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Causal AI for Financial Markets

Competing in financial markets requires trustworthy technology. However, correlation-based machine learning techniques lack reliability, flexibility and transparency. Trading desks, institutional brokerages and market-makers trust the power of Causal AI to augment their decision-making, implement successful strategies and achieve better execution.

Top of mind for our customers operating in capital markets

  1. Extreme competition, with market participants competing for liquidity and best execution
  2. Flawed machine learning technology, generating strategies with very short half-lives 
  3. Pressure to automate trading strategies and improve client relationships
  4. Heightened macroeconomic uncertainty and volatility.

Solutions

Causal AI empowers front-office teams with a new class of models that uncover deeper structures within the data. Business outcomes (whether to hedge a trade or where to execute) can be modeled using causal methods, disregarding spurious correlations in data, and producing more generalizable models. Causal AI helps traders produce a more holistic view of current market movements beyond correlation analysis, identifying regime changes and adjusting behavior in each regime.

Servicing clients is at the core of what makes banks succeed. From institutional clients  to personal wealth management advisory, 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 those relationships.

Banks need to demonstrate that they have strong model validation in place to justify the use of complex algorithms both internally and to external regulators. With Causal AI, banks can automatically infer the causal drivers that led to model predictions, and work with compliance managers to develop the most cost-effective solutions to make sure that these predictions are fair and in line with expectations.

Systematic Trading

Causal AI examines entire dynamic systems to understand the true causal relationships that drive change – relationships that cannot be found with correlation-based techniques. These new angles can be employed in signals research, execution and risk management to create investment opportunities that persist and are robust to unexpected regime changes.

causaLens combines up-to-date academic methods with proprietary R&D from a leading causality research lab. The platform fits easily into existing pipelines, allowing systematic investors to move ahead of existing approaches by applying causal techniques across the business.

High Frequency Trading & Market Making

causaLens can be integrated directly into any pipeline to optimise high-frequency trading strategies, trade execution, and market making. Designed for high-throughput online time series prediction, our models continuously adapt in real time to discover the current causal drivers in the market and produce the most efficient execution.

Data Search & Building New Products

causaLens empowers exchanges, data sourcing teams & data vendors to autonomously discover value in time-series data. Standard AI techniques are confused by large amounts of intercorrelated and mismatched data; only Causal AI allows users to extract valuable signals and build exciting data products that are easily monetized.

AI Capital Markets Can Trust

causaLens co-founders Darko Matovski and Maxim Sipos discuss why current AI fails and how Causal AI unlocks superior decision-making, in our Capital Markets webinar. Applied Data Science Director Andre Franca presents some causaLens success stories.

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Capital Markets' Pain Points

causaLens’ approach to the capital market industry’s biggest challenges

Capital Markets and Wealth Management

Objectives
To get relevant client segmentation and next best action for either institutional or individual clients with each suggestion being explainable, transparent and justifiable.

Current Approach and Pain Points
Based on basic filters without any predictive power.

Causal AI Solution

  • Highly personalized and directly relevant updates based on explainable models
  • Tailored updates increase PnL by improving customer engagement, building loyalty and encouraging additional product uptake.

Treasury

Objectives
To improve liquidity, asset and liability management by establishing the correct balance between risk, regulations and profit opportunities.

Current Approach and Pain Points

Spreadsheets and heuristic models based on risk appetite for liquidity coverage ratio and net stable funding ratio, portfolio level aggregations and simple intraday liquidity management. Cash may not be utilized as efficiently as it could be and risks may be heightened.

Causal AI Solution

  • Develops finely detailed models from customer cohort to portfolio level
  • Provides accurate forecasting of operational balances, price sensitivity and volatility for risk management and optimizes and extends asset and liability management
  • Enhances PnL

Model Risk

Objectives
To comply — as speedily and as efficiently as possible — with existing and evolving regulations (including the new EU AI regulations) for model risk and AI, taking account of the increased emphasis on explainability, stability, fairness, bias and trust.

Current Approach and Pain Points
Control and process framework involving manual reviews and expensive iteration process between data science team, business units and model risk team. A high reliance on SHAP and/or Lime for explaining outcomes or limited to linear models only. Ill-prepared for either the new EU AI requirements or evolving US and Singapore regulatory specifications.

Causal AI Solution

  • CausalOps model risk framework — risk assess and evaluate existing models, testing features, causality, explainability, bias, fairness, sensitivity and robustness to change
  • A user interface that accelerates model review and acceptance by allowing business, DS and model review teams to collaborate and test iterations in one session
  • Accelerate end to end model adoption speed by 30X