BigDataScore | Improving loan quality and acceptance rates by predicting credit behavior through social mediadata.

Summary
The problem: Credit Losses on Banks’ Loan Portfolios - Youngsters and 2nd generation immigrants still have difficulty in obtaining credit.

The solution: Our credit scoring model called Big Data Score assesses the credit quality of people and accurately predicts their payment behaviour based on data from social media (Facebook) and internet browsing behaviour.

Objectives of the overall innovation project:
1) Bring the present Technology Readiness from level 7 to 9.
2) Provide the system with complete access to real life data and Open Data made available from governments.
3) Development of a marketing and sales strategy based on two key principle: vertical approach and distribution approach.

Value Proposition: To help lenders to save money on credit losses and to make more money on increased acceptance rate.

Business Model: Business follows a simple and easily scalable model where lender pays for each score:
0.99 EUR per Facebook score and 0.20 EUR per browser score.

Users/Clients: Our target client is anyone who is taking a short to medium term (1-36 months) credit risk.

Competition: Traditional credit bureaus and innovative credit score (Kreditech, Leendo, ZestFinance).

Revenue Streams: 36M€ revenues at the 3rd year after commercialization.

Team: Big Data Scoring AS, Aasa Global AS.

Required funding: 1,5M€
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More information & hyperlinks
Web resources: https://cordis.europa.eu/project/id/662652
Start date: 01-03-2015
End date: 30-06-2015
Total budget - Public funding: 71 429,00 Euro - 50 000,00 Euro
Cordis data

Original description

The problem: Credit Losses on Banks’ Loan Portfolios - Youngsters and 2nd generation immigrants still have difficulty in obtaining credit.

The solution: Our credit scoring model called Big Data Score assesses the credit quality of people and accurately predicts their payment behaviour based on data from social media (Facebook) and internet browsing behaviour.

Objectives of the overall innovation project:
1) Bring the present Technology Readiness from level 7 to 9.
2) Provide the system with complete access to real life data and Open Data made available from governments.
3) Development of a marketing and sales strategy based on two key principle: vertical approach and distribution approach.

Value Proposition: To help lenders to save money on credit losses and to make more money on increased acceptance rate.

Business Model: Business follows a simple and easily scalable model where lender pays for each score:
0.99 EUR per Facebook score and 0.20 EUR per browser score.

Users/Clients: Our target client is anyone who is taking a short to medium term (1-36 months) credit risk.

Competition: Traditional credit bureaus and innovative credit score (Kreditech, Leendo, ZestFinance).

Revenue Streams: 36M€ revenues at the 3rd year after commercialization.

Team: Big Data Scoring AS, Aasa Global AS.

Required funding: 1,5M€

Status

CLOSED

Call topic

ICT-37-2014-1

Update Date

27-10-2022
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Horizon 2020
H2020-EU.2. INDUSTRIAL LEADERSHIP
H2020-EU.2.1. INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies
H2020-EU.2.1.1. INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Information and Communication Technologies (ICT)
H2020-EU.2.1.1.0. INDUSTRIAL LEADERSHIP - ICT - Cross-cutting calls
H2020-SMEINST-1-2014
ICT-37-2014-1 Open Disruptive Innovation Scheme (implemented through the SME instrument)
H2020-EU.2.3. INDUSTRIAL LEADERSHIP - Innovation In SMEs
H2020-EU.2.3.1. Mainstreaming SME support, especially through a dedicated instrument
H2020-SMEINST-1-2014
ICT-37-2014-1 Open Disruptive Innovation Scheme (implemented through the SME instrument)