THE LEADER IN BIG DATA CREDIT SCORING SOLUTIONS
26% improvement in scoring accuracy
What is Big Data Scoring?
Big Data Scoring is an easy-to-integrate, cloud-based service that lets consumer lenders improve loan quality and acceptance rates through the use of big data.
Do you find that the information from credit bureaus isn’t always enough to make the correct credit decision? Would you like to improve your loan acceptance rates and credit quality? If so, then Big Data Scoring can help.
Our system gathers thousands of pieces of data, from a variety of online sources – such as social media, Google search terms, IP address and device used – on the loan applicant and links it with their behaviour on your web site. From this we can – in a matter of seconds – accurately predict the potential client’s payment behaviour, helping you make informed and more profitable credit decisions.
More accurate underwriting helps lenders issue more loans and better manage credit quality. When combined with good in-house scoring models, Big Data Scoring can improve the predictive quality by up to 26%.
More accepted loans
% of accepted loans
with Big Data Scoring
Smaller credit losses
credit loss %
with Big Data Scoring
We work with lenders of all kinds – banks, payday lenders, P2P lending platforms, microfinance providers and leasing companies. As long as consumer lending is your business and online channels play an important role in client acquisition, we can improve your credit quality and loan acceptance rates. We also work with insurance companies, telecoms providers and e-commerce platforms.
A Central European bank with a large consumer lending business across seven countries
Underwriting for consumer loans relied on in-house underwriters using data from an external credit bureau
Big Data Scoring’s solution was backtested and calibrated on historical data within three days. Big Data Scores were seamlessly integrated to bank underwriting
The bank saw a 14.7% instant improvement in credit scoring accuracy for new clients, leading to lower credit losses and higher loan approval rates.
The models continue to learn and are on course to hit 26% value added within six months.
Big Data Scoring's solution can be backtested and integrated within a day.
Big Data Scoring’s service is mostly used to complement the current in-house underwriting processes. If information for your clients is scarce (for example, thin-file customers such as millennials or new to bank clients) 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.
Risk-free backtesting and calibration on historical data
5-15% improvement in scoring accuracy
Simple and easy installation of the code on a website or in mobile apps
Combined 20-30% accuracy improvement
After using Big Data Scoring for 3-6 months
Our team has over 30 years' experience in developing credit scoring solutions. Today, we help lenders collect and analyze big data for more accurate - and therefore more profitable - credit decisions.
Backed by London based private equity investor Novator Partners and Greek investors Olympia Development.
Work in partnership with MasterCard through their Start Path program.
Erki Kert, CEO / Co-founder► LinkedIn
Erki has nearly 10 years’ experience in one of the fastest growing commercial banks in the Baltic region, LHV Bank. He served on the management board and was also a member of the bank’s credit committee, which developed the idea of creating a better way for credit scoring. Erki is also a radio show host and a member of the NASDAQ Tallinn Listing and Surveillance Committee.
Kenneth Halin, Chief Data Scientist► LinkedIn
Kenneth has worked with various credit bureaus for more than 20 years. He has served as Development Director at Experian Nordic, responsible for risk modeling in the Nordics for four years, as well as filling the role of Development Director at Dun & Bradstreet for seven years. He founded of Connectus, an Estonian credit bureau (now Bisnode Estonia). He is also the chief architect of all Big Data Scoring models.
Meelis Kosk, Head of Sales► LinkedIn
After graduating from Boston College in the States, Meelis has held various positions in financial sector, including in a Boston hedge fund, the Estonian Central Bank and a credit insurance company. Throughout his career he has focused on selling complex financial products and making the use of such products as simple as possible for hundreds of clients. In his free time, Meelis enjoys offshore sail racing and endurance sports.
Sanna Susiluoto, Head of Data Science► LinkedIn
Sanna has experience from consultation, project management and data analytics in different business development projects for customers mostly in finance and retail industries. She is passionate about location based analytics and holds a PhD in ecology. Before moving to private sector she was doing research in the university. Sanna’s hobbies include various sports and experimental gardening.
Juha Palomäki, CTO► LinkedIn
Juha has 15+ years of IT consulting and product development experience, working with various technologies. He has the ability to solve complex problems with tight deadlines and innovative solutions. Expert in banking related software solutions.
Antonio Latorre, Director of Latin America► LinkedIn
Antonio is an entrepreneur that holds an Economy and MBA degree at IESE. With more than 20 years of experience in financial advisory, he is a local media consultant and panellist for retail finance. He has developed outsourced business cases for several financial institutions in Latin America for over 8 years. With a long entrepreneurial career with more than 12 start-ups successfully mounted. He is also a keen outdoor enthusiast, that enjoys mountain bike and cross country racing.