SMALL BUSINESS OWNERS' AGREEABLENESS AND INFLUENCE ON THE FINANCIAL RISK FACED BY BANKS THROUGH THE APPLICATION OF DISTRIBUTED GRAPH-BASED DATA MINING IN THE UNITED STATES.

The primary objective of this experimental research study is to investigate unique small business owners' personality traits, and the influence of their agreeableness on the financial risk faced by banks through the application of distributed graph-based data mining in the United States. We use the parallel coordinators based distributed graphical model to find out the hidden patterns in the input data. Prior studies only found negative significantly correlated agreeableness with the microloans having lower risk measurement. Our studies found both positive significantly correlated agreeableness with the microloans having high risk measurement for the group participants falling within the age range of 36-55 years, and negative significantly correlated agreeableness with the microloans having lower risk measurement for the group participants falling within the age range of 36-55 years. The additional novel findings of our study are that, while we understand that most of the participants to whom we sent the survey did not have the microloans yet, they can function as good stable candidates for the secured microloans per our experimentally unique graphical trends analysis. We discovered that the people who have microloans tend to largely have the following characteristics: usually warm, friendly, and tactful, between 36 and 55 years of age, female, and white. Thus, banks should search for microloan candidates with similar new characteristic to be sure that they improve the quality of bank risk and loans do not fail in their financial asset's portfolio. The distributed graph analytics shows more accuracy with prescriptive analytics when compared to the traditional statistical approach. These findings can contribute to improving bank risk to build more stronger financial assets with lower bank risk for our financial institutions around the World and the modern new data analytics. [ABSTRACT FROM AUTHOR]