machine Learning for Credit Card Fraud Detection (2015, unknown): “For example, Siddiqi [186] proposes to use Weight Of Evidence (WOE) as similarity measure between two profiles in credit risk.”
• Credit Scoring, Statistical Techniques and Evaluation Criteria: A Review of the Literature (2011, unknown): “While a few numbers of studies have investigated the use of the weight of evidence measure in the field, results were comparable with those from other techniques (Abdou, 2009b; Banasik et al, 2003; Bailey, 2001; Siddiqi, 2006).”