Determining customer limits by data mining methods in credit allocation process

The demand for credit is increasing constantly. Banks are looking for various methods of credit evaluation that provide the most accurate results in a shorter period in order to minimize their rising risks. This study focuses on various methods that enable the banks to increase their asset quality without market loss regarding the credit allocation process. These methods enable the automatic evaluation of loan applications in line with the sector practices, and enable determination of credit policies/strategies based on actual needs. Within the scope of this study, the relationship between the predetermined attributes and the credit limit outputs are analyzed by using a sample data set of consumer loans. Random forest (RF), sequential minimal optimization (SMO), PART, decision table (DT), J48, multilayer perceptron(MP), JRip, naïve Bayes (NB), one rule (OneR) and zero rule (ZeroR) algorithms were used in this process. As a result of this analysis, SMO, PART and random forest algorithms are the top three approaches for determining customer credit limits.

Penyaluran Kredit Bank Umum di Indonesia Ditinjau dari Risiko Kredit dengan Profitabilitas Sebagai Mediasi

This research was aimed at finding to analyze the effect of credit risk on profitability, to analyze the effect of credit risk on credit allocation, to analyze the effect of profitability on credit allocation at commercial banking in Indonesia. The method of analyze was path analysis by smart PLS. Our empirical results showed that credit risk had a negative and significant effect to profitability, credit risk did not have significant effect to credit allocation, profitability had a positive significant effect to credit allocation and also credit risk had a negative effect on credit allocation through profitability at commercial banking in Indonesia

Kelly Criterion for Optimal Credit Allocation

The purpose of this study is to address the critical issue of optimal credit allocation. Predicting a borrower’s probability of default is a key requirement of any credit allocation system but turning it into labeled classes leads to problems in performance measurement. In this paper the connection between the probability of default and optimal credit allocation is established through a conceptual construct called the Kelly criterion. Conflicting performance measures in dichotomous classification are replaced with coherent criteria for judging the performance of credit allocation decisions. Extensive testing on peer-to-peer lending data shows that the Kelly strategy enables consistent outperformance and efficiency in processing information relative to alternative credit allocation approaches.

A network approach to expertise retrieval based on path similarity and credit allocation

With the increasing availability of online scholarly databases, publication records can be easily extracted and analysed. Researchers can promptly keep abreast of others’ scientific production and, in principle, can select new collaborators and build new research teams. A critical factor one should consider when contemplating new potential collaborations is the possibility of unambiguously defining the expertise of other researchers. While some organisations have established database systems to enable their members to manually produce a profile, maintaining such systems is time-consuming and costly. Therefore, there has been a growing interest in retrieving expertise through automated approaches. Indeed, the identification of researchers’ expertise is of great value in many applications, such as identifying qualified experts to supervise new researchers, assigning manuscripts to reviewers, and forming a qualified team. Here, we propose a network-based approach to the construction of authors’ expertise profiles. Using the MEDLINE corpus as an example, we show that our method can be applied to a number of widely used data sets and outperforms other methods traditionally used for expertise identification.

BANK OWNERSHIP, MODE OF ENTRY, AND CREDIT ALLOCATION TO MSMEs

Micro, small, and medium enterprises (MSMEs) have strategic roles in economic structure, especially in developing countries, so, they need supports from the banking industry, including foreign banks, by giving loans they need. This study aims to find the effect of bank ownership and mode of entry on credit allocation to MSMEs. We use 110 samples of conventional commercial banks and 41 samples of foreign-owned banks in Indonesia during 2010-2017, with 686 and 266 observations. The results of multiple regression show that banks with government ownership have higher credit allocation to MSMEs than non-government ownership and banks with foreign ownership have lower credit allocation to MSMEs than domestic ownership. Based on their mode of entry, banks with foreign ownership via greenfield have lower credit allocation to MSMEs than via takeover.