What is Big Data Scoring?

Big Data Scoring is a cloud-based credit decision engine that helps banks, telecoms and consumer lenders improve credit quality and acceptance rates through the use of big data.

We develop and deploy custom scoring models that combine a lender’s internal data with thousands of pieces of external data such as location based information, web search results, behavioural tracking, device technical details, mobile app data and much more. This enables lenders to accurately predict borrower payment behaviour, helping then make informed and more profitable credit decisions in real time.

More accurate underwriting helps lenders issue more loans, better manage credit quality and fight fraud. The fully automated scoring engine returns credit decisions in real time and removes the risk of human error or personal judgement.

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 – some of the largest banks in the world, payday lenders, P2P lending platforms, microfinance providers, leasing companies, insurance providers, e-commerce platforms and telecoms. In the field of consumer lending and where online channels play an important role in client acquisition, we can improve credit quality and loan acceptance rates.

Case study


One of the biggest banks in Latin America with a large consumer lending business was looking to fully automate its credit decisions and improve loan acceptance rates.


Before the project, underwriting 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 3 days. The solution was seamlessly integrated to bank underwriting.


The bank saw an instant improvement in credit scoring accuracy, allowing it to issue 17% more loans at the same risk level. This translated to an additional EUR 20m EBITDA in 4 years. With the addition of our proprietary data collection tools, the models continue to learn and the added value doubles within 6 months.

Easy integration

Big Data Scoring solution can be easily integrated with any bank core or credit platform

Big Data Scoring solution can be easily integrated with any bank core or credit platform via a simple REST API. We make sure all data transfer is secure and data is processed and stored in a jurisdiction suitable for each customer. The process begins with training the big data algorithms for each business case based on the enriched historical data. After validation by the client, the model(s) and business rules are deployed to the Big Data Scoring Decision Engine and are ready to be used in underwriting. At the same time, the proprietary Big Data collection tools would be added to the lender’s online client acquisition channels that allow future model training with additional unique data.


Risk-free backtest

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

About us

Our team has over 30 years experience in developing credit scoring solutions

The team members have been responsible for building businesses that have become part of Experian and Dun & Bradstreet; with decades of experience from credit bureaus, data science and business consulting. Today, we help lenders make better credit decisions through utilising the latest technologies in big data and scorecard development. We work with many of the largest companies in the world – banks, retailers, non-bank lenders and telecoms. We are backed by the London based private equity investor Novator Partners and Greek investors Olympia Development. We work in partnership with MasterCard through their Start Path program and were among the most promising fintech startups in the Accenture’s FinTech Innovation Lab program in London.


Antonio Latorre, CEO / Management Board Member


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.

Kenneth Halin, Chief Analytics Advisory Board


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)

Vesa Huotelin, Head of Development and Analytics


Vesa has a degree in statistics (M.Soc.Sc.) and previous experience in co-founding a tech startup. In BDS, while taking care of the data science and analytics team in Finland, he is also focusing on automated tools for data preprocessing and predictive modelling. Vesa's hobbies range from songwriting to outdoor activities such as running and disc golf.

Jani Reinikainen, Chief Information Officer

Jani has strong hands-on experience in software architectures and cloud based solutions. He has over twenty years of experience in software engineering and development in multiple different industries. His studies were on software and telecommunication technology (MSc Mathematical IT). He loves delivering great solutions.

Juha Palomäki, Chief Technology Advisory Board

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.