Monitoring in Originate-to-Distribute Lending: Reputation versus Skin in the Game

2021 ◽  
Author(s):  
Andrew Winton ◽  
Vijay Yerramilli
Keyword(s):  
Loan Default ◽  
Loan Sales ◽  
Reputation Mechanism ◽  
Loan Demand ◽  
Current Monitoring

Abstract Banks face liquidity and capital pressures that favor selling off the loans they originate, but loan sales undermine their monitoring incentives. A bank’s loan default history is a noisy measure of its past monitoring choices, which can serve as a reputation mechanism to incentivize current monitoring. In equilibrium, higher reputation banks monitor (weakly) more intensively; if retention is credible, they generally retain less of the loans they originate. Monitoring is difficult to sustain in periods with uncommonly large spikes in loan demand (“booms”), especially for low-reputation banks, which are more likely to accommodate boom demand and forgo monitoring.

Accurate and Adaptive Reputation Mechanism for P2P File Sharing Network

2011 ◽  
Vol 22 (10) ◽  
pp. 2346-2357 ◽  
Author(s):  
Miao WANG ◽  
Fei TAO ◽  
Yu-Jun ZHANG ◽  
Guo-Jie LI
Keyword(s):  
File Sharing ◽  
Reputation Mechanism ◽  
P2p File Sharing

Biases in estimating bank loan default probabilities

2005 ◽  
Vol 7 (4) ◽  
pp. 75-102 ◽  
Author(s):  
Thomas Mählmann
Keyword(s):  
Bank Loan ◽  
Loan Default

Fuzzy control of coagulation reaction through streaming current monitoring

1997 ◽  
Vol 36 (4) ◽  
pp. 127-134 ◽  
Author(s):  
J. C. Liu ◽  
M. D. Wu
Keyword(s):  
Fuzzy Logic Controller ◽  
Treatment Plant ◽  
Control Process ◽  
Water Treatment Plant ◽  
Critical Parameters ◽  
Flow Mode ◽  
Raw Water ◽  
Streaming Current ◽  
On Line ◽  
Current Monitoring

A fuzzy logic controller (FLC) incorporating the streaming current detector (SDC) was utilized in the automatic control of the coagulation reaction. Kaolinite was used to prepare synthetic raw water, and ferric chloride was used as the coagulant. The control set point was decided at a streaming current (SC) of −0.05 and pH of 8.0 from jar tests, zeta potential and streaming current measurements. A bench-scale water treatment plant with rapid mix, flocculation, and sedimentation units, operated in a continuous-flow mode, was utilized to simulate the reaction. Two critical parameters affecting the coagulation reaction, i.e., pH and streaming current, were chosen as process outputs; while coagulant dose and base dose were chosen as control process inputs. They were on-line monitored and transduced through a FLC. With raw water of initial turbidity of 110 NTU, residual turbidity of lower than 10 NTU before filtration was obtained. Results show that this combination functions satisfactorily for coagulation control.

EXPRESS: How Does Bonus Payment Affect the Demand for Auto Loans and Their Delinquency?

2021 ◽  
pp. 002224372110092
Author(s):  
Zhenling Jiang ◽  
Dennis J. Zhang ◽  
Tat Chan
Keyword(s):  
Consumer Products ◽  
Data Set ◽  
Financial Decisions ◽  
Consumer Panel ◽  
Loan Demand ◽  
Bonus Payments ◽  
Base Salary ◽  
Long Term Impact ◽  
Shed Light

This paper studies how receiving a bonus changes the consumers’ demand for auto loans and the risk of future delinquency. Unlike traditional consumer products, auto loans have a long-term impact on consumers’ financial state because of the monthly payment obligation. Using a large consumer panel data set of credit and employment information, the authors find that receiving a bonus increases auto loan demand by 21 percent. These loans, however, are associated with higher risk, as the delinquency rate increases by 18.5 −31.4 percent depending on different measures. In contrast, an increase in consumers’ base salary will increase the demand for auto loans but not the delinquency. By comparing consumers with bonuses with those without bonuses, the authors find that bonus payments lead to both demand expansion and demand shifting on auto loans. The empirical findings help shed light on how consumers make financial decisions and have important implications for financial institutions on when demand for auto loans and the associated risk arise.

scholarly journals Technology Used to Recognize Activities of Daily Living in Community-Dwelling Older Adults

2020 ◽  
Vol 18 (1) ◽  
pp. 163
Author(s):  
Nicola Camp ◽  
Martin Lewis ◽  
Kirsty Hunter ◽  
Julie Johnston ◽  
Massimiliano Zecca ◽  
...  
Keyword(s):  
Older Adults ◽  
Activities Of Daily Living ◽  
Daily Living ◽  
Wearable Technology ◽  
Community Dwelling ◽  
Use Of Technology ◽  
Key Stakeholders ◽  
Environmental Technologies ◽  
Current Monitoring ◽  
Future Work

The use of technology has been suggested as a means of allowing continued autonomous living for older adults, while reducing the burden on caregivers and aiding decision-making relating to healthcare. However, more clarity is needed relating to the Activities of Daily Living (ADL) recognised, and the types of technology included within current monitoring approaches. This review aims to identify these differences and highlight the current gaps in these systems. A scoping review was conducted in accordance with PRISMA-ScR, drawing on PubMed, Scopus, and Google Scholar. Articles and commercially available systems were selected if they focused on ADL recognition of older adults within their home environment. Thirty-nine ADL recognition systems were identified, nine of which were commercially available. One system incorporated environmental and wearable technology, two used only wearable technology, and 34 used only environmental technologies. Overall, 14 ADL were identified but there was variation in the specific ADL recognised by each system. Although the use of technology to monitor ADL of older adults is becoming more prevalent, there is a large variation in the ADL recognised, how ADL are defined, and the types of technology used within monitoring systems. Key stakeholders, such as older adults and healthcare workers, should be consulted in future work to ensure that future developments are functional and useable.

A deep metric learning approach for weakly supervised loan default prediction1

2021 ◽  
pp. 1-13
Author(s):  
Kai Zhuang ◽  
Sen Wu ◽  
Xiaonan Gao
Keyword(s):  
Default Risk ◽  
Metric Learning ◽  
Real Life ◽  
Risk Scores ◽  
Loan Default ◽  
Default Prediction ◽  
Heterogeneous Features ◽  
Deep Metric Learning ◽  
Weakly Supervised ◽  
Decision Boundaries

To deal with the systematic risk of financial institutions and the rapid increasing of loan applications, it is becoming extremely important to automatically predict the default probability of a loan. However, this task is non-trivial due to the insufficient default samples, hard decision boundaries and numerous heterogeneous features. To the best of our knowledge, existing related researches fail in handling these three difficulties simultaneously. In this paper, we propose a weakly supervised loan default prediction model WEAKLOAN that systematically solves all these challenges based on deep metric learning. WEAKLOAN is composed of three key modules which are used for encoding loan features, learning evaluation metrics and calculating default risk scores. By doing so, WEAKLOAN can not only extract the features of a loan itself, but also model the hidden relationships in loan pairs. Extensive experiments on real-life datasets show that WEAKLOAN significantly outperforms all compared baselines even though the default loans for training are limited.

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