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cred_project_xx by Mind Map: cred_project_xx

1. Suggested Launch Countries (based on immigration to Spain)

1.1. Wave 1: Colombia, Venezuela and Pery

1.2. Wave 2: Argentina, Ecuador and Chile

2. Additional Revenue Generation Streams

2.1. Additional Loans (above 2k EUR)

2.2. Data sharing api (for other institutions)

2.3. Referrals for Insurance, Banks, other Financial Institutions

2.4. Debit cards

3. Money Disbursal

3.1. Bizum

3.2. Bank transfer

3.2.1. Spanish banks

3.2.2. Neobanks

4. Billing innitiatives

4.1. popups

4.2. emails

4.3. sms

4.4. phone calls

5. Benchmarks

5.1. M-Kopa (Kenya)

5.2. LenddoEFL

5.3. Tala.co

5.4. Branch.co

6. Regulatory Environment

6.1. Check Spanish / EU law for microloands

6.2. Key Stakeholders

7. Credit Concession Model

7.1. Demographics

7.2. Home Country

7.2.1. Bureaus Data

7.2.2. Bills

7.2.3. Statements

7.2.4. Possessions

7.3. Spanish available Data

7.3.1. Bills

7.3.2. Empadronamento

7.3.3. Bank Statements

7.3.4. Employment related

7.4. Previous Activity

7.4.1. Behaviour Score

7.5. Established models

7.5.1. FICO Score (Fair Isaac Corporation)

7.5.2. VantageScore

7.5.3. MachineLearning Based Modelling

7.6. Microloans especific models

7.6.1. Psycometric Scoring

7.6.2. Alternative Data Models

7.6.2.1. Mobile Phone Data

7.6.2.2. Utility and Rent Payments

7.6.2.3. Ecommerce Actity

8. Partnership with Bureaus

8.1. Colombia

8.1.1. Datacredito

8.1.2. CIFIN

8.1.3. Procredito

8.2. Venezuela

8.2.1. CEVEN

8.2.2. SICRIS

8.2.3. TuCredito

8.3. Peru

8.3.1. Equifax Peru

8.3.2. Sentinel

8.3.3. Xchange

8.4. Argentina

8.4.1. Veraz

8.4.2. Nosis

8.4.3. Fidelitas

8.5. Ecuador

8.5.1. Equifax Ecuador

8.5.2. Buró de Credito

8.5.3. Centrales de Riesgo

8.6. Chile

8.6.1. Equifax Chile

8.6.2. DICOM

8.6.3. SINACOFI

9. Risks

9.1. Funding

9.1.1. Not enough

9.2. Bad Debt

9.2.1. Wrong models

9.3. Fraud

9.3.1. Registration

9.3.2. Loan Applications

9.4. Economics

9.4.1. Wrong BC

9.5. Technical Infrastructure

9.5.1. Bugs

9.5.2. Excessive demand

9.5.3. Security

10. Fraud Protection

10.1. App Registration & Login

10.1.1. Document validation

10.1.1.1. Auomated documents review

10.1.1.1.1. Resistant.ai

10.1.1.1.2. Veriff

10.1.1.1.3. Onfido

10.1.1.2. Human validation (in the beginning)

10.1.2. Biometric validation

10.1.3. Multi factor authentication

10.2. Credit Bureau Integration

10.3. Advanced Analytics

10.3.1. Machine Learning

10.3.1.1. Suspicious Applications

10.3.2. Behavioural

10.3.2.1. Suspicious IPs (outside of Spain)

10.3.2.2. Automated filling (bots)

10.4. Audit and Review

10.4.1. Regular

10.4.1.1. Review data

10.4.1.2. Review processes

10.4.2. Post Loan

10.4.2.1. Review approved loan applications for improvements in future screening

11. Storytelling

11.1. Muhammad Yunus

11.1.1. Bangladesh

11.1.2. 1980s

11.1.3. Nobel Prize

11.2. Immigration numbers to Spain

11.2.1. ine.es