Data Science for Banks and Financial Service Providers
From default risk for loans to a more targeted customer approach: banks and financial service providers have large data pools that need to be used with Data Science. The application scenarios in which analytics in the area of finance can generate decisive competitive advantages are virtually unlimited.
Specializing in data science, we have been supporting banks and financial service providers for more than 10 years in exploiting their data potential.
Data Science for Banks and Financial Service Providers
From default risk for loans to a more targeted customer approach: banks and financial service providers have large data pools that need to be used with Data Science. The application scenarios in which analytics in the area of finance can generate decisive competitive advantages are virtually unlimited.
Specializing in data science, we have been supporting banks and financial service providers for more than 10 years in exploiting their data potential.
A selection of questions we have answered in our projects in the field of finance:
Which customer has an affinity for a particular product?
Next best offer: determining customer interest based on historical conversions and demographic information. The resulting affinity score is the knowledge base for campaign management in sales and the reason for a significant increase in response rates.
How can fraud be detected earlier?
Analysis of anonymized transaction data based on the Benford Law to detect irregularities in transactions. The algorithm detects conspicuous values and triggers an alert. The negative effects of fraud have thus been reduced.
How can the risk analysis process be more automated?
Calculation of the individual credit default risk of customers using simulations and statistical models. Employees are relieved of this routine work and at the same time the forecast quality can be further optimized.
Which customer is considering changing his bank?
Churn prevention: using data science to identify earlier which customer you are in danger of losing. Link different data sources for a time-to-event forecast that enables you to proactively target the customer and persuade them to stay.
How can the prediction of KPIs be further improved?
Further develop the existing forecast using an ensemble modeling approach that uses the best forecast model for each KPI. Implementation of this forecast in a user-friendly, interactive Shiny app.
What does the appropriate IT infrastructure for data science in a strictly regulated environment look like?
Concept development and implementation of a high-performance IT infrastructure regarding the strict regulations and requirements regarding the operating concept and security.
Data-Driven Software:
YUNA – Data Science Platform
YUNA is the central platform for the development and control of digital, AI-supported projects. It combines BI functions with the possibility to use various models and scripts.
Case Study:
Customer analysis for VR Bank Mitte eG
VR-Bank Mitte eG is striving to optimize its sales processes in the area of customer approach and support. Specifically, the goal is to assess and evaluate customers’ latent interest in a particular product in advance of a sales campaign.
Learn more about our solution!
Data Science Infrastructure:
On Premise, Cloud or Hybrid
Design, implementation and support! We are the provider of resilient and reliable data-driven services. We also make your infrastructure ready for the digital challenges of the future!
Data Projects:
From the idea to productive service!
Which use cases are particularly purposeful for you? How can knowledge creation succeed in your company? From the solution idea to the productive use of AI systems in your company: We create perceptible added value for you from data.
Jump start now – we will walk you through it!