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Financial Services

Traditional, correlational, Machine Learning approaches are often not sufficiently trusted or capable enough to address some of the most crucial questions across the customer journey, credit portfolio, marketing, model risk, insurance claims, insurance policies, AI regulations as well as centralised planning & strategy

Traditional machine learning approaches often fail to address critical business questions

Correlation, not causation

Spurious correlations lead to bad decisions


Read more on our blog:
How can AI discover cause and effect

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They struggle to answer why

And are often perceived as “black boxes”


Read more on our blog:
Explainable AI (XAI) doesn’t explain enough

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Prediction, not next best action

For example, they can predict if a customer will churn or not but can’t recommend the optimal next best action to retain the customer

Read more on our blog:
From predicting to Influencing

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Do these questions sound familiar?

Financial Services run on causal questions

Customer Journey
  • What are the best actions (interventions) to retain or up-sell my customers?
  • How do I best price my products to optimize for market share or profitability?
  • How does my competitor’s behavior impact churn and customer lifetime value?
Credit Portfolio
  • How do interest rates, inflation, FX rates, and other external drivers impact the creditworthiness of my customers?
  • What are the causal drivers of credit risk?
  • What are the best actions (interventions) for loans that are defaulted or about to default?
  • How should I rebalance my portfolio to optimize risk-adjusted returns?
Marketing Optimization
  • Based on my budget, what is the optimal allocation of investment across channels (e.g.: media, digital, and mail)?
  • How well did my last marketing campaign perform? Why?
  • What are the causal drivers of campaign performance?
  • What are the best interventions to improve campaign performance?
Model Risk
  • Has the model accounted for confounders?
  • Does the model include any spurious correlations?
  • Is the model affected by Simpson’s Paradox? (Does the full population relationship hold for subpopulations?)
  • How would the model behave in unseen scenarios that I haven’t observed before?
  • Is my model biased towards gender, race, and other protected classes?
  • Is my model fair, and can it withstand the scrutiny of any external audit?
Insurance Claims
  • What are the root causes of claims costs?
  • What is the causal impact of inflation on claim costs?
  • Does a partnered network (e.g. garages) help reduce claim costs? Which claims are they most effective at reducing?
  • What is causing the claim in our policy network? Can we reduce these with physical features or training?
  • What is the causal impact of claims experience on renewal?
  • Would auto-approving all claims vs the cost to investigate fraud be beneficial?
Insurance Policies
  • What is the optimal price for our policy?
  • What happens if I offer a rewards programme?
  • What is our policies’ true price elasticity?
  • What policy would be most relevant to a potential client?
  • How can I fairly approve Life & Health policies?
  • What is the causal impact of market conditions on demand for policy?
Centralized planning & strategy
  • How do interest rates and inflation dynamics affect the risk profile of the entire portfolio?
  • How will the competitive, market, and macro dynamics impact my PnL?
  • How will decisions in pricing, marketing or risk management affect the PnL and the risk profile of the entire portfolio?

See Our Solutions in Practice

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Leverage decisionOS

the first operating system for decision making powered by Causal AI, to address all those causal questions

Causal AI

To move beyond traditional ML and into a world where you can provide actionable recommendations by leveraging state of the Causal AI tools and methods.

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DecisionApp Building

Seamlessly surface recommendations to your business partners as expressive, tailored and interactive applications focused on decision-making.

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For the deployment and monitoring of decision workflow, trusting those workflows in production and measuring the causal impact of your decision-making.

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Trusted by leading organisations

Causal AI plays an important role in our investment analysis. It empowers out strategists and portfolio manager to generate alpha by identifying new causla relationships in economic, financial and alternative data, with sophisticated, adaptive and explainable models that don’t suffer from overfitting.

Michael Grady, Head of Investment Strategy & Chief Economist

Transparency & explainability of AI models requires an understanding of causality – an inherent advantage of the causaLens platform.

Wendy Harrington, Chief Data & AI Officer




The causaLens platform has empowered us to create powerful and beautiful decision applications that we rely on for critical decisions.

Gerald Mullaly, Director




Causal AI at Scotiabank

Watch the talk from the Causal AI Conference 2023

Case studies

Customer Case Study: Deposit Modeling

A large European bank with over €400bn in aggregate deposits wanted sees a return of €12mn using decisionOS to better understand and manage deposit risk.

Customer Case Study: Client Retention

North American pension plan improved beneficiary satisfaction and increased retention by 17% using decisionOS powered by Causal AI

Customer Case Study: Marketing Mix Modeling

A leading Mobile App company sees a projected 15x ROI through a reduction of 5% in annual marketing spend using decisionOS to optimise marketing allocation

Use Cases

Customer Retention

Marketing Mix Modeling

Marketing leaders use Causal AI to improve their marketing mix, attribution, and modeling. decisionOS is the leading Causal AI platform used by marketers globally.

Pricing and Promotion

Enterprises use Causal AI to optimise their pricing and promotion strategies. Traditional ML approaches aren’t sufficient, see why Causal AI could be the answer

Proven value in weeks

  • 1 Icon
    Internal meeting

    One hour

  • 2 Icon
    Scoping sessions

    Two to three hours

  • 3 Icon
    Platform Trial

    Three to four weeks

  • 4 Icon

    Twelve months

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