Building credit scoring models using genetic programming

By Chorng-Shyong Ong, Jih-Jeng Huang, Gwo-Hshiung Tzeng. Expert systems with applications, 2005

356 citations recorded by the source corpus.

Subfields: Accounting, Artificial Intelligence, Finance

View Paper: https://www.semanticscholar.org/paper/479ab04f1586ea337ff8fba9e6102260d7726ea7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Accounting

1. Lyn C. Thomas
2. Teh Ying Wah
3. Adel Lahsasna
4. Raja Noor Ainon
5. Hussein A. Abdou
6. John Pointon
7. Quyen Ho
8. Thanh Duong

In Artificial Intelligence

1. Hussein A. Abdou
2. John Pointon
3. Quyen Ho
4. Jonathan N. Crook
5. Teh Ying Wah
6. Thanh Duong
7. K. Tran
8. Anderson Ara

In Finance

1. Lyn C. Thomas
2. Hussein A. Abdou
3. John Pointon
4. Jonathan N. Crook
5. Quyen Ho
6. Teh Ying Wah
7. Thanh Duong
8. K. Tran

Citations in the corpus

• A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems (2010, background): “Work done by [53] show that genetic programming performs better than ANN in credit scoring.”
• Credit Scoring, Statistical Techniques and Evaluation Criteria: A Review of the Literature (2011, methodology): “Meanwhile, comparisons between traditional and advanced statistical techniques have been investigated too (Abdou & Pointon, 2009; Abdou et al. 2009; Lee & Chen 2005; Ong et al, 2005; Zekic-Susac et al, 2004; Malhotra & Malhotra, 2003; Lee et al, 2002; Fritz & Hosemann, 2000).”
• Credit Scoring Models Using Soft Computing Methods: A Survey (2010, methodology): “Some studies used neural networks as a benchmark to compare with the new proposed algorithms like evolutionary computation [24, 14] and support vector machine [20, 30, 8, 35, 23 and 13].”
• Recent developments in consumer credit risk assessment (2007, background): “For example when GAs are applied to a decision tree mutation involves randomly replacing the branches from a node in a tree and crossover involves swapping branches from a node in one tree with those in another tree (Ong et al., 2005).”
• Forecasting energy consumption using a grey model improved by incorporating genetic programming (2011, methodology): “Second, the accuracy and reliability of GP exceed those of ANN in constructing a forecasting model or classification [2,3].”
• Classification methods applied to credit scoring: A systematic review and overall comparison (2016, background): “Other important comparisons may be seen in Adams et al. (2001); Hoffmann et al. (2002); Ong et al. (2005); Baesens et al. (2005); Wang et al. (2005); Lee et al. (2006); Huang et al. (2006b); Xiao et al. (2006); Van Gestel et al. (2007); Martens et al. (2007); Hu and Ansell (2007); Tsai (2008);…”
• Credit scoring model: A combination of genetic programming and deep learning (2016, methodology): “THE DISCRETIZATION TABLE FOR GERMAN DATASET [8].”
• Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research (2015, neutral): “, 2005) 1 1,000 20 4 X X (Ong et al., 2005) 2 845 17 6 X X (West et al.”