CREDIT SCORING MODELS
Automatically gathering tens of thousands of data points, Big Data Scoring’s platform uses machine learning algorithms to calculate your clients’ expected probability of default. Analyzing data from a number of online sources including social media, blogs and other web pages, Big Data Scoring collects relevant information to help you make an informed decision about your client.
Loan applications that have been incorrectly declined and credit losses cost lenders money. More accurate underwriting that uses publicly-available data about customers can solve these problems.
Big Data Scoring’s service is mostly used to complement to current in-house underwriting processes. If information for your clients is scarce (for example, thin-file customers such as millennials or recent immigrants) or traditional credit scores are of low quality or non-existent (such as in emerging markets), Big Data Scoring’s solution is able to form the core of your underwriting processes.