مطالب

The primary purpose of this research is to develop and present dimensions and components of information transparency in banks to evaluate and rank the level of information disclosure and transparency of Iranian banks, considering the environmental and native conditions of Iran. Regarding the reporting conditions and reporting environment of Iranian banks and the information needs that govern them, one can evaluate the banks on an applied level by explaining the level of disclosure and specific information transparency and then ranked it. For this purpose, firstly, the list of dimensions and components of disclosure and transparency is extracted from research, documents, and standards issued by professional banking authorities. Then after incorporation, adjustment, and summary, the final list of dimensions and components is reviewed by experts. Due to the lack of a proper model of information disclosure and transparency in Iranian banks, the best way to confirm the criteria, dimensions, and components of the model was to use the opinions of experts. The Delphi method and the one-sample t-test were used to verify the components and finally design the model. As a result, the final model consists of 11 dimensions of information transparency to evaluate and rank the level of disclosure and transparency of Iranian banks and assess the degree of compliance of the current status of disclosure and transparency of Iranian banks with the proposed model using (SEM) and the t-sample.

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.