Evaluating the predictive accuracy of microloan officers’ subjective judgment

Abstract

The peculiarity in lending methodology in Microfinance institution characterized by unchallenged dominance of the loan officers in the decision-making prompted us to investigate the predictive accuracy of their subjective judgement. In addition, we investigate if the accuracy of this information depends on the strength of lenders-borrowers relationship.The objective of this paper is to understand the loan officer behaviour in default prediction task. Using credit file data from Tunisian Microfinance bank, we have found evidence that subjective judgment of loan officers has statistically significant predictive capability of default risk. In addition, our results suggest that novice loan officers have incorrect prediction of future default events because they are prone to different behavioral biases. Novice loan officers may overweight any information that is consistent with their existing beliefs and overestimate the precision of their own private information. However, through learning experienced loan officers have less behavior bias and more accurate feeling allowing them to make an accurate default prediction. To make correct decision in relationship lending approach, credit committee can rely on subjective judgment of experienced loan officers mainly when the latter judged the creditworthiness of frequent borrowers. As soft information is not easily and accurately transferable, Microfinance institutions have more interest to retain their experienced loan officers than their borrowers.

The interaction effect of gender and ethnicity in loan approval: A Bayesian estimation with data from a laboratory field experiment

“…Identity may have also played a role in the results if nonindigenous loan officers better appreciate the ability of nonethnic entrepreneurs in terms of completing their project and/or repaying the debt (Beck, Behr, & Madestam, 2011). It is evident in any case that the granting decision is based on the subjective judgment of loan officers drawing from previous experience (Baklouti & Baccar, 2013), and nonquantifiable data (Wilson, 2015), which creates room for discrimination and explains why different credit officers reached different conclusions after analyzing the same credit profile 8 .…”

Moderating Effect of Capital Outflow Monitoring Management (COMM) on Credit Risk and Loan Performance

“…Capital outflow monitoring management (COMM). The concept of Capital outflow monitoring management (COMM) in this study uses measurement indicators adapted from the concept of credit risk management (Wachira, 2017); (Murigi & Thuo, 2018); Training and upgrading skills (Katerega, Ngoma, Masaba, Nangoli, & Waswa, 2015); (Agier, 2012); (Baklouti & Baccar, 2013); customer relationship management (Mohamad et al, 2014); (Wachira, 2017) and Indonesian National Work Competency Standards (SKKNI) number 181 in 2017 (Kementrian Tenaga Kerja, 2017), namely: (1). Focused on the debtor, (2).…”

“…The lowest score (1) shows the very low loan performance and the highest score (5) shows the high loan performance. (Wachira, 2017); (Murigi & Thuo, 2018); (Katerega et al, 2015); (Agier, 2012); (Baklouti & Baccar, 2013) (Mohamad et al, 2014 (Latan & Ghozali, 2015) The effect of Credit Risk on Loan Performance which is moderated by Capital Outflow Monitoring Management (COMM) is done by analyzing respondents' answers to the questions given. Then the results are processed using the help of Smart PLS 3.0 software…”

“…Marketing (account officer) is at the forefront as the spearhead of executing credit from financial institutions to process credit (analysis, monitoring, coaching and collection). Therefore, AO is required to have good skills and knowledge about credit and things that support the credit process (Baklouti & Baccar, 2013). As an extension of the bank, AO is expected to gather information about the financing process, both from the debtor and from other parties related to the credit process (Wachira, 2017).…”

“…Process credit applications, monitor loans, collect installments up to customer development (Nsiah, 2017); (Schwarz, 2011); (Osei-Nyarko, 2015). Therefore, AO is required to have good skills and knowledge about credit and things that support the credit process (Baklouti & Baccar, 2013). As an extension of the bank, AO is expected to gather information about the financing process, both from the debtor and from other parties related to the credit process (Wachira, 2017).…”