Data quality improvement

Challenge: A French insurance company was looking to improve its data quality management for some of its business lines. Solution: A standardized process was designed, making use of data science techniques, including machine learning, and a web app was built to provide the business experts with a user-friendly and maintainable tool. Technologies: Python, Pandas, SciPy, Scikit-learn, Dash, GitLab

Solvency II process optimization

Challenge: An insurance company was looking to improve its Solvency 2 calculation process. The objective was to rationalize the number of tools, avoid duplication and reduce manual intervention in the process. Solution: We performed a deep analysis of the existing tools in order to identify the different improvement areas and suggest potential enhancement scenarios. Based on the client's preferred scenario, the corresponding improvements were then implemented. Technologies: Excel, VBA

Physical risk assessment

Challenge: Banks and insurance companies are looking to measure the physical risk exposure of their portfolio at exposure level. Solution: A geocoding tool was developed to link each address with geocoordinates. The geolocation data can then be placed on any map to carry out physical risk analyses (e.g. flood, droughts…) at a granular level. The geocoding tool handles typing errors and allows to track the quality of matches made (linking addresses with geocoordinates). Technologies: Python, GeoPandas

Pricing structure optimisation

Challenge: A leading scale-up in the payment sector was looking for an optimal pricing model for its major clients who operate an extensive global network of cross border payments with a high transaction volumes of varied types. Solution: We generated an optimal pricing model that incorporates various parameters including expected and average transaction volumes and values, risk due to parameter uncertainty, cost structure, and the competitive environment. Technologies: Monte Carlo Simulations, Numpy, Matplotlib

Runtime improvement

Challenge: A Belgian insurance company faced a runtime issue with its Dynamic Financial Analysis tool. This tool was Microsoft Excel based including some VBA code. Solution: Given the client's technology stack and skills, we proposed a solution where the most intensive computation has been written in C++. We also leveraged parallel computing capabilities of the projections. Technologies: C++, Armadillo

Advanced text recognition and analysis techniques

Challenge: A startup, engaged in developing innovative accounting solutions, is seeking fiduciary support for managing smaller-scale independent clients. Solution: Advanced techniques in text recognition and analysis facilitate automated extraction of essential information such as price, supplier, and date, thereby reducing the time required for document processing. Continuous refinement of the algorithm, driven by user validation, enables further performance improvements. Technologies: OCR, Natural Language Processing, Machine Learning, Python, Web Development

Data quality improvement

Challenge: A French insurance company was looking to improve its data quality management for some of its business lines. Solution: A standardized process was designed, making use of data science techniques, including machine learning, and a web app was built to provide the business experts with a user-friendly and maintainable tool. Technologies: Python, Pandas, SciPy, Scikit-learn, Dash, GitLab

Solvency II process optimization

Challenge: An insurance company was looking to improve its Solvency 2 calculation process. The objective was to rationalize the number of tools, avoid duplication and reduce manual intervention in the process. Solution: We performed a deep analysis of the existing tools in order to identify the different improvement areas and suggest potential enhancement scenarios. Based on the client's preferred scenario, the corresponding improvements were then implemented. Technologies: Excel, VBA

Physical risk assessment

Challenge: Banks and insurance companies are looking to measure the physical risk exposure of their portfolio at exposure level. Solution: A geocoding tool was developed to link each address with geocoordinates. The geolocation data can then be placed on any map to carry out physical risk analyses (e.g. flood, droughts…) at a granular level. The geocoding tool handles typing errors and allows to track the quality of matches made (linking addresses with geocoordinates). Technologies: Python, GeoPandas

Pricing structure optimisation

Challenge: A leading scale-up in the payment sector was looking for an optimal pricing model for its major clients who operate an extensive global network of cross border payments with a high transaction volumes of varied types. Solution: We generated an optimal pricing model that incorporates various parameters including expected and average transaction volumes and values, risk due to parameter uncertainty, cost structure, and the competitive environment. Technologies: Monte Carlo Simulations, Numpy, Matplotlib

Runtime improvement

Challenge: A Belgian insurance company faced a runtime issue with its Dynamic Financial Analysis tool. This tool was Microsoft Excel based including some VBA code. Solution: Given the client's technology stack and skills, we proposed a solution where the most intensive computation has been written in C++. We also leveraged parallel computing capabilities of the projections. Technologies: C++, Armadillo

Advanced text recognition and analysis techniques

Challenge: A startup, engaged in developing innovative accounting solutions, is seeking fiduciary support for managing smaller-scale independent clients. Solution: Advanced techniques in text recognition and analysis facilitate automated extraction of essential information such as price, supplier, and date, thereby reducing the time required for document processing. Continuous refinement of the algorithm, driven by user validation, enables further performance improvements. Technologies: OCR, Natural Language Processing, Machine Learning, Python, Web Development

Data quality improvement

Challenge: A French insurance company was looking to improve its data quality management for some of its business lines. Solution: A standardized process was designed, making use of data science techniques, including machine learning, and a web app was built to provide the business experts with a user-friendly and maintainable tool. Technologies: Python, Pandas, SciPy, Scikit-learn, Dash, GitLab

Solvency II process optimization

Challenge: An insurance company was looking to improve its Solvency 2 calculation process. The objective was to rationalize the number of tools, avoid duplication and reduce manual intervention in the process. Solution: We performed a deep analysis of the existing tools in order to identify the different improvement areas and suggest potential enhancement scenarios. Based on the client's preferred scenario, the corresponding improvements were then implemented. Technologies: Excel, VBA

Driving IT innovation in Finance

By blending strong actuarial and financial business expertise with an in-depth understanding of cutting-edge IT technologies, Reaxii enables you to become more competitive and focus on your core business such as complex analysis, strategic decision-making and innovation.

Through this actuarial engineering offer, we bring together actuarial expertise and financial mastery with cutting-edge technology

Needs assessment

We work closely with your departments to understand your needs and the challenges you face.

Technology selection

Together with your IT team, we select the technologies best suited to your existing infrastructure.

Technical expertise

We create robust solutions that remain within the control of your own teams.

Data quality improvement

Solvency II process optimization

Physical risk assessment

Pricing structure optimisation

Runtime improvement

Advanced text recognition and analysis techniques

Data quality improvement

Solvency II process optimization

Physical risk assessment

Pricing structure optimisation

Runtime improvement

Advanced text recognition and analysis techniques

Data quality improvement

Solvency II process optimization

Transform and innovate your processes to boost your productivity and gain a competitive edge through advanced AI-powered analysis tools tailored to your strategy.