Data Science algorithms

Detects employer-employee relationships in transaction data so you can tell which of your clients work together for a single company, who is a newbie, who is the boss, who got promoted,
and who got fired.

Household detector

Correlates people in time and space to reconstruct family relationships between clients. This knowledge can be used to improve a person’s risk score or to make a better-targeted marketing offer.

Instalment detector

Built to detect all loan payments going from your bank to other banks and financial institutions enabling you to offer your clients a loan transfer or consolidation.

Interest rate optimiser

Improves acceptance, reduces turnover, and boosts profit while still complying with strict banking regulations by taking into account the price sensitivity of each client. You can find more information here.

Related webinars & articles

• Propensity modelling for banks (webinar)
• A data-driven society
• Social scoring and credit forecasting: Data science breakfast
• How can banks increase loan uptake among their client base?
• How using data helps banks minimize the risks of loan default
• Data-driven incident handling
• The 4 most important applications of data science in banking

Our main and foremost goal is to bridge the gap between you, the leading business data analyst in your company, and the vast amount of mindblowingly complex data your company has. You are the expert in your field, you understand the business needs of your company, and you know where the money is. Your company gathers a huge amount of data about your clients. Somewhere in all those terabytes is everything about your clients – their status, their needs, their preferences, even their personality traits, habits, and relationships. If only you could get those pieces of information in an easily accessible format loaded into the tools you are familiar with…  

Have a look at our Welcome to the World of Data Science sheet and Big Data for Banking services.