Abstract This paper reviews various analysis conducted in order toobtain a relationship between student loans and loan defaults furthermoresuggest ways to minimise the default risk on these loans. Empirical research conductedby scholars suggest that education loan defaults are mainly influenced by various factors such as security, borrowermargin, and repayment periods. On the other hand, the presence of guarantor or co-borrower andcollateral significantly reduce default loss rates. Other determinants of thedefault risk could be socioeconomic characteristics of borrowers and theirregional locations which act as important factors associated with educationloan defaults. In order to mitigate risk, banks can follow certain pricingstrategies that have been suggested by various empirical analysis that involvesegmentation of borrowers on various characteristics.
Our main objective is to study the performance of loans overtime and identify key risk factors of such loans across various geographies andconstitutions. Introduction Banks act as a backbone of an economy, they play animportant role in promotion of education and skill development. In an emergingeconomy such as india, education loans are essential for pursuing highereducation. An education loan scheme provides financial assistance of Rs 1million for pursuing higher education and Rs 2 million for studies abroad.
Theinterest rate is around 13%, depending upon the amount of the loan. Repaymentcommences one year after completion of the course or six months after securinga job, whichever is earlier. The maturity period of the loan for studies inIndia (up to Rs 1 million) and studies abroad (Rs 2 million) is 5–7 years.
Generally, no security is required for loans up to Rs 400 000. But forloan amounts ranging from Rs 400 000 to Rs 750 000, banks may seekthird party guarantee. For loans above Rs 750 000, tangible collateralsecurity of suitable value, along with the assignment of the future income ofthe student for payment of instalments, is required. The loans for vocationalcourses are unsecured loans generally in the range of Rs 20 000 to Rs150 000 for those pursuing courses that have a tenure ranging from 2 to 3months to 3 years. The moratorium period ranges from six months to one year.
As per the data collected by Bandhopadhya,A.(2016), thegross NPA for student loans is around 6%, whereas for the retail counterpartsit is 2%-3% (2012-13). The accumulation of NPA with the bank has led to a sharpdecline in the growth rate of education loans across various commercial banksin recent years. The various causes of default of loans are – Idiosyncratic borrower specific problems; These includerisk arising out of repayment problems, collateral risk, academic failure,financial problems etc.- Systematic factors ; These involve various external factors such as unavailability of jobsdue to economic slowdown, recession, lack of quality education etc.- wrong selection of beneficiaries- ineffective follow up of advances- failure of debt collection framework in banks Literature review One of the earliest researches done on the subject ofstudent loans was by Boyes, Hoffman, andLow (1989) wherein research demonstrated a method of successfully determining theprobability of individual default risk usingthe data on borrower specific personal characteristics, economic variables andfinancial variables from credit card applicants.
It was by Greene (1992) when he successfully developed astatistical scoring model for discrete choice moreover, the research wassuccessful in explaining the utility ofthis model to determine the credit risk of an individual. Scoring models are anaid in predicting the future default and survival probability of a customer andhelp financial institutions make informed choices. Fritz, Luxenburger, andMiehe (2007) with the help of variables such as socio –demographic information,account information, credit history etc described the retail score carddevelopment process through the linear combination of the above mentionedvariables as input.This was followed by Roszbach (2004) wherein he developed abivariate Tobit model to predict future defaults and loan survival time for newretail applicants.
Such models allow banks to better predict the risk of acustomer and make more realistic evaluations of the returns.Flint (1997), Knapp and Seaks (1992), Volkwein and Szelest(1995), and Woo (2002) focused on the individual’s socio economic backgroundand its relation to defaulting of student loans. Gross, Cekic, Hossler, andHillman (2009) conducted a survey of studies of student loan default. Summarising research between the 1990s to the 2007s factors influencingthe student loan defult can be classified as under: a) students characteristics b) institutioncategory (type, area, educational outcomes etc.);c) level of student debtd) students’ employment and income and total debt position.
Lochner, Stinebrickner, and Suleymanoglu (2013) have usedsurvey and administrative data from the Canadian Student Loan Programme andhave considered demographic characteristics (age, gender, and aboriginalstatus), educational background, income and other financial resources in theirstudy. They find that income level, access to savings and family support,educational attainment, and various demographic factors have influence onstudent loan repayment behaviour. The findings from these studies have further guidedresearchers in the study and the framing of the hypotheses, the methodology,and the choice of variables. Analysis Bandhopadhya,A. (2016) examined education loan default inregards of the various characteristics associated with the loan (loan amount,interest rate and repayment period), and security positions (margin given,security, etc.
). The mathematical model included a multivariate statisticaltechnique to control multiple factors that contribute to default risk. We also check how various borrower characteristics (age,marital status, presence of guarantor/co-borrower, etc.), geographic locationsof borrowers (rural, semi-urban, urban, and metro), course related factors(domestic vs. overseas education and placement record) and rating of theeducation institutes explain risk of default in student loans. VariablesAcross various empirical researches conducted by scholars, experimentationincludes certain variables that affect the probability of defaulting studentloans such as age, presence of guarantor/co-borrower, marital status, etc.),geographic locations of borrowers (semi-urban, urban, rural,and metro), courserelated factors (domestic vs. overseas education and placement record) andrating of the education institutes.
Using the multivariate model as per Bandopadhya ,A(2016), femaleborrowers have a higher risk of defaulting on student loan as compared to themale borrowers. as per calculations, male borrowers are 1.42 times safer thantheir female counterparts.
In a separate regression, it was established that married borrowers are riskier thanunmarried ones. Study of time period as a variable established that longerthe repayment period, the lower is the chance of default. A stark difference was observed within the geographical differenceswherein borrowers from the urban and rural centres were riskier than the urbanborrowers. This captures the local situational factors on risk of default. Efforts to test whether merit and placement records havestatistically significant risk reduction effect by introducing interceptdummies as additional variables in the regressions was also attempted.
The resultsshowcased coefficients to be negative, however they are not statisticallysignificant.Therefore the studyestablishes that in order to lower default rate the loan must be secured andthe borrower’s own contribution for the course must be higher. Moreover, the borrowers with security are 1.5 times more likely to remain solventthan those without security. Hypothesis The hypothesis of the paper is built on the framework set byvarious researches done in the past and the related literature. The study included a set of variables such as age, maritalstatus, gender, geographical regions etc. the hypothesis was if the following affectthe default risk, calculating the probability of the same and controlling themmay lead to a decrease in default risk of student loans.
Design The study was a review type research design. It comprised ofreviewing literature on the subjectmatter of student loans and coming to a common conclusion and suggestion forreduction of the default risk. Conclusion A review of various researches suggest that on order to reducecredit risk in education loans banks need to focus on strengthening credit riskassessment techniques, borrower risk assessment through credit rating,portfolio monitoring, due diligence in lending and institute performancemeasures. Merit, employability of course, and reputation of institutions shouldmatter in loan appraisal to reduce the default risk. Creating awareness amongthe borrowers/co-borrowers for repayment of the dues as scheduled and buildinga repayment culture among the students is also part of the socialresponsibility for banks. Regular tracking of the student and follow-up mayalso reduce the risk of default. Employers should be sensitised regardingpayment of equated monthly instalments (EMI) of education loan of their employees.Moreover, by segmenting borrowers by probability of defaultand loss given default in a multidimensional scale, banks can adopt better lossmitigation and pricing strategy to resolve borrower problems.
Borrowers withhigh probability of default and high loss severity can be segmented from lowercredit risk borrowers.Though the smaller loans are mostly unsecured, for biggeramounts, banks may ask for securities (in the form of fixed deposits (FD), LICpolicies and property) and co-applicant as a guarantor to reduce the risk. Werecommend that banks use yearly cohort default rates measures (e.
g. transitionmatrix or NPA movements) to track the rating slippages to estimate theportfolio credit risk. This is to be done across regions, course types, institution-wise,and so on, to better understand and monitor portfolio risk.
A portfolioapproach may enable a bank to better monitor the risky customers and wouldallow for targeted collection efforts to resolve the default. Banks mayprioritise the collection process for high risk accounts earlier in thedelinquency cycle. Else, they may opt for credit guarantee protection from thegovernment/private agent.