Strict regulatory requirements, record low interest rates and increasing competition from fintechs are some of the challenges that are placing pressure on large financial institutions to be more agile and adapt. But current machine learning approaches lack adaptability and fail to meet expectations of transparency.
With Causal AI , banks can build explainable models that unlock the potential in their data, empowering them to be more efficient and flexible.
We understand the time, effort and scrutiny required for models to be deployed within large financial institutions. With causaLens, model developers can spend less time writing reports and more time deriving insights from the analysis. A lot of our technology goes into ensuring that all models, no matter how complex, are fully explainable, with automatic model stability and performance analysis. Furthermore, causaLens models can be automatically reported in templates compatible with SR 11-7 requirements.
Our scientists have extensive experience in finance and will assist with any internal or external audit queries, as well as in developing models and reports tailored to CCAR, ICAAP, DFAST, EBA Stress Test and any other regulatory stress-testing exercise.
Better servicing clients is at the core of what makes banks succeed. From retail to PWM, having a data-driven understanding of what customers want and how they respond to changes is the key to strengthening that relationship.
causaLens has a proven track record of helping traders in various institutions improve their performance. From tick-level order book strategies to long-term macroeconomic forecasts, causalAI has been shown to outperform traditional machine-learning and deliver an extra edge.
Many institutions have already adapted machine learning as a way to improve the hedging of their derivatives book. Using causaLens, desks can hedge their exposure faster, more accurately, while at the same time optimizing the strategy to minimize costs — allowing them to quote larger volumes.
With its ups and downs, the IPO and M&A markets have been transformative in the last few years. As private markets continue to occupy a bigger share of the economy, investors are looking for more quantitative, data-driven insights — combining traditional and alternative sources of data. With causaLens, analysts can augment their projections with strong forecasts based on Causal AI, gaining an edge over the competition.
causaLens can empower 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 will use large and varied datasets to judge a counterparty’s credit risk. Liquidity outflows can be modeled using causal methods, disregarding spurious correlations in data and producing more generalizable models.
causaLens can help market 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. causaLens can help banks 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 out-of-sample, and may use proxy variables that are discriminatory in nature.
The causaLens platform has enabled us to discover additional value in our data. Their causal AI technology autonomously finds valuable signals in huge datasets and has helped us to understand relationships between our data and other datasets.”
causaLens provides us with key causal insights that continuously unlock untapped value in our data.”