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This article estimates the effects on depressive symptoms, family relationships, social support, and academic self-efficacy of participating in a forgivable loan program, using an instrumental variable (IV) estimation strategy. In particular, we estimate local average treatment effects (LATE) of program participation on these variables, using program eligibility as an instrument. In this case, estimation needs to account for endogeneity, given that not all eligible students decided to participate in the program and that variables affecting the decision to participate in the program might be related to the psychological variables being evaluated. We found negative effects on all the psychological variables. Additionally, we found that program participants were significantly more likely to move and attend elite, accredited, and more expensive universities, which explains the observed psychological symptoms. Results are interpreted in terms of the pressures that non-traditional students receiving financial aid face when adapting to college.

The authors present empirical evidence that borrowers, consciously or not, leave traces of their intentions, circumstances, and personality traits in the text they write when applying for a loan. This textual information has a substantial and significant ability to predict whether borrowers will pay back the loan above and beyond the financial and demographic variables commonly used in models predicting default. The authors use text-mining and machine learning tools to automatically process and analyze the raw text in over 120,000 loan requests from Prosper, an online crowdfunding platform. Including in the predictive model the textual information in the loan significantly helps predict loan default and can have substantial financial implications. The authors find that loan requests written by defaulting borrowers are more likely to include words related to their family, mentions of God, the borrower’s financial and general hardship, pleading lenders for help, and short-term-focused words. The authors further observe that defaulting loan requests are written in a manner consistent with the writing styles of extroverts and liars.

In Chapter 16, we saw Mapula Trust convert their entire loan to IkamvaYouth into a grant through a forgivable loan, but this is not a viable strategy for most funders. In this next journey, we join Rahil Rangwala from the Michael and Susan Dell Foundation as he develops a contract that aims to incentivize school operators in India to achieve better educational outcomes by linking the cost of the loan to impact outcomes. In other words, the contract he designs allows the school operators to qualify for interest rate rebates through achieving pre-defined impact goals. This is an example of impact-linked financing: the linking of financial incentives to the achievement of social and/or environmental performance targets.

In the last chapter, Upaya used recoverable grants (grants that convert to loans), to drive affordable capital to Indian entrepreneurs. In this chapter, we head to South Africa to see how funders can use forgivable loans (loans that convert to grants), to support non-profits and social enterprises.

In this paper, we investigate some implications of recent results about salience on loan decisions. Using the framework of focus-weighted utility we show that consumers might take out loans even when that yield them negative utility due to the focusing bias. We suggest, however, that this can be counterbalanced and consumers might be more prudent in their decisions and less likely to take out such loans when the usual fixed-installments plan is coupled with an equivalent decreasing-installments option. Moreover, we show that this is true even for loans with prepayment options or when borrowers take default into consideration. We argue that harmful loan consumption could be decreased if a policy would prescribe presentation of loan repayment schedules in a way that employs this effect.

We investigate the ability of small business borrowers to signal to investors their credit worthiness through the use of text descriptions in the peer‐to‐peer lending market. Specifically, we examine the relationship between the loan description written by a borrower and whether or not the project is funded by investors. Using textual analysis, we find that small business loan descriptions can be used to predict the likelihood that the loan will be funded. We also find that an index, created from a textual analysis of the words used in the loan description, can forecast the performance of the loan, specifically whether or not the loan defaults. This index has the strongest impact when we focus on borrowers with low FICO scores, suggesting that for these individuals the description can signal information that standard measures used for lending purposes cannot. Overall, it appears as though investors are making investment decisions based on proper and relevant signals given by the borrowers through the loan description. (JEL D47, D53, D82, D83, G14, G21)

This study analyzes the benefits of rapid student loan repayment, defined here as borrowers who repaid their cumulative undergraduate loan debt in half the time of the expected repayment cycle (10 years). Drawing from data on borrowers in two nationally representative samples, I first explain the analytic framework employed to identify rapid loan repayers, then examine whether rapid loan repayment is associated with financial benefits in terms of salary, homeownership, and non–poverty level, identifying how rapid loan repayers differ from their non–rapid loan repayer and nonborrower counterparts. Results show salary benefits associated with rapid loan repayment and indicate that among rapid loan repayers, cumulative loan debt generally did not surpass $15,000. These findings suggest that policy-makers may consider the adoption of shorter repayment plans with clear eligibility cutoff amounts as an alternative to the more common 10-year fixed plan.

Small businesses are among the hardest hit by the COVID-19 crisis. Many are shuttered, and far more face cash flow constraints, raising questions about just how many will survive this recession. The government has responded with a critical forgivable loan program, but for many of these businesses, this program alone will not provide the cash they need to retain workers, pay rent, and help their business come back to life when Americans are no longer sheltering in place. This essay calls on regulators to find new and creative ways to work with existing intermediaries, including banks and online lenders, who have the infrastructure and tools needed to help small businesses get the additional loans they need to survive and thrive. Leveraging existing institutions could enhance the speed, scale, and scope of the government’s response, all critical virtues in the efforts to support small business.

The article is a review of the Paycheck Protection Program (“PPP”), an initiative under the Coronavirus Aid, Relief, and Economic Security (“CARES”) Act. The program was meant as a lifeline for small businesses by offering forgivable loans. However, recent reports have indicated that billions went to large public companies. In the article I examine the pitfalls of the program such as its ambiguous guidelines.

In this study, we seek to understand whether soft information conveyed by contracting language found in private loan agreements is informative regarding borrower risk. We proxy for credit-risk-relevant soft information using Loughran and McDonald’s uncertainty measure. We first examine initial contract terms and find that, incremental to traditional summary measures of credit risk, increased contractual uncertainty is associated with higher initial loan spreads and a greater likelihood of using dynamic and performance-pricing covenants. We then turn to examine realized credit risk over the life of the loan and find that increased uncertainty is associated with a higher likelihood of future loan downgrades and loan amendments. We corroborate our results on the risk relevance of soft information by showing that the bid-ask spreads of loans trading on the secondary loan market are increasing in uncertainty. Overall, the evidence we provide is consistent with embedded linguistic cues in loan agreements publicly revealing the credit risk assessments of privately informed lenders.

Past empirical studies appear to support the idea that banks and finance companies do not differ in their ability to resolve adverse selection problems associated with issuing new debt. In this article, we find there is a difference. More specifically, using an event study we find larger abnormal returns for secured loan disclosures to lower quality borrowers when the lender is a finance company versus a bank. This suggests the market views finance companies as more effective than banks in evaluating/monitoring lower quality borrowers obtaining secured loans. We posit this is due to finance companies’ greater expertise in this type of lending, resulting from specialization. Our findings extend the literature on how lender identity can influence signals about firm value from loan disclosures. Our results also support recent findings that positive abnormal returns to borrowing firms may not be a general feature across the loan population, but may be restricted to smaller, lower quality borrowers. Fi...

Purpose The purpose of this paper is to identify determinants of feasible outcome events (expired with no loss, settled for loss, still performing) and time to event of Farm Service Agency (FSA) operating and farm ownership (FO) loan guarantees. Design/methodology/approach Data on 19,126 FSA guaranteed loans, which were made by various lenders to farmers who have limited ability to obtain loans from normal sources without the Federal guarantee, were collected. Cox proportional hazards models for operating loans (OLs) and FO loans are estimated to identify borrower characteristics, loan characteristics, lender types, and farm and macroeconomic environment factors that influence guarantee outcomes. Findings Loans with different characteristics (loan amount, loan term, lender type, region originated) and assistance programs (Beginning Farmer, Interest Assistance) have differing guarantee outcomes. Contemporaneous variables, in particular delinquency status, have a significant impact on guarantee outcomes. Research limitations/implications All loans were originated in calendar years 2004 and 2005. Since FO loans may have as long as 40 year terms, results are not as robust for FO loans as for OLs. Practical implications Different loan characteristics and macroeconomic conditions significantly influence the occurrence of possible guarantee outcomes and time to the outcomes. Originality/value Guaranteed loans are the primary method of government credit assistance to US farm operators. Data on individual borrowers have been difficult to obtain for much of the life of the guaranteed program because loan applications are held privately. This study provides insight on how various factors drive guarantee performance which is useful to policy makers trying to increase guaranteed loan program efficiency.

More than 80% of US syndicated loans contain at least one fee type and contracts typically specify a menu of spread and different types of fees. We test the predictions of existing theories about the main purposes of fees and provide supporting evidence that: (1) fees are used to price options embedded in loan contracts such as the draw-down option for credit lines and the cancellation option in term loans; and (2) fees are used to screen borrowers about the likelihood of exercising these options. We also propose a new total-cost-of-borrowing measure that includes various fees charged by lenders.

Firms can be credit constrained both because banks deny credit when they apply for it and because they refrain from filing for the loan in the first place, anticipating rejection. Interestingly the latter aspect has been only marginally investigated, so far and, no previous research pays attention to firm manager’s trust in the loan manager as a factor that can affect firm’s decision to file for a loan. Present research investigates the role of the trust of firm’s managers in the loan managers as a determinant of not being discouraged from applying for a loan by looking at a strictly randomly selected sample of firms in China. By relying on a structural equation model that investigates both the direct and the mediating role of trust we find that the firm manager’s trust in the loan manager affects the firm’s decision to file for a loan by reducing the firm’s risk of being a discouraged borrower. We also discover that trust mediates the role of the firm manager’s experience and the monitoring activity pursued by the bank.

SYNOPSIS In this study, we examine whether and how borrowing firms' financial statement comparability affects the contracting features of syndicated loans. Using a sample of loans issued by U.S. public firms in the syndicated loan market over the period 1992–2008, we find strong and robust evidence that financial statement comparability is negatively associated with loan spread and the likelihood of pledging collateral, and positively associated with loan maturity and the likelihood of including performance pricing provisions in loan contracts. We also find that borrowing firms with greater financial statement comparability are able to complete the loan syndication process more swiftly, form loan syndicates enabling the lead lenders to retain smaller percentages of loan shares, and attract a greater number of lenders and, particularly, a greater number of uninformed participating lenders. Altogether, these findings are consistent with the view that financial statement comparability plays an important role in alleviating information asymmetry in the syndicated loan market. JEL Classifications: G12; G14; M41

Loan guarantee schemes have existed since 1953 (in the US) and are widely used throughout the world to provide financial support to smaller firms by guaranteeing loans from commercial banks. The UK government has been an active supporter of loan guarantees since 1981, and has a long track record of modifying its scheme to reflect changing market conditions and the financing needs of its SME sector. Arguably the two most significant changes occurred in 2008 when the 5-Year Rule on eligibility was removed and in 2009 when the long-standing Small Firms Loan Guarantee Scheme was replaced by the Enterprise Finance Guarantee Scheme. We treat the removal of the 5-Year Rule as a natural policy experiment and empirically question whether, on economic grounds, this was a sensible policy. Our findings suggest that the 5-Year Rule was a better policy choice with regard to employment but had no impact on sales growth.

Usually, loan transactions contracted in practice are nonrandom; that is to say, all amounts received (principal) and paid (period instalments) by the borrower are previously agreed with the lender, as well as their respective dates. In this paper, two new alternative loan models are introduced, depending on whether the borrower survives or not to fulfil all repayment obligations. In this way, either the initial or the final date of repayments can be subject to this contingency. Additionally, the different parameters of such random transactions are determined, as well as several measures of profitability/cost for the lender/borrower, respectively. These transactions can be attractive for both the lender and the borrower, which therefore make them worthy of consideration and subsequent implementation for the benefit of both parties.

We construct a synthesized model to study credit rationing by loan size. In our model, the borrower faces a trade-off between raising debt and exerting costly effort to undertake an investment project. In the absence of agency costs, increasing the loan size at the equilibrium interest rate raises the default risk and hence reduces the average cost of the loan for the borrower, so the borrower always demands a larger loan than what the lender can offer. Furthermore, agency cost raises this excess demand for a given interest rate. If the agency cost is sufficiently high, the borrower is unable to obtain the loan that she needs at any interest rate, requiring the use of non-price instruments in the loan contract. This is the common logic underlying the agency models of non-price credit rationing, as we show in the cases of costly state verification, money diversion, risk-shifting and hidden shirking.

Despite the prominent role that information plays in the economic theory of credit markets, no direct evidence exists on the causal relationship between the availability of information about loan applicants and loan performance. This paper provides such evidence by exploiting an unanticipated change in the amount of information visible in an online market for loans to measure the impact of lender information on loan outcomes. Conditional on data available in both periods, allowing lenders to access more borrower credit information substantially reduced default rates among high-risk borrowers by 17 percentage points on average but had almost no effect on low-risk borrowers. Immediate lender returns increased by about 12 percentage points and took 5 weeks to decay. Among high-risk loans, returns converged within credit grade bins. Using panel information on lenders, I find that the information improved loan performance in two ways: first, it significantly improved the screening performed by lenders already active on the website. Second, it attracted new lenders who were better at screening loan applicants and earned higher returns. I test whether the reform resulted in selection among loan applicants using data that is unobserved by lenders in both periods. I find that there was no change in unobserved credit quality among loan applicants, but that the information improved lenders’ ability to select the (unobservably) higher quality borrowers from the pool of applicants. I also find suggestive evidence that lenders’ beliefs about loan applicants, as measured by the minimum interest rate at which they were willing to lend, converged.

A common situation is where a borrower is offered the choice of two types of loans—a fixed interest rate loan for a fixed period or a variable interest (floating interest) rate loan. Usual wisdom and advice is that the borrower opts for the fixed rate loan if interest rates are anticipated to rise in the future or opts for the variable rate loan if interest rates are anticipated to fall. However, depending on the magnitude of the rates offered, and the rate uncertainty in the market, such wisdom may not always be well founded. This note demonstrates why this might be. It derives a readily usable, low-mathematical-background expression showing, for borrowers, when fixed and variable rate loans are equivalent and when one rate type is better/worse than the other.

The authors describe the structural solution of the loan rate as a function of default and response risk that maximizes expected return on equity for a lender's portfolio of risky loans. Under the assumptions of our model, the non-linear differential equation for the optimizing price is found to be separable in transformed financial, response and risk variables. With an end-point condition where default-free borrowers are willing to borrow at loan rates higher than the lender's cost of funds, general solutions are obtained for cases where default probabilities may depend explicitly on the offered loan rate and where adverse selection may or may not be present. For the general solution, we suggest a numerical algorithm that involves the sequential solutions of two separate transcendental equations each one of which depends on parameters of the risk and response scores. For the special case where the borrower's default probability is conditionally independent of loan rate, it is shown that the optimal solution is independent of Basel regulations on equity capital. © 2014 Operational Research Society Ltd. All rights reserved.

Beck and Narayanamoorthy (this issue) argue and provide evidence that SEC pressure culminating in the issuance of SAB 102 in July 2001: (1) caused banks to record allowances for loan losses that were more associated with historical loan charge-offs and less associated with current non-accrual loans; (2) primarily affected large and strong banks; and (3) caused allowances for loan losses to be more (less) informative of future loan charge-offs for strong (weak) banks. We argue and provide evidence that the results the authors ascribe to SAB 102 are primarily explained by consumer loan charge-offs dominating banks’ loan charge-offs and, thus, allowances for loan losses in the post-SAB 102/pre-financial crisis period. This period coincided with a real estate and general macroeconomic boom in which other loan types experienced very low charge-offs.

We examine the probability of exit for different types of investors in the syndicated loan market, as well as how the entry and exit of different types of investors is associated with changes in loan characteristics. Nonbanks, particularly CLOs, closed-end funds, and mutual funds, are more likely than bank lenders to exit the syndicate rather than to participate in the renegotiated loan. For mutual funds, greater net fund outflows imply a greater likelihood of exit, and this finding is consistent with nonbank lending creating greater systemic risk (Stein 2013). For most nonbanks, the likelihood of an exit increases if the financial condition of the borrower improves and the potential for higher spreads wanes. Controlling for borrower risk, the addition of most nonbank institutions, in contrast to banks, is accompanied by an increase in loan spreads , but no significant increase in the number or tightness of covenants.

This research paper aims to analyze the credit risk involved in peer-to-peer (P2P) lending system of “LendingClub” Company. The P2P system allows investors to get significantly higher return on investment as compared to bank deposit, but it comes with a risk of the loan and interest not being repaid. Ensemble machine learning algorithms and preprocessing techniques are used to explore, analyze and determine the factors which play crucial role in predicting the credit risk involved in “LendingClub” publicly available 2013–2015 loan applications dataset. A loan is considered “good” if it's repaid with interest and on time. The algorithms are optimized to favor the potential good loans whilst identifying defaults or risky credits.

The evidence on demand for sharia-compliant financial services is mixed. On the one hand, IFC-funded studies showed relatively high demand, while results from Findex showed that financial exclusion due to religious reasons was minimal (Demirguc-Kunt, Klapper, and Randall 2013). One of the reasons behind these contradictory messages could be the survey instruments themselves: perhaps the issue is not so much what people want but how they were asked. For example, if you ask participants why they do not use borrowing or savings products, their responses may include ‘they are too expensive’ or ‘they are too far away’ even if the real reason is something more nuanced, such as intra-household bargaining issues. Similarly, if you ask Muslims if they prefer an interest-bearing loan or a noninterest-bearing loan, it might not be surprising that they would choose the noninterest-bearing loan, perhaps because they want to demonstrate piety, or maybe because they assume it means a no-cost loan.

The aim of this article is to test the relationship among organizational architecture, joint liabilities contracts, and loan conditions. Based on a sample of 135 MFIs rated between 2003 and 2008, the study shows that solidarity lending and a decentralized credit decision have no significant influence on loan conditions. Being a village bank lender is significantly associated with higher interest rates charged, higher outreach, lower depth of outreach, and higher transaction costs. Results seem to highlight the existence of a trade-off between outreach and the average loan size per borrower when MFIs decentralize credit decisions or establish joint liability contracts.

I analyze optimal loan modification schemes in a stochastic home price and stochastic interest-rate environment. Lenders maximize loan values by managing the borrower’s option to default on the loan and prepayment option. Given negative equity, controlling for the borrower’s ability to pay, rate reductions and maturity extensions result in a higher probability of redefault by homeowners even after modification of their loans. In contrast, loan write-downs (the Principal Principle), not a favored recipe, are value maximizing for the lender. A shared-appreciation mortgage enhances the ability to pay, mitigates adverse selection, and reduces the present value of expected deadweight foreclosure costs.

Our country generally has voluntarily buys custom of the room, but house price actually non- each individual people all can bear its amount, therefore all relies on the mortgage loan which the financial organ provides. However the financial organ after the loaning out fund, will be able to face the loan person in also the funds behavior irregularity, will break a contract the situation influence to be biggest, further inquired into its influence factor has its necessity. Breaks a contract influence factor the situation to be possible to differentiate for the interest rate with the non- interest rate two aspects, tended to by the recent years market rate undulation relaxes the tendency to look, analysis the non- interest rate influence factor appeared is important. This research directly embarks from the loan characteristic, the analysis contains the loan amount, the loan interest rate, period of payments and so on three variables to break a contract influence the situation, and returns (Logistic Regression , LR) the model makes the real diagnosis analysis, the real diagnosis result also demonstrated the non- interest rate influence factor reveals the influence to the loan person irregular also funds behavior existence.

This paper studies loan conditions when firms switch banks. Recent theoretical work on bank–firm relationships motivates our matching models. The dynamic cycle of the loan rate that we uncover is as follows: a loan granted by a new (outside) bank carries a loan rate that is significantly lower than the rates on comparable new loans from the firm's current (inside) banks. The new bank initially decreases the loan rate further but eventually ratchets it up sharply. Other loan conditions follow a similar economically relevant pattern. This bank strategy is consistent with the existence of hold-up costs in bank–firm relationships.

As Ergungor (2001) indicated, some critical problems still exist in the valuation of bank loan commitments. One of them is the Material Adverse Change (“MAC”) clause as a contractual discretion that may be exercised by banks. We explain how the MAC clause can affect the value of bank loan commitments in the competitive loan market.

The article analyzes effects of borrower interest rates and student lender subsidies on federally guaranteed student loan volumes from 1988 to 1994 and from 1996 to 2006. Some have argued that lender subsidy cuts would reduce loan supply or cause lenders to exit the student loan market. If lenders get economic rents due to overly generous subsidies, a simple model of the student loan market suggests that small changes in subsidy levels should not affect loan supply. Empirical results based on a variety of generalized method of moments panel estimators suggest that evidence of links between higher special allowance payment margins and higher loan volumes is weak or inconclusive for both the 1988–94 and 1996–2006 periods. Subsidy reductions, according to this analysis, had no discernable effect on student loan volumes. Results also suggest that higher real borrower interest rates reduce student loan volumes for public colleges and universities. Changes in the federal student loan program in the wake of the credit crunch that began in August 2007 are briefly discussed, along with current proposals to shift all new loans to a direct lending program. © 2010 American Education Finance Association

In a roll-up mortgage, the borrower receives a loan in the form of a lump sum. The loan is rolled up with interest until the borrower dies, sells the house, or moves into long-term care permanently. The house is sold at that time, and the proceeds are used to repay the loan and interest. Most roll-up mortgages are sold with a no-negative-equity guarantee (NNEG), which caps the redemption amount at the lesser of the face amount of the loan and the sale proceeds. The core of this study is to develop a framework for pricing and managing the risks of the NNEG.

Notwithstanding his exhaustive treatment of alternative loan subsidy valuation methods, Jennergren entertains only one type of loan structure in his paper, to wit, bond-type arrangements that entail only interest payments during the life of the debt and the repayment of the entire principal at maturity. The effects of this loan structure specificity are illustrated in the context of Jennergren's dual interpretations of his first loan subsidy valuation method. It is shown that, absent a restriction that the loan must satisfy, the two interpretations result in different valuations. This cautionary note argues that future research must develop loan subsidy valuation methods that are loan structure robust by addressing the more fundamental issue that remains unresolved, namely, how to calculate the amount of the unsubsidized loan that is replaced by the subsidized loan.

This paper examines the secondary market for loan sales and, in particular, loan contract design as a mechanism to resolve informational issues in loan sales and associated costs and benefits. Using loan-level data, we find that sold loans contain additional covenants and more restrictive net worth covenants, particularly when agency and informational problems are more severe. Why do borrowers agree to incur the additional costs associated with loan sales? Our evidence suggests that these borrowers benefit through increased private debt availability. Further, loan sales go hand in hand with more durable lending relationships, suggesting that risk management aids continued relationship lending. The Author 2008. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: journals.permissions@oxfordjournals.org, Oxford University Press.

We provide an empirical support for theories of lender specialization using the recently developed market for Debtor-in-Possession (DIP) financing. The legal environment in which DIP financing operates represents a natural laboratory for testing determinants of lending specialization (e.g. lender choice). We find that the choice of lender is not driven by credit risk, but by information considerations and that this lending specialization has loan pricing effects. In short, banks (non-bank lenders) lend to more (less) transparent firms and at lower (higher) loan spreads. Our results are consistent with the interpretation that banks provide important and useful services.

The notion of loan syndication is founded on the need for two or more banks to jointly lend money to a large borrower. Thus, the loan amount is beyond the funding capacity of one bank. Besides, the loan may be unusually risky or have multiple components such that no bank may be willing or able to grant it all by itself. However, a particular consideration—credit risk, how to correctly anticipate, analyze, and mitigate it—holds sway. It remains constant and influences the disposition of both the lead and participating banks. The lead bank arranges the syndicate of banks, ostensibly to mitigate the risk. Each bank analyzes the credit independent of the others before signing up for it. That assures comfort from collective diligence and a sense of responsibility of the participating banks. This chapter evaluates the conduct and process of loan syndication, underscoring its risk control significance in emerging economies.

This chapter examines credit structuring possibilities for banks in emerging markets. A clear understanding of loan structure is critical for successful loan utilization and repayment by borrowers. In most cases, delinquent loans have structural problems. At the outset when a credit request is being appraised or granted, a bank often contends with the task of devising an arrangement that will ensure that utilization and repayment of the loan will be in accordance with terms agreed with the borrower. Inappropriate credit structure portends risk of loan default because of possible conflict with the borrowing cause. Unfortunately, borrowers tend to concern themselves with fulfilling loan conditions and seem less bothered about loan structure. Yet the structure is as important as conditions of the loan in mitigating default risk. Often this fact begs for recognition by borrowers and lending officers alike. It behooves credit analysts to educate borrowers on appropriate and applicable loan structure.

In a non-recourse collateralized loan agreement, the lender's recovery is only limited to the market value of the asset as a collateral. In practice, a loan lender can protect himself against any loss by introducing either a margin requirement or a callable feature in the loan contract. In this respect, it is natural to ask: (1) which one of the two mentioned features should be more preferable to the loan manager; (2) how the borrower reacts towards either one of these two features. In the present work, we address these issues by solving explicitly the respective valuation problems of collateralized lending with margin requirement and callable feature under the Black-Scholes model. For the callable feature, we also provide systematic discussions on (1) how to identify whether the smooth-fit condition holds or fails at the optimal stopping boundaries of the associated Dynkin game, and (2) how to solve it when the smooth-fit condition fails at one or both boundaries. We finally utilize these explicit solutions to conduct detailed comparative analysis.

Personal loans can be obtained by borrowers from very different types of lending institution. The most common are a traditional loan institutions, payday lenders, or a Peer to Peer (P2P) lending brokers. P2P lending companies do not loan the money directly. They link the borrower to a lender and provide the lender with the borrower’s income which is not usually verified. The P2P lenders collects fees based on the transaction and financially benefits from a higher number of introductions of borrowers and lenders. P2P lending is becoming more popular among borrowers because the pay highest interest rates are much lower than payday lenders and loans require less verification of income and assets than traditional loan institutions. A higher reported income with P2P lenders can result in a larger loan for the borrower and thus more profit and fees generated for the P2P lender. If the loan defaults due to an overstated or fraudulently reported income by the borrow, the P2P lender does not suffer, it is the lender that was matched to the borrower by the P2P lender that will incur the financial loss. This paper focuses on proposing a data science approach to detecting loan applicants that provide fraudulent income data to P2P lenders. The data obtained for this study contained 887,379 observations and 74 variables of loan applicants from the P2P loan company, Lending Club. The initial observations of this data showed that unverified loans make up 23% of the defaulted loans while verified and source verified loans made up about 77%. Described within this paper is how the data set for analysis was obtained and prepared for analysis, the initial findings, the proposed data science approach to fully analyzing this data, and the significance of the lending industry, both traditional and P2P, with a method of detecting fraudulent income reported on loan applications. Models generated from this analysis could be incorporated by lenders into their applications along with research in this area should improve the P2P lending industry by increasing the detection of fraudulent income reported on loan applications.

This paper shows that discrete and vastly different loan rates offered by different types of financial firms constitute, in fact, an elaborate mechanism that makes borrowers tell the truth regarding their ability to pay back loan principal and interest. Suppose that once a borrower fails to pay back a loan to a bank, he cannot borrow from any banks again and must contact higher-interest charging credit finance companies to get a new loan. This creates a well-defined incentive for borrowers: pay back and remain in the banks' loan market vs. do not pay back and move to, say, credit finance companies' loan market in which a higher loan rate is charged. This mechanism does not require the financial firms to verify even if the borrower declares bankruptcy, and therefore is more efficient than a standard debt contract a la Townsend (1979) in terms of verification cost. As the interest rates offered by different types of financial firms should be well aligned in order to prevent the deception of borrowers, we can also analyze how many different types of financial firms, that is, how many discrete and different loan rates, can co-exist in the economy.

Loan agreement assumes a crucial role in defining credit structure. Explicitly defining the terms and conditions of a credit facility consolidates the loan agreement. A critical aspect of loan agreement is the inclusion of operating covenants. Covenants establish acts that the bank and borrower should, or should not, perform. This ensures that neither the bank nor borrower can willfully truncate a loan. In effect, covenants strengthen loan agreements. Besides covenants, a typical loan agreement contains specific conditions precedent to drawdown, representations and warranties, and events of default. These slippery clauses of a loan agreement often cause relationship friction between a bank and borrower. This chapter examines critically how banks enforce the slippery issues of corporate loan structure. It also examines how borrowers tackle banks on the same front. Literature search, analysis, and discussion shed light on the basis and probable conflicts of lending and borrowing money on those slippery terms.

Loan recovery is the terminal action that a bank may want to take in a failed credit relationship. It starts when remedial measures taken to revive a delinquent loan prove unsuccessful. The basis of loan recovery is that a bank cannot afford to carelessly lose a risk asset. Bank management is accountable to its supervising board for avoidable loan losses. Unfortunately, it would appear that loan defaulters in emerging markets do not seem to appreciate this fact when they face the risk of recovery actions—as in foreclosures. This chapter investigates why they would want to thwart even innocuous actions to recover their bad loans. One could opine that loan recovery should be conducted with a human face and the milk of kindness. Otherwise it might prove to be an arduous task and quite frustrating. Doing it well helps to check the intrigues that often characterize taking actions and the responses to them.

A decrease in the reputation of a loan syndicate’s lead arranger, caused by a regulatory enforcement action for non-compliance with laws and regulations, disincentivizes potential syndicate participants from co-financing the loan. We formally argue that in such cases, the lead arranger must increase his share of the loan in order to make the loan sufficiently attractive to potential participants. We provide strong empirical evidence to support our theoretical argument, using the full sample of enforcement actions enacted on U.S. banks from 2000 through 2010 as well as syndicated loan-level data.

Most loans that have high default probabilities have unique characteristic features. First they deteriorate to substandard performance. Then they degenerate to doubtful accounts before ultimately adding to the lost credits statistic. Some of the obvious early warning signs of loan default could be inferred from actions of the borrower, changing attributes of the loan, and sometimes curious inquiries from third parties. There is no uniform pattern of early warning signs applicable to all credit facilities and borrowers. How can lending officers anticipate the early warning signs of loan default? When identified, what appropriate steps could account officers take to check the possible default threat to the loan? While answering these questions, this chapter pinpoints the remote causes of loan default in emerging markets. It proffers preemptive actions that lending officers could take to forestall loan default, and the implications of early warning signs for credit risk management based on true lessons of experience.

In the syndicated loan market, borrowers and syndicate arrangers sometimes employ contractual restrictions that influence a loan’s liquidity. We analyze two types of constraints on loan resales: (1) prior consent constraints implemented by the borrower or the syndicate’s lead arranger and (2) a minimum denomination requirement for loan sales. We hypothesize that constraints could be mechanisms for fostering relationships and/or facilitating the resolution of financial distress and find some support for each notion. We find that resale constraints are more likely when borrowers are small and have relatively poor credit ratings. We also find that loans with any type of constraint have higher all-in-spreads and are more likely to be secured than unconstrained loans and that the marginal cost of constraining liquidity is relatively high.

This study designs optimal loan quality models for a two-period loan market. Specifically, this work explores whether enterprise and individual provide the guarantees for loan credit and whether financial institution repackages the rights of the loan creditor to issue financial asset securitization products to assess the upper (maximum net loan profit) and lower (specific net loan profit) bounds of optimal loan quality. This proposed model can facilitate optimal decision making regarding loan qualities based on financial institution operating performance.

Loan provision is associated with a credit risk. Banks assess the creditworthiness of potential borrowers to lower a credit risk. Creditworthiness assessment is carried out by credit scoring methods. Most of these methods classify individuals into two categories: ‘good’ or ‘bad’ creditworthiness. Decision support system for loan granting based on these methods fail to differentiate the loan price for private borrowers. In practice, banks mostly grant loans applying decision support system that makes no price differentiation for individuals according to their private information. A bank may differentiate a loan price to attract a specific group of borrowers. In this paper, loan price differentiation impact on the behaviour of banks potential borrowers in the market for consumer lending is analyzed. A concept of a decision support system for loan granting based on the use of a continuous loan price function of the borrower's creditworthiness rating is proposed. Competition between banks using different loan price function of creditworthiness rating is analyzed. Borrowers with a low credit risk are interested in switching to a bank that reflects more private information in a loan price. Application of the proposed system provides a competitive advantage for a bank to attract borrowers with a low credit risk. It is shown that fuzzy logic has a potential to be applied in a decision support system based on continuous price function for granting a credit.

When a loan is sold, it goes to a lower-cost financing source than its originator. Yet, lending markets are less than perfectly competitive. Despite the lower funding cost, therefore, the loan price is not necessarily more favorable to the borrower. However, corporate borrowers are averse to the participation of their loans to other lenders because of the complexity of dealing with multiple banks and the potential information costs of the sale announcement. Consequently, I conjecture that the borrower extracts a price concession in exchange for allowing the bank to sell participations in the loan. Using a hand-matched dataset of loans, borrowers, and lenders, I find that the average yield spread on loans originated by active loan sellers is about 20 basis points lower than the average spread on loans originated by moderate loan sellers. Copyright 2006, Oxford University Press.

This paper studies the bank's lending decision, based on three observed phenomena: banks earn substantial profits from off-balance sheet activities and services, which they take into account in their lending decisions. Secondly, the critical point in the customer relation is the loan decision: the probability of the customer staying with the bank is a function of the loan extended each time one is applied for. Third, what is at stake in the loan decision is the expected value of the entire customer relation, which is the probability times the present value of expected future profits. The bank is a maximizer of this expected present value, while making decisions on individual loan applications. It is shown that the bank is in a corner solution with respect to its good customers, and other customers often have an incentive to get to a corner. Therefore, corner solutions may be the rule rather than the exception in the bank's customer relations, and there is no mechanism making the bank indifferent, at the margin, between lending to different customers. It can be optimal to extend loans to (present and expected future) good customers at an interest rate loss. A rationed customer with a concave enough probability function can receive a larger loan by asking for less. Loyalty increases the customer's value to the bank but improves its loan terms only if the customer makes it conditional on the loan extended.