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Haksoo Ko Seoul National University Sept. 19, 2018 Sogang University ICT Law & Economy Institute Platform Competition and Data-Driven Innovation Data-Driven Innovation: Some Observations

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Page 1: Data-Driven Innovationicle.sogang.ac.kr › static › files › admin_at_icle.sogang.ac...2018/09/18  · Haksoo Ko Seoul National University Sept. 19, 2018 Sogang University ICT

Haksoo KoSeoul National University

Sept. 19, 2018

Sogang University ICT Law & Economy Institute

Platform Competition and Data-Driven Innovation

Data-Driven Innovation:Some Observations

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http://eventplanningtemplate.org/wp-content/uploads/2016/09/credit-card-decline-letter-13434983.png

Credit Card Application: Rejection Letter

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“adverse action”• a denial or revocation of credit

• a refusal to grant credit in the amount or terms requested

• a negative change in account terms in connection with an unfavorable review of a consumer’s account

Purposes of giving notice• Prevent discrimination in credit

• Consumer education

• Error checking

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Adverse Action Notice Requirementsunder FCRA (Fair Credit Report Act) and ECOA (Equal Credit Opportunity Act)

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Adverse Action Notice Requirementsunder FCRA (Fair Credit Report Act) and ECOA (Equal Credit Opportunity Act)

__Credit application incomplete__Insufficient number of credit references

provided__Unacceptable type of credit references

provided__Unable to verify credit references__Temporary or irregular employment__Unable to verify employment__Length of employment__Income insufficient for amount of credit

requested__Excessive obligations in relation to

income__Unable to verify income__Length of residence__Temporary residence

__Unable to verify residence__No credit file__Limited credit experience__Poor credit performance with us__Delinquent past or present credit

obligations with others__Collection action or judgment__Garnishment or attachment__Foreclosure or repossession__Bankruptcy__Number of recent inquiries on credit

bureau report__Value or type of collateral not sufficient__Other, specify: ___

12 CFR Appendix C to Part 202, Sample Notification Forms

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Credit Information and Discrimination?

Different states of (relevant and available) information• (Almost) full information

• When a credit card company has most of the information that it requires to make an assessment

• Lack of information• When a credit card company has virtually no information that it requires to make an

assessment

• E.g., a new or illegal immigrant

• Partial or distorted information• When a credit card company has partial information about the card applicant

• E.g., an expatriate business executive who would stay in the country for a few years

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Credit Information and Discrimination?

Case of partial or distorted information: Reactions in the market?• From a credit company perspective:

• To look for alternative or supplementary information which would serve as a proxy for a credit report

• From a consumer’s perspective:

• To resign from the credit market

• (If possible) to provide further relevant information

• Incentives to provide positive information, but to hide negative information

• Market outcome(s)??• What if data from different sources can be combined, so that a missing

link can be filled…

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Source : Jentzsch, The Personal Data Economy: Introduction and Insights (2017)

Data-Intermediation System

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Source : Jentzsch, The Personal Data Economy: Introduction and Insights (2017)

Data-Intermediation System: Taxonomy

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Source : Jentzsch (2007, p13), Figure 2.1

Case of consumer credit reporting:General structure of the credit reporting market

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Sample of consumer credit report: Korea

What does it mean to have a score of 353?

• What if the score is, e.g., 330?

How is this score determined?

• How to give convincing and credible explanation about a specific score?

How would the consumer be able to improve the score?

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https://algorithmwatch.org/en/openschufa-shedding-light-on-germanys-opaque-credit-scoring/

Consumer credit report in Germany: Schufa

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https://algorithmwatch.org/en/openschufa-data-donation-launches//

Schufa

“OpenSchufa”• Over 20,000 volunteers: their

credit reports to be used to reverse engineer credit scoring algorithm

• What can be accomplished?• What about ‘abuse/gaming’

risk?• Higher transparency, or a

‘catch 22’ situation?