Model Of Lending Research Articles

Actions speak louder than words: Imputing users’ reputation from transaction history

The choice of market mechanism is key to success for any online marketplace. In recent years, as peer‐to‐peer (P2P) lending has seen phenomenal growth, leading P2P lending platforms have used various market mechanisms and, in some cases, even switched from one mechanism to another, chasing higher market share and overall growth. While Prosper.com, a leading P2P lending platform, has switched from the auction lending model to a fixed price lending model, recent studies show that overall social welfare was higher with the auction lending model. While the auction lending model gives more power to the lenders, the success of the auction lending model hinges on the accuracy of lenders’ assessment of the credit risk of the borrowers. Building on extant literature and in support of the auction lending model to increase social welfare, we design an artifact to dynamically estimate borrower reputation to help the lenders and improve the allocative efficiency in P2P lending markets. We posit that borrowers’ reputation built on transactional data, readily available on P2P lending platforms, represents the collective perception of the lenders about the borrowers. We propose a dynamic latent class model of reputation and use the latent instrumental variable approach to deal with endogeneity. We test our artifact using real‐world P2P lending data. We show that accounting for reputation improves the model's explanatory power and provides a way to empirically model the evolution and impact of reputation in online platforms where repeated transactions are performed.

Mark-to-Market, Loan Retention, and Loan Origination

ABSTRACTWe study the effects of mark-to-market accounting (MTM) for banks following the originate-to-distribute lending model. Banks have expertise in originating loans, but it is costly for them to retain the loans on their books. We study how the accounting measurement of the retained loans affects the banks’ origination and retention decisions. We show that, relative to historic cost accounting (HC), MTM has three consequences. First, it improves the accuracy of loan measurement ex post. Second, it forces banks to retain more risk exposure on their own books. Finally, it can reduce ex ante origination efforts and lower the average quality of loans in the economy.JEL Classifications: G01; G21; G30; M41.

A New Credit and Loan Lending Strategy and Credit in Banking Systems: An Evolutionary Game Theory Approach

In this paper, authors offer one novel mathematical model of credit lending to customers based on evolutionary game theory, and the model presents an efficient and realistic approach. The purpose of the article is to examine the evolutionary game between banks and customers for granting facilities and credit. Authors assumed that customers are divided into two types. The first type of customers includes individuals or small and medium enterprises (SME), applying for microloans from the bank. The second type of customers includes corporate banking or large enterprises, applying for large loans from the bank. The relationship between the bank and the customers is a double-sided problem. Banks and customers may trust each other or want to behave opportunistically. The results show that the game has two equilibriums, and the optimal equilibrium, which is the best-case scenario, occurs when customers and bank players tending to keep “honest” and to “credit,” respectively. Authors used the evolutionary stable strategy to express the parameters that affect these interactions, and by adjusting some of these parameters, authors move the equilibrium towards the optimal solution of the game. Also, by adjusting these parameters, banks can gain more profitability.

Balancing liquidity and returns through interbank markets: Endogenous interest rates and network structures

In this article, we develop a model of interbank lending based on liquidity and return on equity considerations of homogeneous banks. We derive the reservation prices of interbank lending and its properties before exploring how, because of an idiosyncratic liquidity shock, banks engage in bilateral lending to form an interbank network. We establish that the resulting networks exhibit realistic properties, including a core-periphery structure. Banks in the core and the periphery of this network differ not only in the amounts of interbank lending and borrowing but also in the interest rates applied to their transactions.

The Contribution of Microlending Models to the growth of Micro and Small Entrepreneurs

Microfinance has been providing microcredit services to beneficiaries through group, individual, association loans, community banking, and corporative microlending models. The sector has been fostering the growth of business in the economy by enabling entrepreneurs to participate in a variety of income-generating activities. However, there is limited information on the extent the microlending models contribute to the performance of micro and small enterprises. Therefore, the current research primarily intended to assess the contribution of Microfinance Bank's microlending models to the growth of micro and small entrepreneurs in Tanzania. This was a mixed approach study which adopted explanatory design and used convenience and purposive sampling techniques to obtain 179 respondents. Questionnaires, document review and interviews were used to collect both primary and secondary data. Statistical Packages for Social Science were used to quantitatively analyse field data using descriptive statistics (SPSS). MAXQDA10 was also used to conduct a content analysis of qualitative data. 
 The findings of the study show that microlending models of microfinance banks contribute to the growth of micro and small entrepreneurs. It is thus recommended that microfinance banks should use appropriate lending model that is compatible with the nature of micro and small firms for growth and sustainable operations as they constitute the larger portion of lending clientele.

Network Formation and Financial Inclusion in P2P Lending: A Computational Model

What characteristics of fintech lending platforms improve access to funding and increase financial inclusion? We build a computational model of platform lending that is used to study the endogenous loan network formation process on the platform. Given the multidimensional nature of financial inclusion, we address what factors influence the number of loans, the level of investment/debt, and how those relate to the distribution of investment/debt across agents. We find that platform scale and SME reach are essential in determining the number of loans on the platform. However, the willingness to accept risks is the main driver behind the value of those loans. We also find that increased platform scale, high-risk thresholds, and low-interest rates lead to more evenly distributed investments. Moreover, we find that large platforms help increase diversity and lead to a more evenly distributed power among peers. We conclude that digital platforms increase financial inclusion, helping to foster investment and achieve a more egalitarian allocation of resources. These results can guide new theory development about the impact of P2P lending on inequality as well as help platforms to promote financial inclusion.

Nonbanks and Mortgage Securitization

This article reviews the dramatic growth of nonbank mortgage lending after the Global Financial Crisis, especially to borrowers with lower credit scores, and the related importance of mortgage-backed securitization. Our literature review suggests that the existing theoretical and empirical work on securitization is more relevant to bank than to nonbank lenders, thus leaving outstanding questions as to why nonbank market shares have increased to their current levels and how best to structure nonbank oversight. To highlight key differences in the mortgage-lending incentives of banks and nonbanks, we build a simple theoretical model of bank versus nonbank mortgage lending and use it to generate and test empirical hypotheses. We find, in particular, that loans issued by nonbanks are more likely to prepay early than loans issued by banks, the difference not explainable by nonbank borrowers prepaying more rationally. Using regulatory filings from nonbanks that are typically unavailable to academic researchers, we examine the balance sheets and liquidity and capital positions of large Ginnie Mae nonbank servicers, which face and pose more risk in the current mortgage system. We find that on average these servicers have reasonable liquidity and capital positions relative to standard regulatory thresholds, particularly in 2022:Q1 after a few quarters of elevated profits. However, some large Ginnie Mae servicers appear to have inadequate capital, as gauged by risk-based capital measures. If defaults rise on a large scale, the liquidity and capital positions of these servicers may amplify the disruption in the mortgage and housing markets.

What are reference rates for?

Reference rates like LIBOR were designed to reflect banks’ full cost of funds, including credit premia. With publication of LIBOR expected to cease soon, regulators have recommended a shift to risk-free reference rates. To study the implications of this shift, this paper develops a tractable model of bank lending and hedging in which the design of reference rates shapes the allocation of risk in equilibrium. Reference rates that capture credit risk, but are sufficiently free from manipulation, could improve welfare relative to risk-free reference rates. It remains an open question whether such reference rates can be constructed.

Evergreening

We develop a simple model of relationship lending where lenders have incentives for evergreening loans by offering better terms to less productive and more indebted firms. We detect such lending behavior using loan-level supervisory data for the United States. Low-capitalized banks systematically distort firms’ risk assessments to window-dress their balance sheets. To avoid further reductions in their capital ratios, such banks extend relatively more credit to underreported borrowers. We incorporate the theoretical mechanism into a dynamic heterogeneous-firm model to show that evergreening affects aggregate outcomes, resulting in lower interest rates, higher levels of debt, and lower productivity.

A Deep Learning Approach to Dynamic Interbank Network Link Prediction

Lehman Brothers’ failure in 2008 demonstrated the importance of understanding interconnectedness in interbank networks. The interbank market plays a significant role in facilitating market liquidity and providing short-term funding for each other to smooth liquidity shortages. Knowing the trading relationship could also help understand risk contagion among banks. Therefore, future lending relationship prediction is important to understand the dynamic evolution of interbank networks. To achieve the goal, we apply a deep learning framework model of interbank lending to an electronic trading interbank network for temporal trading relationship prediction. There are two important components of the model, which are the Graph convolutional network (GCN) and the Long short-term memory (LSTM) model. The GCN and LSTM components together capture the spatial–temporal information of the dynamic network snapshots. Compared with the Discrete autoregressive model and Dynamic latent space model, our proposed model achieves better performance in both the precrisis and the crisis period.

P2P Lending’s Credit Risk in Vietnam: Determinants and Discussions

The recent upsurge in information technology has created the automation of processes and the reorganization of the chain of financial business area. In a new trend technology application, peer-to-peer lending (P2P) model, a service that connects individuals to borrow and lend money is adopted by the new online platform along with advanced technology systems and many different purposes. Hence, this research explores the situation and draws on possible potential causes of peer-to-peer lending risk. The questionnaire surveys were launched in December of 2021 with arepresentative sample consisted of 205 participants, including the majority of Vietnamese who had stable incomes in major cities of domestic nation and some others abroad as well as in-depth interviews also took place with persons who have a clear understanding of peer-to-peer lending and its risks. Based on findings, some recommendations were proposed to reduce risk of a new form of credit provision.

Entrepreneurial activities and women empowerment in rural India between microfinance and social capital

AbstractHuman development cannot occur without women's empowerment. Particularly in marginalized contexts lacking equal treatment between women and men, women's empowerment is conditioned upon their control of financial resources and activities outside their households' tasks, such as entrepreneurial activities. Such contexts have been experiencing the escalation of microfinance schemes of which women have been the main target group. While scholars generally agree on the opportunities that microcredit represents for microentrepreneurial activities, its effective impact on women entrepreneurship, and hence their empowerment, show contrasting results. We aim to contribute to this debate by conducting in‐depth semi‐structured interviews with female members of the group lending model of the microfinance institutions in Uttar Pradesh, northern India. The thematic questions of the interviews address, particularly, the social resources (social capital) and economic conditions underpinning the process of empowerment among women's members of peer‐group lending schemes. We find that social capital plays a crucial role for peer‐group lending to be successful in accessing financial resources and loan repayment. However, we detect the presence of empowerment only where a woman uses the outcome of these social capital resources to pursue her entrepreneurial activities and self‐determined goals.

Disproportionate Safety Preference and the Innovation of Fintech Shadow Banking

There has been a dramatic shift in financial intermediation in the last 10 - 15 years from traditional banks to shadow banks (non-depository institutions that rely on originate-to-distribute lending model). We link this rise to an emerging literature that shows that certain and uncertain utility functions are different with a disproportionate preference for certainty. We show that such a preference plays a role in diverting lending away from the traditional banking model to the shadow banking model. Furthermore, a low interest-rate environment emerges as the key contributing factor in the dramatic rise of shadow banking.

РИЗИКИ ІПОТЕЧНОГО КРЕДИТУВАННЯ ТА ШЛЯХИ ЇХ МІНІМІЗАЦІЇ В УМОВАХ ФІНАНСОВОЇ НЕСАБІЛЬНОСТІ

The article provides the author's definition of the terms "mortgage loan" and "mortgage lending risk". The classification of mortgage lending risks according to risks related mainly to real estate (systemic, non-systematic, random) and risks related to the capital market (banking risks) is given. The definition of each risk highlighted in the author's risk classification of mortgage lending risks is given. The distribution of risks between participants in mortgage relations (creditor, borrower, issuer of mortgage securities, investor) is presented, depending on the one- and two-level model of mortgage lending. The correlation between the level of risks and the cost of credit services is indicated. A detailed analysis of the following risks was carried out: credit risk, market risk, interest rate risk, liquidity risk, currency risk, operational and technological risks, reputational risks and legal risk. Attention is paid to one of the types of credit risk – the risk of early loan repayment, and an example of its minimization is given. It is described how credit risk directly affects the market value and profitability of the mortgage portfolio. The causes of mortgage lending risks are listed and their consequences for all participants in credit relations are listed. The close relationship between the macroeconomic situation in the country and the effectiveness of mortgage lending is substantiated using the example of real estate price fluctuations. The importance of the correct assessment of collateral property when concluding a credit agreement is explained. The experience of the banks of the leading countries of the world and the leading banks of Ukraine in managing mortgage risks is presented. The methods of risk management given in the classification are considered and proposed, and the ways of improving the effectiveness of the mortgage loan are given. The most adequate monthly payment for a mortgage loan is given as a percentage of the borrower's income to ensure his solvency. Possible scenarios of fraud in the field of mortgage lending are given and recommendations for their prevention are given. Real cases of scams from developers of Ukraine in the context of mortgage lending to the population are given.

Effect of Credit Information Bureau and Appraisal Methods on performance of Commercial Banks in Mwanza Region

This study intended to identify the effect of credit information bureau and appraisal methods on performance of commercial banks in Mwanza Region. Specifically, the study intended to assess the effect of Credit Reference Bureaus’ information sharing on the credit performance of commercial banks, to assess the effect of the credit scoring appraisal method employed on the performance of commercial banks and to assess the effect of non-performing loans on the performances of Commercial Banks in the Mwanza Region. The study was guided by three theories; the theory of asymmetric information, moral hazard theory and multiple - bank lending model. The study adopted a descriptive research design, sampling 105 respondents randomly and purposefully and collected data through questionnaires. The aim is to establish the effect of credit information bureau and appraisal credit scoring methods on the performance of commercial banks in the Mwanza Region. Results revealed that credit information bureau had a positive, strong effect on the credit performance of commercial banks, and this effect is significant in Mwanza region. Non-performing loans had a positive, weak effect on the credit performance of commercial bank and it was not significant. Finally the credit scoring appraisal approach has a negative, weak effect on commercial bank credit performance. The study concluded that credit information bureau has a significant influence on credit performance of commercial banks and therefore should be used to reduce non-performing loans. The study recommended that banks should continue to use the credit reference bureau’s information-sharing services as it enhances their profitability through the reduction of non-performing loans.

The Big Tech Lending Model

The Big Tech Lending Model

Application of credit‐scoring methods in a decision support system of investment for peer‐to‐peer lending

AbstractPeer‐to‐peer lending, as a novel lending model, has challenged investors to make effective investment decisions. Issued loans are grouped into default and nondefault. Therefore, different classification methods can be utilized to predict the status of loans in the future. Our study aim is to propose an investment decision model based on the nondefault loans predicted using three different classifiers, including random forest (RF) that is a multitude of decision trees, support vector machine, and naïve Bayes. In fact, we combine each of these classifiers with the portfolio optimization problem to understand which combination leads to the best portfolio concerning the risk and return. In order to find the best combined model, numerical studies are conducted based on real‐world data. In addition, the performances of these classifiers are evaluated based on different performance measures using the 10‐fold cross‐validation method. The results indicate that combining the RF algorithm and portfolio optimization problem leads to the least level of risk. Moreover, it is concluded that RF is the best classifier in terms of performance analysis.

The repo channel of cross-border lending in the European sovereign debt crisis

I build an equilibrium model of cross-border lending to understand how shocks to repo funding and leverage induce banks to reallocate credit by region and asset type during the European sovereign debt crisis of 2010–2013. A higher collateral risk on lower-rated sovereigns induces banks to fly to liquid collateral. Banks in the periphery sovereign crisis region with worse funding conditions rebalance their loan portfolios toward risky illiquid sovereigns. The European Central Bank’s Long Term Refinancing Operations program stands as a mechanism whereby optimistic banks carried out the successful policy of the ECB to tamp down euro redenomination risk.

Skala i skutki alienacji młodych Polaków na rynku mieszkaniowym

As statistics show, few people can afford to buy an apartment. Many people cannot even afford to rent. On the other hand, the overcrowding rate and the number of adults living permanently with their parents in Poland are among the highest in Europe. Outstanding architects and sociologists have been dealing with housing problems for many years. In many countries, research is conducted showing the importance and essence of the problem, not only in the economic context but also in the psychological, health, and social context that influences the behavior and attitudes of future generations. Access to housing is not only a question of ensuring safety, stability, and shelter; it also affects social relations that constitute the basis of the existence of the individuals and the families. Own house is a collection of meanings, codes, and symbols. It is a product of multi-faceted and multi-generational, interdependent values. Failure to understand these issues eliminates all forms of striving for sustainable development. This article aims to discuss the existing housing problem from diversified perspectives, with particular emphasis on the analysis of the current housing situation among adult Poles. The author will present the topic in some analogy to the condition of the 19th century alienated worker. Based on the conducted research, predictions about the intergenerational effects of the situation disrupting the city's sustainable development in its economic and social layer, having a significant impact on future generations, will also be formulated. Next, the author of the paper presents the results of research conducted from July to October 2020. The study was a part of expert analysis concerning an alternative mortgage lending model for residential purposes, commissioned by the Warsaw Banking Institute Foundation.

Does IT Help? Information Technology in Banking and Entrepreneurship

This paper analyzes the importance of information technology (IT) in banking for entrepreneurship. To guide our empirical analysis, we build a parsimonious model of bank screening and lending that predicts that IT in banking can spur entrepreneurship by making it easier for startups to borrow against collateral. We provide empirical evidence that job creation by young firms is stronger in US counties that are more exposed to ITintensive banks. Consistent with a strengthened collateral lending channel for IT banks, entrepreneurship increases more in IT-exposed counties when house prices rise. In line with the model's implications, IT in banking increases startup activity without diminishing startup quality and it also weakens the importance of geographical distance between borrowers and lenders. These results suggest that banks' IT adoption can increase dynamism and productivity.