causaLens logocausaLens logo alternate

Causal AI for Financial Services

Competing in financial services requires trustworthy technology. However, correlation-based machine learning techniques lack reliability, flexibility, and transparency. Leading wealth managers and market-makers trust the power of Causal AI in capital markets to augment their client analytics, market analysis, and decision-making. 

Top of mind for our customers operating in financial services

  1. Elevated fee pressure and fading client loyalty
  2. Lack of transparency creating model risk-management problems
  3. Heightened macroeconomic uncertainty and volatility.
  4. Optimizing portfolios from an ESG perspective


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.

  • Optimize resource allocation of sales and marketing budget.
  • Deploy causal analysis to understand the factors that causally drive your clients’ actions.
  • Incorporate domain expertise into causal models to supply business context and cost constraints.
  • Leverage external data and nowcasts to predict market disruptions and macroeconomic forces that influence clients’ behavior.

With Causal AI, wealth managers can identify the causal drivers behind their clients’ behavior, resulting in informed decisions, improved client loyalty, and growing assets under management.

Banks need to demonstrate that they have strong model validation in place to justify the use of complex algorithms by both internal auditors and 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.

  • Track causal relationships between ESG pillars and financial performance.
  • Recalibrate portfolio construction to optimize for ESG exposure and long-term growth without compromising expected returns.
  • Conduct stress testing for ESG risk via counterfactual analysis — a signature capability of Causal AI that allows the machine to investigate outcomes on which there is little or no historical data.

Trusted leader in the Industry

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.

Play video

Financial Services Pain Points

causaLens’ approach to the financial services industry biggest challenges:

Next-best-action in Wealth Management


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.

Data Search and Building New Products


causaLens empowers exchanges, data sourcing teams & data vendors to autonomously discover value in time-series data.


Current Approach and Pain Points

Standard AI techniques are confused by large amounts of intercorrelated and mismatched data.


Causal AI Solution

Only Causal AI allows users to extract valuable signals and build exciting data products that are easily monetized.

ESG Stress Testing and Risk Management

Track causal relationships between ESG pillars and financial performance. Ensure compliance with current and future regulatory requirements utilizing accurate scenario planning, stress testing, and reverse stress testing.

Current Approach and Pain Points

Linear models that draw on small data sets, offer limited and/or inflexible scenarios, and involve a lengthy implementation process.

Causal AI Solution

  • Accurate and explainable models that enable domain input from the start
  • Identifies and models non-linear causal relationships
  • Accommodates scenarios of any and all types and reverse stress testing
  • Model development to implementation up to 10X faster