Solutions We Provide

Client Engagement Prediction Modeling
We use spacial analysis and behavioral modeling to shape engagement strategies. Our approach is used to automate core functions using machine learning and AI models.
Dynamic Segmentation
Traditionally institutions only segment their target market based on a limited set of attributes. As financial and other behaviors are shifting, new approaches are needed.
Client Attrition Prediction
Do you know why your customers are leaving? Do you know why they’re not spending on your product any more? Using behavioral modeling through computational social science, new insights are extracted that be acted upon.
Digital Intelligence
We assist institutions worldwide with their digital solutions by finding machine-intelligent ways to utilize data in order to improve channel & customer engagement.
Philosophy of Transaction and Wellbeing
Financial health is an important aspect of modern life. Understanding how transactions affect everyday life is a crucial component of customer engagement.
Technology Modernization
Technology modernization processes include the use of agile approaches, devops, continuous delivery and full-stack technology understanding.

Reasons for Choosing Us

Full-stack Prediction

Service customer behaviors and expectations change, and organizations are becoming more digital. At the same time, lifestyles shift and the institution is not able to keep up. To move with these shifts in behavior, banks need state-of-the-art technology and expertise that can easily integrate into their organizations. ecosystem.Ai provides end-to-end client intelligence solutions that enable retail banks to provide a positive, practical and exciting customer experience.

  • ­Full-stack view of your prediction needs
  • ­Getting to automated solutions quickly

ecosystem.Ai shields the complexity of modern machine learning and artificial intelligence technologies. Our prediction products guide you through the process of producing predictors that can be deployed in days.

Latest Blogs

November 8, 2018

Machine learning rules of engagement: finding your client pulse through computational social science

Relationship dynamics can be seen in two ways. Either as a data science problem or as a human driven endeavor. But, how far do we need to go to understand the customer’s behavior in order to capture more value and keep them engaged through automated predictions? There is a complexity in the trust model that

August 28, 2018

Prediction and the importance of continuous recalibration

Prediction is a key element of the work of Computational Social Science. Why? In a company environment, implementing actions based on accurate predictions creates opportunities for generating and maintaining competitive advantage.  It can become a key driver to propel you to success over your competitors. But before examining the value of Computation Social Science and

August 20, 2018

A view on how Computational Social Scientists see money in a digital world

The concept of money has evolved over centuries. Concurrently with this evolution, society’s psychological and social relationship to money has evolved from the original “philosophy of money.” Little did we know how things would change. Bartering is the exchange of items of perceived similar value, which was the main method of transacting before the progression