Breaking down the impact of Causal AI on banking, insurance, and retail.

In today’s data-driven environment, the ability to understand the “why” behind AI-powered decisions is more important than ever. Business data contains significant information that empowers more than just predictions. Businesses can use their data to uncover the relationships within their data that can be used with predictions to take actionable steps toward improving their business’s ROI. Causal AI is emerging as the key to providing deeper insight into the hidden information in enterprise data, shifting the focus from obvious correlations to nuanced cause-and-effect, especially in industries like banking, insurance, retail, and financial services.

Howso is the technology at the forefront of delivering Causal AI. It allows businesses to unlock the power of explainable and Understandable AI®, making AI-driven decisions transparent and actionable. Through a combination of robust feature analysis, uncertainty quantification, and boundary case exploration, Howso transforms AI into a straightforward tool that not only predicts but explains the factors driving each decision.

Howso in Banking: Uncovering the Drivers Behind Customer Behavior

In the banking industry, understanding what drives customer behaviors—such as loan defaults or account closures—is crucial. Traditional AI models often work as black boxes, predicting that customers will default on their loans or close accounts, without providing any insight into why. This leaves decision-makers guessing about which factors matter most in these outcomes and asking “why will this outcome occur?” Howso’s glass box algorithm overcomes this challenge by providing robust feature contribution analysis, showing which variables significantly impact outcomes and highlighting any asymmetries in those contributions, indicating the directionality of an outcome, i.e., outcome Y occurred because of variables A and B.

For example, when analyzing loan default risk, Howso reveals that while income might generally reduce default risk, it may have a far larger impact on low-income borrowers compared to high-income ones and that another factor, such as payment history, contributes much higher to high-income defaults. By surfacing these asymmetries, Howso enables banks to take targeted, proactive actions, such as offering loan restructuring or financial counseling to at-risk customers.

Use Case: Loan Default Prediction

Problem: A bank wants to reduce its loan default rate by identifying customers at risk before they default.
Solution: Howso helps the bank identify key variables—such as income, payment history, and credit score—that most influence default risk.
Through its feature contribution analysis, Howso identifies the specific factors that differ between defaulting and non-defaulting customers, as well as boundary cases that represent customers on the verge of default. These insights allow the bank to take early action and prevent defaults.

Howso in Insurance: Detecting Fraud and Managing Risk with Causal Insights

In the insurance industry, detecting fraud and managing risk requires deep insights into patterns and anomalies. Howso provides insurers with the ability to uncover these patterns
by analyzing causal relationships in claim histories and risk factors. Unlike traditional models that merely identify correlations, Howso highlights feature contributions and influential, most similar, and boundary records that highlight why certain claims are more likely to be fraudulent.

Howso’s ability to integrate feature contributions with the analysis of similar cases enables insurers to isolate key risk factors and fraudulent indicators, such as unusual claim sizes or geographic inconsistencies. Additionally, Howso’s generative AI core offers deep insight into the uncertainties and reliability of fraud detection, providing confidence in the predictions. By surfacing these uncertainties, insurers can prioritize cases and adjust their strategies accordingly.

Use Case: Fraud Detection

Problem: An insurance company is struggling with a high volume of fraudulent claims.
Solution: Howso provides the company with robust feature contribution analysis, identifying key factors such as geographic anomalies or suspicious claim patterns.
The interplay of feature contributions and influential, most similar, and boundary case identification helps the insurer refine its fraud detection system, reducing false positives and making the detection process more efficient and accurate.

Howso in Retail: Understanding Customer Purchase Behavior and Optimizing Inventory

Retail is another industry where Causal AI offers significant advantages. In a fast-moving retail environment, understanding customer purchase behavior and inventory needs is critical for improving sales and minimizing waste. Howso can reveal the underlying causal factors that drive customer decisions, such as price sensitivity, product quality, or marketing campaigns.

For example, a retailer can use Howso to analyze customer segments and uncover why certain customers are more likely to make repeat purchases, while others may abandon their shopping carts. By understanding the causal relationships behind these behaviors, Howso enables retailers to optimize their marketing strategies and better target their customers.

Additionally, Howso can be applied to inventory management, where it helps businesses understand the causal factors that influence product demand fluctuations. For instance, seasonal trends, competitor actions, or even economic indicators might be the drivers of demand, and by identifying these factors, retailers can ensure they stock the right products at the right time, avoiding excess inventory or stockouts.

Use Case: Inventory Optimization

Problem: A retailer wants to optimize its inventory by understanding what factors drive demand fluctuations.
Solution: Howso analyzes historical sales data to identify key drivers of demand, such as seasonal trends, pricing strategies, or promotions. By providing insights into which factors contribute most significantly to changes in demand, Howso allows the retailer to adjust its inventory in real time, improving stock availability and minimizing overstock issues.

Causal AI in Time Series Data: Empowering Financial Forecasting with Causal Insights

For businesses working with time series data, such as financial forecasting or supply chain management, understanding the causal relationships between data points over time is essential. Howso excels in this domain by revealing the underlying factors that drive trends and changes in time-sensitive data, enabling more robust forecasting and strategy development.

By analyzing historical market data or operational trends, Howso provides actionable insights based on causal AI, rather than mere correlations. Additionally, Howso offers probability density functions around each prediction, providing a clear understanding of the uncertainties associated with forecasts. This allows businesses to not only make predictions but also to account for potential deviations and unexpected changes in the market or operational environment.

Use Case: Financial Market Predictions

Problem: A financial firm seeks to improve its trading strategies by understanding the causal factors behind market fluctuations.
Solution: Howso analyzes historical market data, identifying key periods where certain factors—such as interest rates or geopolitical events—had a causal impact on market movements. By comparing similar periods and analyzing the uncertainties in future market behavior, Howso helps the firm develop more reliable and robust trading strategies that consider the causal relationships driving market fluctuations.

Why Howso’s Causal AI Matters

In regulated industries like banking and insurance, as well as fast-paced environments like retail, understanding and explaining AI-driven decisions is becoming increasingly critical. Transparency and explainability are not just regulatory requirements—they are essential for building trust, making informed decisions, and utilizing valuable data to it’s true potential. Howso delivers on this need by making AI systems more transparent and understandable through Causal AI.

Howso goes beyond traditional black-box AI models by offering detailed insights into feature contributions, boundary cases, influential and most similar records, and uncertainty around each prediction. This ensures that every decision made by AI can be clearly explained and justified, providing businesses with the confidence to act on their data insights.

As businesses continue to embrace AI, the demand for causal AI solutions like Howso will only grow. By offering explainable, actionable insights, Howso helps organizations improve decision-making, reduce risks, and enhance overall performance. Whether it’s helping a bank prevent loan defaults, an insurer detect fraud, a retailer optimize inventory, or a financial firm optimize trading strategies, Howso is at the forefront of delivering transparent, Understandable AI® solutions that drive business success.

Howso enables businesses to not only make predictions but to understand the reasons behind them. With Causal AI at its core, Howso is transforming AI-driven decision-making into a process that’s both transparent and actionable, empowering organizations to leverage their data for better outcomes.

Try it out now with Howso Playground here.

• • •