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Banks are adopting AI to solve their most challenging problems — from risk management to marketing optimization. However they are disappointed with the failures of correlation-based machine learning technology, which is often confined to automating back office administrative tasks. Causal AI inspires the trust needed to unlock the true value of AI in banking.


Top of mind for our customers in banking

  1. Competition from fintechs and new entrants
  2. Risk management under extreme uncertainty
  3. Increasing regulatory pressures
  4. AI systems that pose risks, lack transparency, and break during crises


Servicing clients is at the core of what makes banks succeed. From retail to personal wealth management, having a data-driven understanding of what customers want and how they respond to changes is the key to strengthening that relationship. Causal AI identifies what really makes your customers tick, and recommends the most cost-efficient actions to improve client relationships. 

Causal AI empowers risk managers with a new class of models that uncover deeper structures within the data. Credit analysts can have their domain expertise augmented by Causal AI, which can judge a counterparty’s credit risk. Liquidity outflows can be modelled using causal methods, disregarding spurious correlations in data, and producing more generalizable models. Causal AI helps risk managers produce a more holistic view of current market movements beyond correlation analysis, identifying regime changes and understanding how the market behaves in each regime.

Compliance and operational risk events have become a significant source of losses for large banks today. With Causal AI, banks can identify and mitigate these risk events. Our technology automatically infers the causal drivers that led to these errors, and works with compliance managers to develop the most cost-effective solutions to prevent these losses in the future.

Forecasting lending losses and setting credit limits is a data-intensive problem that is riddled with confounders and spurious correlations. Current machine learning techniques produce models that fail whenever there are regime changes, do not generalize well to new settings, and may use proxy variables that are discriminatory in nature. Causal AI makes far more trustworthy lending recommendations with better outcomes. 

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