In today's rapidly evolving financial sector, leveraging artificial intelligence is more than just a trend — it's a necessity. At Management Solutions, we stand at the forefront of this transformation, enabling financial institutions to harness the full potential of Artificial Intelligence (AI).


AI applied to the financial industry

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Our value proposition for the financial sector has four blocks:

  • AI adoption model: thanks to our understanding of the context, regulation and supervisory expectations in the financial industry, we offer a tailored approach that takes into account each institution's appetite for AI risk and guides them in developing a comprehensive AI diagnosis and adoption plan.
  • Data, infrastructure and architecture: we help institutions develop forward-looking technology roadmaps, leverage cloud services, and embrace the MLOps philosophy. By prioritizing data quality, traceability, and integration, we ensure that AI models are built on a solid foundation.
  • AI model development, validation and auditing: we assist in identifying the right use cases for AI, whether regulatory or non-regulatory. With a keen focus on model validation, compliance, interpretability and fairness, we ensure that AI models are not only effective but also ethical and safe.
  • Deployment and automation of models: we support institutions in automating their modelling and validation processes, and we do this by using our state-of-the-art proprietary tools – like ModelCraft™ for AutoML, Gamma™ for model governance, and Hatari™ for the interpretation of natural language.

All of this allows us to support financial institutions in the application of artificial intelligence in their daily processes, and specifically in numerous use cases such as:

  • Customer and workforce intelligence: applications to enhance the customer experience and customer journey with tailored marketing campaigns, chatbots, and sentiment analysis. This also includes understanding workforce dynamics better with AI-driven people analytics and CV screening.
  • Efficiency and operational enhancement: cross-cutting use cases to streamline processes with solutions for document handling, automated report generation, AI-driven data quality checks, code translation, or biometric authentication, among many others.
  • Risk and compliance: AI applied to financial risks, from credit granting to collections to market risk, and to non-financial risks, like fraud detection, cyber threats, and reputational risks – and also supporting financial institutions in the risk assessment of their use of AI.