درخت حوزه‌های تخصصی

Businesses are increasingly interested in how big data, artificial intelligence, machine learning, and predictive analytics can be used to increase revenue, lower costs, and improve their business processes. In this paper, we describe how we have developed a data-driven machine learning method to optimize the collection process for a debt collection agency. Precisely speaking, we create a framework for the data-driven scheduling of outbound calls made by debt collectors. These phone calls are used to persuade debtors to settle their debt, or to negotiate payment arrangements in case debtors are willing, but unable to repay. We determine daily which debtors should be called to maximize the amount of delinquent debt recovered in the long term, under the constraint that only a limited number of phone calls can be made each day. Our approach is to formulate a Markov decision process and, given its intractability, approximate the value function based on historical data through the use of state-of-the-art machine learning techniques. Precisely, we predict the likelihood with which a debtor in a particular state is going to settle its debt and use this as a proxy for the value function. Based on this value function approximation, we compute for each debtor the marginal value of making a call. This leads to a particularly straightforward optimization procedure, namely, we prioritize the debtors that have the highest marginal value per phone call. We believe that our optimized policy substantially outperforms the current scheduling policy that has been used in business practice for many years. Most importantly, our policy collects more debt in less time, whilst using substantially fewer resources leading to a large increase in the amount of debt collected per phone call.

This study aimed to investigate the role of financial and non-financial determinants of the sustainability of Iranian banks. Accordingly, the sustainability score of 27 public and private Iranian banks were evaluated in 2017 by employing a sustainability model. The model was developed by the acquisition of sustainability codes, themes, and categories in the banking industry through Meta Synthesis, while its casual structure was determined by a combined method of Interpretive Structural Modeling and Analytical Network Process. Subsequently, we calculated the sustainability scores by using our proposed model to analyze the content of the banks’ disclosed information. Then, the effect of capital adequacy, total assets, financial leverage, loan to deposit ratio, return on assets and number of branches were investigated using multiple regressions on the banks' sustainability scores. Findings depict that total assets have a positive and significant relationship and capital adequacy has a negative and significant relationship with a bank's sustainability. Therefore, banks with more assets are more willing to participate in sustainability activities, due to more appropriate financial resources, as well as to support the bank's brand and its reputation to stakeholders. On the other hand, due to the wrong belief that sustainability is costly, and non-value adding for the banks, they get less involved in order to increase their capital adequacy ratio.

Iran's banking industry has faced a wide range of changes during recent years and its banks' performance has been affected by these changes. On the one hand, the entry of private banks to the industry has made various changes in the industry's concentration and the market share of banks, on the other hand, banking technologies and strengthened competitive environment have led banks to increase efficiency and improve their performance. Given the importance of competition and efficiency in different components of the economy, the present study aims to investigate the competitiveness of private banks over the period 2005 to 2017. For this purpose, according to Panzar-Rosse model in the framework of panel data models, required data are collected from all private banks, then the competitiveness of private banks is calculated using the Panzar-Rosse model. According to the results, markets of Iran’s private banks are involved in monopolistic competition during the underlying time period.