An application of data mining classification and bi-level programming for optimal credit allocation

Abstract

This paper investigates credit allocation policy making and its effect on economic development using bi-level programming. There are two challenging problems in bi-level credit allocation; at the first level government/public related institutes must allocate the credit strategically concerning sustainable development to regions and industrial sectors. At the second level, there are agent banks, which should allocate the credit tactically to individual applicants based on their own profitability and risk using their credit scoring models. There is a conflict of interest between these two stakeholders but the cooperation is inevitable. In this paper, a new bi-level programming formulation of the leader-follower game in association with sustainable development theory in the first level and data mining classifier at the second level is used to mathematically model the problem. The model is applied to a national development fund (NDF) as a government related organization and one of its agent banks. A new algorithm called Bi-level Genetic fuzzy apriori Algorithm (BGFAA) is introduced to solve the bilateral model. Experimental results are presented and compared with a unilateral policy making scenario by the leader. Findings show that although the objective functions of the leader are worse in the bilateral scenario but agent banks collaboration is attracted and guaranteed.

On the solution of convex bilevel optimization problems

“…problems in bioengineering (Burgard et al [5]), greenhouse gas emissions (Hibino et al [6]) or traffic systems (González et al [7]). Recent applications are in optimal credit allocation (Sadatrasou et al [8]) or interdiction problems investigated by Aksen et al [9]. Problem (1.1), (1.2) is a nonconvex optimization problem of which the feasible set is not given explicitly.…”

Classification methods applied to credit scoring: Systematic review and overall comparison

“…a a a bcde Hu and Ansell (2009) Thomas (2010) c Zhou et al (2010) Gzyl et al (2015) c l b jb Bahnsen et al (2015) c Koutanaei et al (2015) a Lessmann et al (2015) e b Aryuni and Madyatmadja (2015) Deng et al (2015) a l a c b b 1 b a b a ba Tomczak and Zie¸ba (2015) a Sadatrasou et al (2015) a Bravo and Maldonado (2015) c e b e Liu et al (2015) a l b b a 1 a b Wu et al (2014) Verbraken et al (2014) f b b ea Ravi and Krishna (2014)…”

Feasibility study for banking loan using association rule mining classifier

“…Applied research related to data mining methods for bank credit has been investigated by several researchers. Data mining classification and bi-level programming were used for optimal credit allocation, and findings show that although the objective functions of the leader are worse in the bilateral scenario but agent banks collaboration is attracted and guaranteed [1]. Data mining for credit worthiness using decision tree.…”

“…Each transaction ID is calculated for determine the transaction weight, the example for transaction ID number1 or ‫ݐ(ݓݐ‬ ଵ ) = .ା.ଵା.ଷା.ଽ ସ = 0.475. The results of transaction weight that calculated is stored in table 3.…”