Today's credit scoring is mostly based on the combination of credit bureau information and bank's internal data. As a result, banks struggle to correctly assess the creditworthiness of new to bank clients, young people, and recent immigrants. Too many clients get declined.

Big Data Scoring solves that issue by sourcing additional data about people from a variety of online sources - public web search results, blogs, social media, device data, behavioral aspects, geodata classification, statistics offices, census studies, etc. On average, our solutions find up to 5,000 additional data points per each individual from the aforementioned sources. Based on such data our machine learning algorithms can precisely predict the payment behavior of each loan applicant. As a result, banks can issue more loans and better manage credit quality.

When combined with the banks' own in-house scoring models, Big Data Scoring solutions can improve the predictive power of bank underwriting by 20-25%. This ...
Today's credit scoring is mostly based on the combination of credit bureau information and bank's internal data. As a result, banks struggle to correctly assess the creditworthiness of new to bank clients, young people, and recent immigrants. Too many clients get declined.

Big Data Scoring solves that issue by sourcing additional data about people from a variety of online sources - public web search results, blogs, social media, device data, behavioral aspects, geodata classification, statistics offices, census studies, etc. On average, our solutions find up to 5,000 additional data points per each individual from the aforementioned sources. Based on such data our machine learning algorithms can precisely predict the payment behavior of each loan applicant. As a result, banks can issue more loans and better manage credit quality.

When combined with the banks' own in-house scoring models, Big Data Scoring solutions can improve the predictive power of bank underwriting by 20-25%. This translates to tens of millions of additional euros in revenue and significant savings in credit losses.

Based on the bank's historical client data, we can show the predictive power of our solutions in a risk-free environment and without any investment from the bank. After a successful proof of concept, the solutions can be used via our cloud API or deployed on the banks' servers.
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