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It is a fact that blocking banks’ resources has its major negative impact on the institutions in specific and the economic field in general. For example, it minimizes the bank’s capabilities, affects negatively on the bank’s productivity, as well as reduces the economic progress (Khedr & Borgman, 2006). Therefore, in addition to the vital objective that ensuring the efficient resources’ return for the financial institutions, to avoid any lacking for these resources (Khedr & Kok, 2006), the successful assessment of the organization progress is also strongly recommended (Rehman & Hashim, 2020). Both objectives could be viewed as a collaborative environment. In (Rehman & Hashim, 2020), a relation between assessing the risk level of internal fraudulent with its impact on the organization progress is confirmed which ensures the efficient utilization of the organization resources while the same goal is also confirmed in (Khedr & Kok, 2006) with the perspective of ensuring the efficient utilization of these resources.
On the other hand, data mining techniques have been positively contributed to different fields in general (Idrees, ElSeddawy, & Zeidan, 2019) (Al Mazroi, Khedr, & Idrees, 2021) (Hassan, Dahab, Bahnasy, Idrees, & Gamal, 2014) and in the financial aspects of the banks in specific which has a positive contribution in both the banks as the main organization as well as the positive economic impact (Khedr & Borgman, 2006). Therefore, the requirement for continuous enhancements in applying these techniques with different perspectives has been highly recommended specially with the success of the collaborative environment approach (Hassan, Dahab, Bahnassy, Idrees, & Gamal, 2015). Although customers’ loans are only one of the profitable services in the banking fields that generate an income to the financial institution, however, it is considered one of the highest services’ risk. One of these risks is the requirement of correctly identifying the customers’ segment for the loans with correctly setting the loan amount. Exposure of these risks leads to lack of control and mismanagement, inability to identify the risk intensity and diversity all along with the ability to reach more dangerous situation such as bankruptcy. Therefore, detecting the targeted customers for loans, identifying the terms, as well as ensuring the payment are three yet interrelated targets for loans granting risk’s minimization and consequently, increase the institution profitability while directly leading to economic enhancement. The risks of credits could be modeled in the inability of the customers to pay the loan’s installments, therefore, accurate detection to the loans’ customers is a critical target to consider. Therefore, validating the suitable customers who demand such a service with this level of high risk could be monitored as one of the highest sector objectives (Khedr, Abdel-Fattah, & Nagm-Aldeen, 2015).
Does adopting intelligent techniques can affect the uncertain environment’ business process? What are the most suitable techniques for classifying loans’ granting customers? These questions address the research motivation that targets to explore the effective techniques’ environment for enhancing the business services’ decisions in an uncertain environment such as the banking field. The main success factor of the intelligent techniques offered by different fields such as data mining is the ability to integrate the business information with the proposed intelligent mechanism and method targeting to discover non-intuitive insights hidden information in the institution data (Khedr, Idrees, & El Seddawy, 2016) (El Seddawy, Sultan, & Khedr, 2013) (El Seddawy, Sultan, & Khedr, 2013). This discovered information is considered the spark for refining the corporation process, re-design the corporation policies, and re-organize the corporation relations with its customers as well as the employees (Idrees & Ibrahim, 2018).