Cognitive Finance A.I. Solutions

Cognitive Finance Solutions focus on building bespoke solutions for our financial services clients using

  • Machine Learning

  • Natural Language Processing


Added value to your business

Our AI and ML  solutions translate in better customer and product models, personalized marketing, operational excellence, and fewer costs due to cybersecurity and fraud attacks. In short, we provide you with all the necessary ingredients to research, implement and deploy your next generation data-driven products and solutions.  


Our Method

We have an unique way of working which combines both the steady deliverables of a software development life cycle (SDLC) with the insights of a data science life cycle (DSLC) with practical business strategy implementation. Furthermore, we operate in sprints both for data engineering and data science in order to grow the results on both fronts iteratively from the beginning of the project. We also use collaborative methods such as Jupyter Hub, Git, and Trello to get as fast as possible to results.


Our method follows 5 phases, with mixed business, science and engineering tasks: (BizDevOps approach)


Use cases we have experiences in


  • Better understand customer behaviour

    • Fraud detection and prevention

    • Better cyber-security techniques

    • Personalised customer experience

    • Better advice on products

    • Better diligence on contracts

    • Conversational interfaces

    • Improved risk scoring

    • Less intrusive marketing campaigns    


  • Identify and prevent failures and errors

    • Robotic and Intelligent process automation

    • Smart pattern detection

    • Forecasting and Temporal Analysis


  • Predicting analytics: loans defaults

At Cognitive Finance Solutions we have developed a novel and documented approach which uses deep learning to extract clusters and patterns from the original data and subsequently cluster these patterns into groups. Effectively, this approach favours prediction accuracy first rather than interpretability first.


Currently, models are largely based on hard decision boundaries (for instance using decision trees and ensembles of trees). However this approach, while providing an interpretation of the results, could potentially miss a number of more subtle signals and indicators.

Since interpretability is still an important characteristic of predictive models, we have engineered a way of re-mapping the features generated by the deep learning predictive algorithms to the original data. Hence, providing detailed statistics about each cluster of data as extracted/learned by the deep learning models.


This is a study case to improve predictability of defaults in credit cards. 


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