A multiple criteria credit rating approach utilizing social media data

An integrated fuzzy credit rating model using fuzzy-BWM and new fuzzy-TOPSIS-Sort-C

Financial institutions use credit rating models to make lending, investing, and risk management decisions. Credit rating models have been developed using a variety of statistical and machine learning methods. These methods, however, are data-intensive and dependent on assumptions about data distribution. This research offers an integrated fuzzy credit rating model to address such issues. This study proposes an integrated fuzzy credit rating model to reduce such problems. The study applies the fuzzy best–worst method (fuzzy-BWM) to obtain the weight of criteria that affect creditworthiness and fuzzy technique for order of preference by similarity to ideal solution (fuzzy-TOPSIS)-Sort-C to evaluate the borrowers. The BWM was found consistent amongst existing multi-criteria decision-making (MCDM) methods, and consistency further improves when BWM is extended to a fuzzy version. The study applies TOPSIS-Sorting along with fuzzy theory to overcome human uncertainty while making a decision. TOPSIS-sorting has been found capable of handling rank reversal problems that persist in the TOPSIS method. The fuzzy-TOPSIS-Sort-C method is applied to evaluate borrowers based on the characteristic profile of the identified criteria. The proposed model's efficacy has been illustrated with a case study to rate fifty firms with real-life data. The proposed model results are compared with previous studies and commercially available ratings. The model results show better accuracy in terms of accuracy and true-positive rates to predict default. It can help financial institutions to find potential borrowers for granting credit.

A Weighted Bonferroni-OWA Operator Based Cumulative Belief Degree Approach to Personnel Selection Based on Automated Video Interview Assessment Data

Asynchronous Video Interviewing (AVI) is considered one of the most recent and promising innovations in the recruitment process. Using AVI in combination with AI-based technologies enables recruiters/employers to automate many of the tasks that are typically required for screening, assessing, and selecting candidates. In fact, the automated assessment and selection process is a complex and uncertain problem involving highly subjective, multiple interrelated criteria. In order to address these issues, an effective and practical approach is proposed that is able to transform, weight, combine, and rank automated AVI assessments obtained through AI technologies and machine learning. The suggested approach combines Cumulative Belief Structures with the Weighted Bonferroni-OWA operator, which allows (i) aggregating assessment scores obtained in different forms and scales; (ii) incorporating interrelationships between criteria into the analysis (iii) considering accuracies of the learning algorithms as weights of criteria; and (iv) weighting criteria objectively. The proposed approach ensures a completely data-driven and efficient approach to the personnel selection process. To justify the effectiveness and applicability of the suggested approach, an example case is presented in which the new approach is compared to classical MCDM techniques.

The impact of social media on the performance of microfinance institutions in developing countries: a quantitative approach

Purpose

Over the last few decades, microfinance industry is argued to have played a constructive role in alleviating poverty level and providing the underprivileged with access to financial services. Statistics from the World Bank reveal that, currently, only 4% of the underprivileged have been served out of the 3 billion+ potential clients. Such results are due to several claims, particularly the operational and financial challenges faced by microfinance institutions (MFIs) in the constant flux inviting more attentions towards its performance. While explicit attention is given by many researchers towards mobile banking and information and communication technology (ICT) in improving the MFIs’ performance, the study on how social media, as a rapidly growing online phenomenon, can impact on the MFIs’ performance remains scarce. As such, this study aims to investigate this impact based on four dimensional performance indicators: efficiency, financial sustainability, portfolio quality and outreach.

Design/methodology/approach

A model is proposed and tested to ascertain the relationship between social media applications and organisational performance. In so doing, web-based questionnaires have been used to collect data from MFI employees in developing countries. Results reveal a significant influence of the social media over the MFIs’ performance, offering valuable insights into both researchers and practitioners in the domain of microfinance, as well as social media—conforming that the adoption of social media as marketing, advertising and communication tools may significantly improve the MFIs’ performance.

Findings

The results demonstrate that there is a positive and significant impact of social media use within microfinance on the key indicators of MFIs. They also show that the highest impact of social media usage within the microfinance is on the portfolio quality. In addition, it was found that marketing and advertising; communication and sales and distribution are the main areas where social media is able to support while social networking websites are the most popular platforms employed in MFIs.

Originality/value

This study adds to the existing literature few theoretical and practical aspects. First, this study developed a model for assessing the value of social media as a new phenomenon within this type of organisation. Second, it offers microfinance sponsors, managers and policy makers with a frame of reference to understand what social media platform can be deployed for each purpose. Third, with the identification of the main MFIs’ performance indicators, this research provided a reference of performance measurement guide for microfinance industry when assessing different technological employment.

A Credit Rating Model in a Fuzzy Inference System Environment

One of the most important functions of an export credit agency (ECA) is to act as an intermediary between national governments and exporters. These organizations provide financing to reduce the political and commercial risks in international trade. The agents assess the buyers based on financial and non-financial indicators to determine whether it is advisable to grant them credit. Because many of these indicators are qualitative and inherently linguistically ambiguous, the agents must make decisions in uncertain environments. Therefore, to make the most accurate decision possible, they often utilize fuzzy inference systems. The purpose of this research was to design a credit rating model in an uncertain environment using the fuzzy inference system (FIS). In this research, we used suitable variables of agency ratings from previous studies and then screened them via the Delphi method. Finally, we created a credit rating model using these variables and FIS including related IF-THEN rules which can be applied in a practical setting.