The challenge regarding supply and demand for loans and access to volume of data can be minimized faster, if P2P platforms expand their offering of crowdfunding from crowdlending to equity crowdfunding and personal loans. By offering equity-crowdfunding, platforms can attract start-ups that will be fully benefited by this form of financing, as they can test the market interest of their project before any real investment is done. Also, because lenders invest individually a smaller amount, they purchase smaller shares and rights, and are less demanding than venture capitalists. This means, that the company’s shareholders can hold the majority of the shares, and therefore, keep control over the company.
Additionally, by offering personal loans, platforms can tap into the market of quick loans, and attract borrowers by offering cheaper credit, while attracting lender with the promise of higher interest rates.
Personal loans can give an excellent source of data for SME financing, because in SMEs, the person of the owner or key manager merges with the person of the company, so understanding the person of the owner/manager, can give excellent indications of whether a loan will default.
Finally, volume can be created through a cross-border European platform, that would increase the scope of the platform, while granting regulatory tranquility, because there is a regulation for crowdfunding on the making, which will render all legislation within the EU area compatible.
73 https://ec.europa.eu/info/tender/identifying-market-and-regulatory-obstacles-cross-border-development-crowdfunding-eu_en
9
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11 Appendix 1: Big Data Scoring
Summary of interview with Mr. Erki Kert, CEO of Big Data Scoring made via Skype on the 28th of February 2019.
Big Data Scoring is a cloud-based credit scoring company located in UK with offices in the USA, Chile, Indonesia, Finland and Poland. They provide credit scoring for banks, telecoms and consumer lenders.
Their purpose is not only to improve in-house credit scoring, but also help those individual without credit like immigrants and young people to acquire loans. Their solution is integrated via an API to the lenders platform, so the credit scoring is done in-house, combining alternative data to the lenders internal data.
Their solution improves the in-house scoring between 5 to 15% as soon as installed and tested. After the solution is used for 3 to 6 months, the level of accuracy is improved to up to 30%.
Big Data Scoring collects vast amount data from multiple sources: web search results, behaviour tracking (clickstream), device technical details, mobile app data. Around 5.000 data points are collected from each borrower.
Some of the parameters they look for are:
• Neighbourhood of the applicant and geodata
o Information about distance between parks, hospitals, number of unemployed in the area among many other variables are collected
• The device and/or system used by the applicant
o How was the access done? Mobile or home computer, IOS or Android, etc.
• The applicant’s IP address
o Are you accessing from where you listed as your address, who is the internet provider.
• The applicant’s E-mail address
o E-mail provider, Has this e-mail been involved in fraud, etc.
• Information publicly available over the internet about the applicant o Google, Linkedin, etc
The credit scoring is all done automatically and it does not take more than a few seconds to be made.