scholarly journals Discovering Hidden Patterns in Loan Reimbursement

2019 ◽  
Author(s):  
Sorush Niknamian
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Central Bank ◽  
Interest Rates ◽  
Wage Rates ◽  
Banking Regulations ◽  
Practical Model ◽  
Hidden Patterns ◽  
The Cost ◽  
Mining Tools

Loans are the major resources at banks. However, in some cases the cost that they incur to banks soar and finally makes them detrimental, as a result of irregular or delaying reimbursement or not paying at all. Due to the low wage rates in Iranian banks and the Central Bank of Iran (CBI) regulations in determining interest rates for deposits and loans, banks are becoming more and more dependent to the loans and their related profits. Therefore, banks have to look for customers with low risk for punctual payment. According to defect loan reimbursement in past years, banks have to specify severe prerequisites and limited contracts in granting loans to their customers. Contravening banking regulations and lack of consistent customers' accreditation banks are getting into heavy losses. Evaluating situations of the granted loans in EN Bank of Iran during a six-month period, based upon the profiles and loans history and the trend of payments useful patterns are discovered; designing a practical model of loan payment in Iran, the future default or failure to regain the granted loans is predicted and sensible methods of granting loans in Iran are developed. In order to extract hidden patterns in data statistical methods and data mining tools with focus on decision tree techniques are applied.

scholarly journals Discovering Hidden Patterns in Loan Reimbursement

2019 ◽  
Author(s):  
Sorush Niknamian
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Central Bank ◽  
Interest Rates ◽  
Wage Rates ◽  
Banking Regulations ◽  
Practical Model ◽  
Hidden Patterns ◽  
The Cost ◽  
Mining Tools

Loans are the major resources at banks. However, in some cases the cost that they incur to banks soar and finally makes them detrimental, as a result of irregular or delaying reimbursement or not paying at all. Due to the low wage rates in Iranian banks and the Central Bank of Iran (CBI) regulations in determining interest rates for deposits and loans, banks are becoming more and more dependent to the loans and their related profits. Therefore, banks have to look for customers with low risk for punctual payment. According to defect loan reimbursement in past years, banks have to specify severe prerequisites and limited contracts in granting loans to their customers. Contravening banking regulations and lack of consistent customers' accreditation banks are getting into heavy losses. Evaluating situations of the granted loans in EN Bank of Iran during a six-month period, based upon the profiles and loans history and the trend of payments useful patterns are discovered; designing a practical model of loan payment in Iran, the future default or failure to regain the granted loans is predicted and sensible methods of granting loans in Iran are developed. In order to extract hidden patterns in data statistical methods and data mining tools with focus on decision tree techniques are applied.

Fault Diagnosis of Automobile ECUs with Data Mining Technologies

2010 ◽  
Vol 40-41 ◽  
pp. 156-161 ◽  
Author(s):  
Yang Li ◽  
Yan Qiang Li ◽  
Zhi Xue Wang
Keyword(s):  
Data Mining ◽  
Fault Diagnosis ◽  
Decision Tree ◽  
Data Stream ◽  
Electronic Control Unit ◽  
Rapid Development ◽  
Reliability And Validity ◽  
Control Unit ◽  
Use Of Data ◽  
The Cost

With the rapid development of automotive ECUs(Electronic Control Unit), the fault diagnosis becomes increasingly complicated. And the link between fault and symptom becomes less obvious. In order to improve the maintenance quality and efficiency, the paper proposes a fault diagnosis approach based on data mining technologies. By making full use of data stream, we firstly extract fault symptom vectors by processing data stream, and then establish a diagnosis decision tree through the ID3 decision tree algorithm, and finally store the link rules between faults and the related symptoms into historical fault database as a foundation for the fault diagnosis. The database provides the basis of trend judgments for a future fault. To verify this approach, an example of diagnosing faults of entertainment ECU is showed. The test result testifies the reliability and validity of this diagnostic method and reduces the cost of ECU diagnosis.

OntoDTA: Ontology-Guided Decision Tree Assistance

2017 ◽  
Vol 16 (03) ◽  
pp. 1750031
Author(s):  
Khaled Benali ◽  
Sidi Ahmed Rahal
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Computer Science ◽  
Domain Ontology ◽  
Effective Application ◽  
Research Areas ◽  
Mining Tools

The effective application of a Decision Tree (DT) process is beset with many difficult and technical decisions about the choice of algorithms, parameters, evaluation, etc. Therefore, we propose assistance by using ontologies for addressing the above-mentioned challenges that face the non-specialist DT miner (person). Ontologies have been used in various research areas such as computer science, including data mining tools. In this paper, we propose the realisation of a domain ontology for DT OntoDTA to empower the non-specialist DT miner throughout the key phases of the DT process. OntoDTA ontology contains the knowledge of DT and provides a common terminology that can be shared and processed by DT miners.

scholarly journals Analyzing Students Performance Using Frequent Item Set Mining, Clustering & Classification

2012 ◽  
Vol 1 (2) ◽  
pp. 31-41
Author(s):  
Rudresh Shirwaikar ◽  
Nikhil Rajadhyax
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Decision Tree ◽  
Student Performance ◽  
Real World ◽  
Scientific Inquiry ◽  
Educational Data Mining ◽  
The Subject ◽  
Hidden Patterns

Educational data mining (EDM) is defined as the area of scientific inquiry centered around the development of methods for making discoveries within the unique kinds of data that come from educational settings , and using those methods to better understand students and the settings which they learn in. Data mining enables organizations to use their current reporting capabilities to uncover and understand hidden patterns in vast databases. As a result of this insight, institutions are able to allocate resources and staff more effectively. In this paper, we present a real-world experiment conducted in Shree Rayeshwar Institute of Engineering and Information Technology (SRIEIT) in Goa, India. Here we found the relevant subjects in an undergraduate syllabus and the strength of their relationship. We have also focused on classification of students into different categories such as good, average, poor depending on their marks scored by them by obtaining a decision tree which will predict the performance of the students and accordingly help the weaker section of students to improve in their academics. We have also found clusters of students for helping in analyzing student  performance and also improvising the subject teaching in that particular subject.

Adaptive Higher Order Neural Network Models for Data Mining

2010 ◽  
pp. 86-98 ◽  
Author(s):  
Shuxiang Xu
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Network Models ◽  
Neural Network Models ◽  
Adaptive Models ◽  
Large Databases ◽  
Hidden Patterns ◽  
Discontinuous Data ◽  
High Order Nonlinearity ◽  
Mining Tools

Data mining, the extraction of hidden patterns and valuable information from large databases, is a powerful technology with great potential to help companies survive competition. Data mining tools search databases for hidden patterns, finding predictive information that business experts may overlook because it lies outside their expectations. This chapter addresses using ANNs for data mining because ANNs are a natural technology which may hold superior predictive capability, compared with other data mining approaches. The chapter proposes Adaptive HONN models which hold potential in effectively dealing with discontinuous data, and business data with high order nonlinearity. The proposed adaptive models demonstrate advantages in handling several benchmark data mining problems.

scholarly journals Interest rate stepping: Theory and evidence

2007 ◽  
Vol 1 (1) ◽  
pp. 67-93
Author(s):  
Sylvester Eijffinger ◽  
Eric Schaling ◽  
Willem Verhagen
Keyword(s):  
Interest Rate ◽  
Central Bank ◽  
Interest Rates ◽  
Aggregate Demand ◽  
Rate Sensitivity ◽  
Theoretical Models ◽  
Sufficient Evidence ◽  
Rate Step ◽  
The Interest Rate ◽  
The Cost

A stylised fact of monetary policymaking is that central banks do not immediately respond to new information but seem instead to prefer to wait until sufficient ‘evidence’ to warrant a change has accumulated. However, theoretical models of inflation targeting imply that an optimising central bank should continuously respond to shocks. This article attempts to explain this stylised fact by introducing a small menu cost which is incurred every time the central bank changes the interest rate. It is shown that this produces a relatively large range of inaction because this cost will induce the central bank to take the option value of the status quo into account. In other words, because action is costly, the central bank will have an incentive to wait and see whether or not the economy will move closer to the inflation target of its own accord. Next, the article analyses the implications for the time series properties of interest rates. In particular, we examine the effect of the interest rate sensitivity of aggregate demand, the slope of the Lucas supply function and the variance of demand shocks on the size of the interest rate step and the expected length of the time period till the next interest rate step. Finally, we analyse the effect of menu costs on inflationary expectations. In this respect we find that the economy will suffer from an inflationary bias if the cost of raising the interest rate exceeds the cost of lowering it.

scholarly journals COMPARISON OF DT& GBDT ALGORITHMS FOR PREDICTIVE MODELING OF CURRENCY EXCHANGE RATES

2020 ◽  
Vol 1 ◽  
pp. 56-61 ◽  
Author(s):  
Maan Y. Anad Alsaleem ◽  
Safwan O. Hasoon
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Decision Tree ◽  
Central Bank ◽  
Exchange Rates ◽  
Global Market ◽  
Gradient Boosting ◽  
Currency Exchange ◽  
Stock Market Forecasting ◽  
Currency Exchange Rates

Recently, many uses of artificial intelligence have appeared in the commercial field. Artificial intelligence allows computers to analyze very large amounts of information and data, reach logical conclusions on many important topics, and make difficult decisions, this will help consumers and businesses make better decisions to improve their lives, and it will also help startups and small companies achieve great long-term success. Currency exchange rates are important matters for both governments, companies, banks and consumers. The decision tree is one of the most widely artificial intelligence tools used in data mining. With the development of this field the decision tree and Gradient boosting decision tree are used to predicate through constructed intelligent predictive system based on it. These algorithms have been used in many stock market forecasting systems based on global market data. The Iraqi dinar exchange rates for the US dollar are affected in local markets, depending on the exchange rate of the Central Bank of Iraq and the features of that auction. The proposed system is used to predict the dollar exchange rates in the Iraq markets Depending on the daily auction data of the Central Bank of Iraq (CBI). The decision tree and Gradient boosting decision tree was trained and testing using dataset of three-year issued by the CBI and compare the performance of both algorithms and find the correlation between the data. (Runtime, accuracy and correlation) criteria are adopted to select the best methods. In system, the characteristic of artificial intelligence have been integrated with the characteristic of data mining to solve problems facing organization to use available data for decision making and multi-source data linking, to provide a unified and integrated view of organization data.

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