Abstract: Credit risk is the probability of a loss resulting from a creditor’s failure to repay a loan or fulfil any other contractual obligations towards the investor. Traditionally, it relates to the hazard that a lender may not receive the owed head and premium, which follows a disruption of incomes and expanded expenses for collection. Unnecessary cash may be written to create additional income to cover for credit risk. Despite it is being impossible to know exactly who will default on commitments, satisfactorily surveying and overseeing credit risk can diminish the seriousness of a loss. The lender or investor earn a bonus for risking credit default and lending money in the form of interest from the borrower or issuer of a debt obligation. When lenders or banks provide mortgages, credit cards, visas or various types of credit or loans, there is a hazard that the borrower is probably not going to reimburse the loan. Likewise, if an organization provides credit to a client, there is a hazard that the client is not going to pay their solicitations. Credit risk additionally clarifies the risk that a guarantor may stall to make payment when asked or that an insurance company will be unable to pay a claim. Credit risks are determined based on the borrower’s general ability to reimburse an advance as indicated by its unique terms. To assess credit risk on a consumer loan, loan specialists inspect the five Cs: credit history, capacity to repay, capital, the loan’s conditions, and associated collateral. Banks have been the most important institutions of money lending and deposits. Primary functions include accepting deposits, offering loans, credit, overdraft, providing liquidity and discounting of bills. Secondary functions include providing safe custody of valuables, loans on valuables, corporate and consumer finances. Though the structure of banks has remained the same, the functionalities have been boosted. Automated tools, bots and computers have modernized the banking system. The dataset accumulated over a period of time is so huge that, automation tools and computer programs are the need of the day. In this paper we have tried to enhance the present bank credit-debit system by the use of Artificial Intelligence. Machine learning is a subset of AI and directly trains the machine by feeding the historic and runtime data collected during transactions. The machine which is trained is now capable of taking decisions, thereby making predictions. This would characterize the dataset as stored and predicted outcomes. Every business enthusiast would have keen interest to carefully study the performance of a financial institute for his/her benefit. In this assignment we have used both classification and regression algorithms to create a ML model of prediction. Linear regression model is designed from scratch using formula method. Classification algorithms like Support Vector Machine (SVM), Random Forest Classifier and KNN algorithms are effectively applied to fit to the dataset. Comparisons must be made during implementation to understand the pattern of predicted data. Regression algorithms like linear regression (developed from scratch) will be a boost to the accuracy of the assignment (categorical data excluded).
Keywords:accepting deposits, offering loans, credit, overdraft, providing liquidity and discounting of bills, Automated tools, bots and computers, Machine learning, Support Vector Machine (SVM), Random Forest Classifier and KNN algorithms, linear regression (developed from scratch) , historic and runtime data collected during transactions, AI, five Cs: credit history, capacity to repay, capital, the loan’s conditions, and associated collateral.
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DOI:
10.17148/IJARCCE.2021.10925
