How Long to Read FinTech in Financial Inclusion: Machine Learning Applications in Assessing Credit Risk

How Long Does it Take to Read FinTech in Financial Inclusion: Machine Learning Applications in Assessing Credit Risk?

It takes the average reader and 34 minutes to read FinTech in Financial Inclusion: Machine Learning Applications in Assessing Credit Risk by Majid Bazarbash

Assuming a reading speed of 250 words per minute. Learn more

Description

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.

How long is FinTech in Financial Inclusion: Machine Learning Applications in Assessing Credit Risk?

FinTech in Financial Inclusion: Machine Learning Applications in Assessing Credit Risk by Majid Bazarbash is 34 pages long, and a total of 8,636 words.

This makes it 11% the length of the average book. It also has 11% more words than the average book.

How Long Does it Take to Read FinTech in Financial Inclusion: Machine Learning Applications in Assessing Credit Risk Aloud?

The average oral reading speed is 183 words per minute. This means it takes and 47 minutes to read FinTech in Financial Inclusion: Machine Learning Applications in Assessing Credit Risk aloud.

What Reading Level is FinTech in Financial Inclusion: Machine Learning Applications in Assessing Credit Risk?

FinTech in Financial Inclusion: Machine Learning Applications in Assessing Credit Risk is suitable for students ages 8 and up.

Note that there may be other factors that effect this rating besides length that are not factored in on this page. This may include things like complex language or sensitive topics not suitable for students of certain ages.

When deciding what to show young students always use your best judgement and consult a professional.

Where Can I Buy FinTech in Financial Inclusion: Machine Learning Applications in Assessing Credit Risk?

FinTech in Financial Inclusion: Machine Learning Applications in Assessing Credit Risk by Majid Bazarbash is sold by several retailers and bookshops. However, Read Time works with Amazon to provide an easier way to purchase books.

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