|
|
|
|
| LEADER |
02479cas a2200241 a 4500 |
| 001 |
AALejournalIMF019320 |
| 008 |
230101c9999 xx r poo 0 0eng d |
| 020 |
|
|
|c 5.00 USD
|
| 020 |
|
|
|z 9781498314428
|
| 022 |
|
|
|a 1018-5941
|
| 040 |
|
|
|a BD-DhAAL
|c BD-DhAAL
|
| 100 |
1 |
|
|a Bazarbash, Majid.
|
| 245 |
1 |
0 |
|a FinTech in Financial Inclusion :
|b Machine Learning Applications in Assessing Credit Risk /
|c Majid Bazarbash.
|
| 264 |
|
1 |
|a Washington, D.C. :
|b International Monetary Fund,
|c 2019.
|
| 300 |
|
|
|a 1 online resource (34 pages)
|
| 490 |
1 |
|
|a IMF Working Papers
|
| 500 |
|
|
|a <strong>Off-Campus Access:</strong> No User ID or Password Required
|
| 500 |
|
|
|a <strong>On-Campus Access:</strong> No User ID or Password Required
|
| 506 |
|
|
|a Electronic access restricted to authorized BRAC University faculty, staff and students
|
| 520 |
3 |
|
|a Recent advances in digital technology and big data have allowed FinTech (financial technology) lending to emerge as a potentially promising solution to reduce the cost of credit and increase financial inclusion. However, machine learning (ML) methods that lie at the heart of FinTech credit have remained largely a black box for the nontechnical audience. This paper contributes to the literature by discussing potential strengths and weaknesses of ML-based credit assessment through (1) presenting core ideas and the most common techniques in ML for the nontechnical audience; and (2) discussing the fundamental challenges in credit risk analysis. FinTech credit has the potential to enhance financial inclusion and outperform traditional credit scoring by (1) leveraging nontraditional data sources to improve the assessment of the borrower's track record; (2) appraising collateral value; (3) forecasting income prospects; and (4) predicting changes in general conditions. However, because of the central role of data in ML-based analysis, data relevance should be ensured, especially in situations when a deep structural change occurs, when borrowers could counterfeit certain indicators, and when agency problems arising from information asymmetry could not be resolved. To avoid digital financial exclusion and redlining, variables that trigger discrimination should not be used to assess credit rating.
|
| 538 |
|
|
|a Mode of access: Internet
|
| 830 |
|
0 |
|a IMF Working Papers; Working Paper ;
|v No. 2019/109
|
| 856 |
4 |
0 |
|z Full text available on IMF
|u http://elibrary.imf.org/view/journals/001/2019/109/001.2019.issue-109-en.xml
|z IMF e-Library
|