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
Spurious correlations lead to bad decisions
And are often perceived as “black boxes”
Read more on our blog:
Explainable AI (XAI) doesn’t explain enough
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
Do these questions sound familiar?
Financial Services run on causal questions
See Our Solutions in Practice
Visit Demo HubLeverage 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.
Learn moreDecisionApp Building
Seamlessly surface recommendations to your business partners as expressive, tailored and interactive applications focused on decision-making.
Learn moreDecisionOps
For the deployment and monitoring of decision workflow, trusting those workflows in production and measuring the causal impact of your decision-making.
Learn moreTrusted 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
Proven value in weeks
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1Internal meeting
One hour
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2Scoping sessions
Two to three hours
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3Platform Trial
Three to four weeks
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4Production
Twelve months
Start your Causal AI Journey
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