DESIGN AND IMPLEMENTATION OF A COMPUTERIZED LOAN MANAGEMENT SYSTEM FOR REJECTING OR APPROVING LOAN REQUESTS USING CREDIT RISK AND EVALUATION MODELS.
Over the past few years, the financial sector have dedicated numerous resources to developing internal models to better quantify their financial risks and attain economic capital. These endeavors have been supported and recognized by bank regulators. Over time, banks have extended these efforts into the field of giving out loans to qualified customers. The loans are given out based on certain conditions like collateral and other factors, these factors may include checking if the customer is loan worthy, or through customers’ salary structures. These strict conditions must be clearly met by the customer seeking loan offer from the bank or they won’t be qualified for the loan they are requesting for. In this research, a computerized loan management system for rejecting or approving loan request using credit risk and evaluation models is to be designed, to solve the enormous challenges financial institutions face in giving out loans. The concept of giving out loans by banks was well understood and the pros and cons were ascertained, this helped in building the system to meet credit risk and evaluation models. The computerized system will provide less risk for the banks when reviewing customers loan request, the system will ascertain if the customer is loan worthy or not automatically. The newly developed system is automated, effective, comprehensive, interactive, and gives the financial institution less credit risk.
1.1 BACKGROUND OF THE STUDY
Financial institutions offer loan services to small organizations individuals and this was initiated after a number of considerations on the banking system. This project attempts to design and implement a computerized loan management system for rejecting or approving loan request using credit risk and evaluation models and know about loan sanctioning and lending procedure among banks across the globe. It also attempts to use the credit risk and evaluation models to develop a strict approach to these areas, to think through policies, principles, and practices to accomplish the new tasks. By the time the system is fully implemented, it will be able to understand, think, and be fully equipped to handle today’s and also for tomorrow’s credit managerial work in financial institutions. Moreover, there is a significant question for banks and their regulators is assessing the accuracy of a model’s forecasts of credit misfortunes and losses, particularly given the small number of accessible forecasts and estimates due to their typically long planning horizons. Utilizing the credit risk and evaluation, we propose evaluation techniques for credit risk models based on cross sectional dependent. In particular, models are evaluated and accessed not only on their estimates over time, but also on their forecasts at a given period in time for simulated credit portfolios. Once the estimates corresponding to these portfolios are generated, they can be evaluated using various statistical methods. This project when implemented, will provide a smooth channel for processing of Loans in financial institutions across the country. The proposed system upon completion will replace the manual loan application process and automates the loan process from both, the banks as well as the customers’. Customer can now apply for loans seamlessly without having to visit the banks and if approved customers can track their loan details from the comfort of their homes. The loan management system with credit risk and evaluation models is a very efficient process to handle all loan related transaction in a very accurate and convenient way. The Credit Risk becomes important to implement in financial and accounting. It incorporates bankruptcy forecasting, financial distress, corporate performance bunching / expectation and credit risk estimation. The online Bank loan management system is an interface which facilitates a customer to apply for a loan using a digital channel through the internet, the system check for possible bankruptcy prediction, credit risk estimation to track the status from time-to-time. This system provides detail about the customers’ financial status over a period of time, their loan details, risk estimation and possible bankruptcy prediction. Getting a loan is a very tiring and complicated process in Nigeria. It may take weeks or even months for loans application to get reviewed, before getting approvals and people have to visit the financial office for documentation and customer verification.
1.2 STATEMENT OF THE PROBLEM
Currently, most financial institutions do not have any an automated system to help manage the data of customers applying for Loans, Grants and Investments with credit risk evaluation. They have to rely on manual procedures which is time consuming and doesn’t calculate the credit risk evaluation in case of bankruptcy. This manual procedure doesn't maintain customer records with proper security and can’t track details easily. It doesn’t allow the customer to check their loan request, submit bank account statement if need be. The Existing manual procedure isn’t equipped with basic functionalities of fast access to information such as customer details and maintenance of all the loan details so it involves lots of paperwork. Apart from administrative task being cumbersome, manual system of registration is also long and error-prone.
1.3 AIMS AND OBJECTIVE OF THE STUDY
The main aim of this research to Design and Implement a computerized loan management system for rejecting or approving loan request using credit risk and evaluation models that will allow bank customers to easily access loans without stress or visiting the banks with the following objectives:
⦁ To analyze the loan sanctioning procedures of the applicants using credit risk and evaluation models.
⦁ To design and implement a system that will help identify the credit worthiness of the borrowers.
⦁ To help the efficiency of loan disbursement and Loan recovery among banks.
⦁ Increase accountability in the financial sector.
1.4 MOTIVATION OF THE STUDY
The motivation for the study is to develop a system for bank customers to easily apply for loans directly from their banks via the computerized loan management system and their loan type from the list available in the system. Once the loan application is complete by the customer, this data is automatically sent to the bank server, a login ID and secured password is sent to the user. The application is received by financial institution for moderation and verification.
1.5 SCOPE OF THE STUDY
They are different financial institutions in across the globe with different services, for this research, the researcher pays attention to developing a computerized loan management system for any forward-thinking financial institution ready to use technology to dispense loans with critical credit risk and evaluation models.
The chapter reviews literature from other scholars on the aspect of credit risk and portfolio allocation. It specifically looks at theoretical literature in section 2.2 Credit risk management in 2.3 Empirical literature in 2.4 and lastly the chapter summary in 2.5.
2.2 Review of Theories
2.2.1 Liquidity Theory of Credit
This theory, first suggested by Emery (1984), proposes that credit rationed firms use more trade credit than those with normal access to financial institutions. The central point of this idea is that when a firm is financially constrained the offer of trade credit can make up for the reduction of the credit offer from financial institutions. In accordance with this view, those firms presenting good liquidity or better access to capital markets can finance those that are credit rationed. Several approaches have tried to obtain empirical evidence in order to support this assumption. For example, Nielsen (2002), using small firms as a proxy for credit rationed firms, finds that when there is a monetary contraction, small firms react by increasing the amount of trade credit accepted. As financially unconstrained firms are less likely to demand trade credit and more prone to offer it, a negative relation between a buyer’s access to other sources of financing and trade credit use is expected. Petersen and Rajan (1997) obtained evidence supporting this negative relation..