Privacy for Loan Applicants Versus Predictive Power for Loan Providers: Is It Possible to Bridge the Gap?

نویسندگان

  • Charlene Jennett
  • Miguel Malheiros
  • Sacha Brostoff
  • M. Angela Sasse
چکیده

Consumers have to trust that financial services will work for, rather than against them. In a recent speech, Mark Hoban (2010) MP, Financial Secretary to the UK Treasury, stated that “We need a financial sector that works for consumers—one that earns their confidence, competes for their services, and keeps them properly informed.” The collection, use, maintenance, and disclosure of consumer information, is an essential part of any financial transaction (MacCarthy and Gellman 2010). However, recent research suggests that more needs to be known about the public’s worries about how their personal information is used and protected (Raab 2004)—and that applies to financial services. This chapter explores consumers’ privacy concerns about information requested on loan applications. Currently, loan applicants have low expectations of privacy— they are expected to: (1) answer all questions, without exception; (2) consent to all terms and conditions (which often includes their data being passed onto third parties); and (3) accept that their credit record will be checked. Based on our interviews and surveys, we argue that it is possible to maintain the efficacy of the loan risk assessment process and respect applicants’ privacy at the same time. In Sect. 3.2, we review existing literature on the perspectives of loan providers and loan applicants, and identify a discrepancy between information that loan providers and loan applicants consider relevant. To explore this discrepancy, we conducted

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Matrix Sequential Hybrid Credit Scorecard Based on Logistic Regression and Clustering

The Basel II Accord pointed out benefits of credit risk management through internal models to estimate Probability of Default (PD). Banks use default predictions to estimate the loan applicants’ PD. However, in practice, PD is not useful and banks applied credit scorecards for their decision making process. Also the competitive pressures in lending industry forced banks to use profit scorecards...

متن کامل

Investigating Loan Applicants’ Perceptions of Alternative Data Items and the Effect of Incentives on Disclosure

Lenders use information about loan applicants to predict whether a person is a good or bad credit risk; however borrowers express reservations about disclosing their personal information. In this paper we describe the design of a study in which we try to identify which data items have bigger privacy costs for individuals and whether it is possible to adjust lenders' data collection procedures i...

متن کامل

Customer Concentration and bank loan contracts: Evidence from the Tehran Stock Exchange

Objective: The variables of customer concentration and bank loan contracts can affect corporate finance activities, and customer concentration may increase corporate returns. Methods: Under this study, the relationship between customer concentration and bank loan contracts have been investigated first and then the moderating effect of financial status variables and accounts payable on relation...

متن کامل

Inside a Lender: A Case Study of the Mortgage Application Process

A lthough fair lending laws mandate that all loan applicants receive equal treatment, all of the evidence reveals wide disparities in origi-nation outcomes between white and minority loan applicants. Some of these differences are attributable to income and wealth differences between minorities and whites. Rigorous statistical analysis, however, continues to find loan denial disparities between ...

متن کامل

Supporting Educational Loan Decision Making Using Neural Network

This study introduces i-Neuro, a decision support system that can assist in loan decision making by educational loan funding institutions. i-Neuro is a predictive system that integrates Neural Network technique, thus can help the management to predict which application to accept or reject. The prediction can be done as Neural Network has trained previous batch of loan application data and store...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2012