Credit referencing is one of the pillars for financial systems and it is rapidly growing in importance. The primary purpose of credit referencing is to operate as a trusted third party holder of data which can be used by lenders to make better decisions about whether to give, or continue to give credit to consumers and businesses.
Traditional credit scoring methods nowadays prove inaccurate as they substantially ignore the vast amount of data available online about clients, focusing on just a minor set of details on the customers requiring loans (full name, address, age, gender, bankruptcy/insolvency data whenever available, court judgements, taxpayer or other unique identification number, assets and properties, etc.) and ignoring lots of valuable inputs. On the contrary, an average Facebook profile contains 5.000-10.000 pieces of information, out of which up to 100 is relevant in financial credit scoring and can be used to predict actual payment behaviour.
Traditional methods also fail in serving and supporting entire generations or groups of people, having no banking history and thus not being identifiable by most of these parameters will actually receive weak accreditation for receiving loans or any type of credit. Among these, youngsters (between 18-30) are especially important due to high youth unemployment in EU and a fear of an entire “lost generation” as a result of the debt crisis. In addition, about the 70% of people has no record in traditional credit bureaus, thus having no banking history and making it difficult for a lender to predict his/her payment behavior. Immigrants are part of the EU open services/people/workforce agenda. For youngsters and immigrants, as well as middle-aged specialists or representatives of the working class, local credit bureaus coverage is low and these clients cannot have access to financial products (underbanked segment). However, for such clients there is still a lot of online data available which makes it possible to assess their creditworthiness and allow banks issuing loans acquiring new clients and getting higher incomes (less credit losses).
We will solve the problems mentioned above by providing the innovative BIG DATA SCORE solution that assesses the credit quality of people and overcomes the limitations of currently available scoring methods. After crunching more than 50 million lines of data, Big Data Scoring has discovered that social media own very strong predictive powers when it comes to credit behaviour. The model we are proposing is therefore innovative and owns incredible competitive advantage, as it allows to collect up to 15.000 pieces of information by integrating social media and Internet browser data to exactly determine the customer’s payment behaviour and their default probability. Because of the use of innovative data sources, we can help banks find the data and make sense of it. Hence, young people and immigrants can be more easily served by banks.
In order to expand the current Big Data Score product to all European countries, and according to the feasibility study carried out in Phase 1 (Big Data Score – GA No. 622652), the proposers intend to set and achieve the following objectives:
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, and development of an IP protection strategy.
