Transforming Financial Services with Causal AI

The Causal AI approach:

Causal AI identifies the best actions to retain and up-sell customers, optimizes pricing strategies for market share or profitability, and analyzes competitor behaviour to understand its impact on churn and customer lifetime value. This approach enables precise, data-driven interventions that enhance overall business performance.

Why this matters:

Understanding the drivers of customer retention, pricing effectiveness, and competitor impact is crucial for maximizing profitability. By leveraging Causal AI, businesses can implement targeted actions that improve customer loyalty, optimize pricing strategies, and enhance competitive positioning, leading to sustained growth and success.

Other example questions:

• 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?

The Causal AI approach:

Causal AI identifies the impact of interest rates, inflation, FX rates, and other external drivers on customer creditworthiness, and determines the causal drivers of credit risk. It suggests the best interventions for defaulted or at-risk loans and guides portfolio rebalancing to optimize risk-adjusted returns. This approach enables precise, data-driven decisions for effective risk management.

Why this matters:

Understanding the factors influencing credit risk and portfolio performance is crucial for financial stability and profitability. By leveraging Causal AI, financial institutions can implement targeted actions that mitigate risk, enhance credit management, and optimize returns, ensuring a more resilient and competitive operation.

Other example questions:

• 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?

The Causal AI approach:

Causal AI identifies the optimal allocation of marketing investments across channels based on budget, evaluates past campaign performance, and determines the causal drivers of success. It provides actionable insights for precise interventions to enhance future campaign effectiveness.

Why this matters:

Understanding the causal drivers of campaign performance and optimal investment allocation is crucial for maximizing marketing ROI. By leveraging Causal AI, businesses can implement targeted actions that improve campaign outcomes, increase customer engagement, and optimize budget utilization, leading to sustained growth and competitiveness.

Other example questions:

• 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?

The Causal AI approach:

Causal AI assesses whether the model accounts for confounders, avoids spurious correlations, and is unaffected by Simpson’s Paradox. It evaluates the model’s behavior in unseen scenarios and ensures it is unbiased and fair towards protected classes. This approach provides insights into the model’s robustness and reliability.

Why this matters:

Ensuring that a model is robust, unbiased, and reliable is essential for making credible and fair data-driven decisions. By leveraging Causal AI, organizations can verify that their models perform accurately across diverse scenarios, uphold fairness, and withstand external scrutiny, ensuring integrity and trust in their analyses.

Other example questions:

• 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?

The Causal AI approach:

Causal AI identifies the root causes of claims costs and evaluates the impact of inflation and network partnerships on these costs. It analyzes the effectiveness of physical features or training in reducing claims and assesses the causal impact of claims experience on renewals. Additionally, it evaluates the trade-off between auto-approving claims and investigating fraud.

Why this matters:

Other example questions:

• 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?

The Causal AI approach:

Causal AI determines the optimal price for policies and evaluates the impact of offering a rewards program. It assesses the true price elasticity of policies and identifies the most relevant policies for potential clients. Additionally, it examines the causal impact of market conditions on policy demand and supports fair policy approval processes.

Why this matters:

Other example questions:

• 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?

The Causal AI approach:

Causal AI analyzes how interest rates, inflation, and other market dynamics impact the risk profile of a portfolio. It evaluates the effects of competitive, market, and macroeconomic factors on P&L and assesses how decisions in pricing, marketing, and risk management influence both P&L and portfolio risk. This approach ensures informed, data-driven decision-making.

Why this matters:

Other example questions:

• 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?