Search

The interest collected by the main borrowers is collected to pay back the principal borrowed from the depositary bank. In financial risk management, credit risk assessment is becoming a significant sector. For the credit risk assessment of client data sets, many credit risk analysis methods are used. The assessment of the credit risk datasets leads to the choice to cancel the customer's loan or to dismiss the customer's request is a challenging task involving a profound assessment of the information set or client information. In this paper, we survey diverse automatic credit risk analysis methods used for credit risk assessment. Data mining approach, as the most often used approach for credit risk analysis was described with the focus to various algorithms, such as neural networks. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

In recent decades, machine learning has become a crucial factor in terms of automating business operations and assisting in the decision-making process within an organization. With the huge volume of data generated at an unprecedented rate has motivated researchers and industry analysts to constantly develop effective and efficient analytical models machine learning techniques. This study adds to the data mining community by evaluating some of the most significant text mining techniques and presenting a predictive model that will supposedly ease the process of literature review for researchers. In addition, it compares the execution of the model in terms of cost, energy consumption, accuracy and scalability in three different environments, namely, google cloud instance, google cloud functions and distributed raspberry PIs. Results yielded in our study showed that distributed Raspberry PIs can have a highly positive impact in terms of lowering costs and being energy efficient. On one hand, we found out that machine learning algorithms can be adapted and run on distributed raspberry PIs with low cost and low energy consumption compared to cloud alternatives. On the other hand, this solution does not offer great scalability and it requires more time on management, deployment and configuration. The distributed Raspberry PIs also showed bad performance on execution time compared to the other alternatives when comes to high processing power.

Next Generation Incident Response System (NICS) is a platform developed by MIT Lincoln Lab that is currently being used in Macedonia by the Centre for Crisis Management (CCM). It allows coordination during natural disasters between first responders of various departments and allows them to use state of the art tools to communicate and share information. This research focuses on advancing the platform by introducing intelligent agents to the platform, based on machine learning techniques and natural language processing. Our goal is to leverage data generated in social media and feed NICS with automatically processed information from these media categorized in twelve different needs (categories). This paper presents the current state of our research, preliminary results and final goals. This work is licensed under aCreative Commons Attribution-NonCommercial 4.0 International License.