Direkt zur Hauptnavigation springen Direkt zum Inhalt springen
info(at)avldigital.remove-this.de
Deutsch English
Letzte Suchanfragen

Ergebnisse für *

Zeige Ergebnisse 1 bis 5 von 5.

  1. The unintended consequences of employer credit check bans for labor markets
    Autor*in: Cortés, Kristle; Glover, Andrew; Tasci, Murat
    Erschienen: 2020
    Verlag:  Federal Research Bank of Kansas City, Kansas City, Mo.

    Zugang:
    Verlag (kostenfrei)
    Verlag (kostenfrei)
    Resolving-System (kostenfrei)

    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signatur:
    VS 359
    Fernleihe:
    keine Fernleihe
    Export in Literaturverwaltung   RIS-Format
      BibTeX-Format
    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    Schriftenreihe: KcFED research working papers ; RWP 20, 04 (July 2020)
    Schlagworte: vacancies; credit score; credit check
    Umfang: 1 Online-Ressource (circa 47 Seiten), Illustrationen
  2. Credit risk analysis for low income earners
    Autor*in: Ntwiga, Davis Bundi
    Erschienen: August 2018
    Verlag:  Kenya Bankers Association, Nairobi

    The low income earners have been excluded from financial services due to their limited ability to access credit as they lack good financial options. Their income is volatile, fluctuating daily and they lack reliable ways to harness the power of their... mehr

    Zugang:
    Verlag (kostenfrei)
    Resolving-System (kostenfrei)

    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signatur:
    DS 792
    Fernleihe:
    keine Fernleihe

     

    The low income earners have been excluded from financial services due to their limited ability to access credit as they lack good financial options. Their income is volatile, fluctuating daily and they lack reliable ways to harness the power of their low incomes. This challenge can be met through use of mobile technology to collect data on the socioeconomic activities of the low income earners at minimum costs. The success of the mobile financial services in Kenya cannot be understated coupled with increase in mobile penetration. A mobile based technology for micro-credit already exists through M-Shwari started in the year 2012 on the M-pesa platform to further increase financial inclusion. This paper pro- poses a decision support system that is mobile based for credit scoring, classification and peer group lending of the low income earners in Kenya. This facility is referred to as Mobile Micro Credit System. We hypothesize that, first, mobile micro-credit lending for low income peer groups is similar to that of the conventional individual lending. Second, credit scores and credit quality levels among low income men and women is the same. Third, a mobile based micro credit can further enhance financial inclusion among the low income earners. A comparison is made between the peer groups and individual customers in terms of their credit scores and credit quality levels. The data for the study was extracted from the financial diaries dataset by Financial Sector Deepening. Customers were clustered in peer groups and as individual households based on gender groupings of men only, women only, or men and women. The credit scoring factors were estimated and resulting data trained the hidden Markov model, the classification technique used. The hidden Markov model emit- ted the credit scores and the credit quality levels of the individual households and the peer groups in which the groups had stronger credit scores and credit quality levels compared to those of the individual households. Peer groups for women only, and those of men and women had superior credit scores when compared to men only peer groups. The optimal peer group size for lending is between four and eight members. The current mobile financial services offer a baseline to implement a mobile micro credit service for the low income earners. This is an incentive for financial services providers to consider providing mobile based micro credit loans to low income earners.

     
    Export in Literaturverwaltung   RIS-Format
      BibTeX-Format
    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    hdl: 10419/249525
    Schriftenreihe: KBA Centre for Research on Financial Markets and Policy working paper series ; WPS, 18, 02 = 24
    Schlagworte: Peer groups; credit score; hidden Markov model; mobile micro credit system; men; women; credit quality and low income earners
    Umfang: 1 Online-Ressource (circa 32 Seiten), Illustrationen
  3. Calibration alternatives to logistic regression and their potential for transferring the dispersion of discriminatory power into uncertainties of probabilities of default
    Autor*in: Wosnitza, Jan Henrik
    Erschienen: [2022]
    Verlag:  Deutsche Bundesbank, Frankfurt am Main

    The transformation of credit scores into probabilities of default plays an important role in credit risk estimation. The linear logistic regression has developed into a standard calibration approach in the banking sector. With the advent of machine... mehr

    Zugang:
    Verlag (kostenfrei)
    Resolving-System (kostenfrei)

    Standort:
    Leibniz-Institut für Wirtschaftsforschung Halle, Bibliothek
    Fernleihe:
    keine Fernleihe

    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signatur:
    DS 12
    Fernleihe:
    keine Fernleihe

     

    The transformation of credit scores into probabilities of default plays an important role in credit risk estimation. The linear logistic regression has developed into a standard calibration approach in the banking sector. With the advent of machine learning techniques in the discriminatory phase of credit risk models, however, the standard calibration approach is currently under scrutiny again. In particular, the assumptions behind the linear logistic regression provide critics with a target. Previous literature has converted the calibration problem into a regression task without any loss of generality. In this paper, we draw on recent academic results in order to suggest two new one-parametric families of differentiable functions as candidates for this regression. The derivation of these two families of differentiable functions is based on the maximum entropy principle and, thus, they rely on a minimum number of assumptions. We compare the performance of four calibration approaches on a real-world data set and find that one of the new one-parametric families outperforms the linear logistic regression. Furthermore, we develop an approach in order to quantify the part of the general estimation error of probabilities of default that stems from the statistical dispersion of the discriminatory power.

     
    Export in Literaturverwaltung   RIS-Format
      BibTeX-Format
    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Ebook
    Format: Online
    ISBN: 9783957298706
    Weitere Identifier:
    hdl: 10419/251197
    Schriftenreihe: Discussion paper / Deutsche Bundesbank ; no 2022, 04
    Schlagworte: Calibration; credit score; cumulative accuracy profile; logistic regression; margin of conservatism; probability of default
    Umfang: 1 Online-Ressource (circa 42 Seiten), Illustrationen
  4. The unintended consequences of employer credit check bans for labor markets
    Autor*in: Cortés, Kristle R.; Glover, Andrew; Tasci, Murat
    Erschienen: 2019
    Verlag:  Federal Reserve Bank of Cleveland, [Cleveland, OH]

    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signatur:
    VS 36
    Fernleihe:
    keine Fernleihe
    Export in Literaturverwaltung   RIS-Format
      BibTeX-Format
    Hinweise zum Inhalt
    Volltext (kostenfrei)
    Volltext (kostenfrei)
    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    Schriftenreihe: Working paper / Federal Reserve Bank of Cleveland ; 19, 05 (February 2019)
    Schlagworte: vacancies; credit score; credit check
    Umfang: 1 Online-Ressource (circa 44 Seiten), Illustrationen
  5. Your friends, your credit
    social capital measures derived from social media and the credit market
    Autor*in: Bricker, Jesse; Li, Geng
    Erschienen: July 2023
    Verlag:  Divisions of Research & Statistics and Monetary Affairs, Federal Reserve Board, Washington, D.C.

    Zugang:
    Verlag (kostenfrei)
    Resolving-System (kostenfrei)

    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signatur:
    VS 412
    Fernleihe:
    keine Fernleihe
    Export in Literaturverwaltung   RIS-Format
      BibTeX-Format
    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    Schriftenreihe: Finance and economics discussion series ; 2023, 048
    Schlagworte: Social trust; social capital; economic mobility; credit score
    Umfang: 1 Online-Ressource (circa 16 Seiten)