Ayman Mansour Khalaf Alkhazaleh

Spurred by the need to evade possible parameter bias associated with earlier works, this study intended to address the subject of whether performance of commercial banking contributes to economic growth. With the aim of answering this question, the present review concentrates on analyzing the association between profitability, deposit and credit facilities as proxy for performance of commercial banks while gross domestic product proxies economic growth. The population of the study is characterized by the Jordanian banking industry; the study enclosed a period of six years from 2010 to 2015 constructed on the annual report of thirteen chosen banks. Using Ordinary Least Square, the regression outcomes found a significant positive association between measures of bank performance and economic growth. Findings demonstrate that measures of bank performance in particular profitability deposits credits have positive relationship with economic growth as measured by GDP. The empirical results suggest that the policy creators should make arrangements to augment and prompt the banking sector in Jordan on account of its key significance in making and advancing development of the economy. It additionally can be inferred that not only commercial banking performance but also other movables such as political stability and technology may assume essential part in the economic prosperity in Jordan.

In an attempt to shed more light on the behavior of lending in banks, especially in the environment of developing countries, this study aims at explaining the impact of some factors proposed as determinants of bank lending in Jordanian commercial banks by benefiting from the financial reports of thirteen banks during the period 2010-2016. The study, in order to achieve the objectives and to test the main hypotheses has adopted Ordinary least square model (OLS). The most important results of the study are a statistically significant adverse effect of both credit risk and liquidity on bank lending, while there is a significant positive effect of the return on assets, size of the bank measured by assets, inflation, money supply and growth in gross domestic product in determining the level of lending. In addition, the study does not show a significant statistical effect between investments, the volume of deposits and bank lending in the same time frame. The review points out that because of the negative impact of liquidity and credit risk factors, commercial banks need to focus more on reducing their impact because presence of this impact at the end will decrease the ability of these banks to provide loans and stay in the banking market.

Type of the article: Research Article

Abstract
The blockchain financial system allows users to send money fast without any border restrictions. However, the same structure of the blockchain may be used as a means of laundering money. This paper assesses the relationship between the complexity of transaction networks and the likelihood of their illicit nature within the public Elliptic Bitcoin benchmark and examines whether anomaly detection using machine learning helps to interpret risks from an AML/CFT perspective. This empirical analysis assumes that Elliptic provides an anonymized transaction network in which nodes correspond to Bitcoin transactions, edges reflect directed transactions, and anonymized features facilitate licit/illicit classification of transactions. Furthermore, the dataset is not considered evidence of sender wallet addresses, receiver wallet addresses, transaction amount, timestamp, ownership of exchanges, user geography, and national AML/CFT effectiveness. Based on the labelled analytical dataset presented in the uploaded workbook (46,564 observations, including 42,019 licit transactions and 4,545 illicit transactions), a logit model found a significant positive correlation between illicit transactions and degree centrality (beta = 1.870, p < 0.001), clustering coefficient (beta = 0.940, p < 0.001), and flow entropy (beta = 0.680, p < 0.001). Isolation Forest and Autoencoder reached AUCs of 0.866 and 0.841, respectively. In turn, the coefficient measuring a country’s regulatory capacity and its interaction term are not included in the estimation because there is no country-window marginal effect. Therefore, this paper does not test for the impact of regulatory capacity of the USA, Singapore, and UAE on transaction classification.

Type of the article: Research Article

Abstract
Banks rely on ownership, transaction, and institutional information to support anti-money-laundering decision-making, yet these sources differ in decision proximity, uncertainty, and evidentiary strength. This multi-study article evaluates three external anti-money-laundering signals within a decision-governance framework emphasizing provenance, auditability, and bounded interpretation. Study 1 examines United Kingdom Persons with Significant Control records as ownership-verification workload signals. Of 6.2 million active companies, 745,200 (12.0%) met at least one verification-priority rule. Among flagged companies, 58.3% were assigned to multi-layered control structures, 25.0% to non-United Kingdom or foreign-linked controllers, 11.2% to high officer/controller turnover, and 5.5% to circular ownership patterns. Study 2 compares extreme gradient boosting, graph convolutional networks, Graph Sample and Aggregate, and graph attention networks on the Elliptic transaction-network benchmark. Graph attention networks recorded the highest point estimates (AUROC = 0.960; AUPRC = 0.740), followed by Graph Sample and Aggregate (0.930; 0.610), graph convolutional networks (0.910; 0.560), and extreme gradient boosting (0.820; 0.370). Study 3 identifies a reported −6.8% posterior mean deviation in the Persons with Significant Control Anomaly Index after the late-June 2020 filing-regime boundary (95% credible interval: −10.9% to −2.4%), interpreted descriptively rather than causally. Overall, the findings establish an evidentiary hierarchy for governing heterogeneous anti-money-laundering signals while preserving boundaries between decision support, adjudication, and causal inference. The empirical objects are companies, transaction nodes, and register observations rather than banks.

Type of the article: Research Article

Abstract
InsurTech adoption faces challenges in emerging markets, where consumers are concerned about the fairness of insurance claims and the security of InsurTech systems. The aim of this study is to test the InsurTech adoption intention in Jordan based on trust, perceived risk, and perceived regulatory protection. The research design is based on a survey of insurance consumers in Jordan (N = 346).
Adoption intention is positively correlated with trust (r = 0.71) and regulatory protection (r = 0.60) and is negatively correlated with perceived risk (r = −0.38). Regulatory protection is positively correlated with trust (r = 0.65) and negatively correlated with perceived risk (r = −0.52). The model explains 57% of the variance in adoption intention (R-squared = 0.57). Trust has the strongest influence on adoption intention (beta = 0.520, p < 0.001), whereas perceived risk has a negative effect (beta = −0.180, p < 0.001). Regulatory protection also positively influences adoption intention (beta = 0.120, p = 0.012) and trust (beta = 0.650, p < 0.001), and negatively influences perceived risk (beta = −0.530, p < 0.001). Regulatory protection also indirectly influences adoption intention through trust (beta = 0.340, p < 0.001) and perceived risk (beta = 0.100, p < 0.001).
InsurTech adoption intention in Jordan is primarily based on confidence and assurance, where trust is the most important factor, and the influence of regulation is manifested through trust and risk perceptions.