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Experian Convex | Use Cases | Insurance
Insurance companies can benefit from Convex to ensure operational efficiency, healthy portfolio management, claim management, correct marketing and fraud management.
Available Use Cases
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Insurance
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Improve operational efficiency
Challenges
- Lengthy processes
- Slow decision-making
- Infrequent client touch-points
- Siloed customer information
- One-fits-all client experience
Opportunities
- Leverage AI to predict whether each application should be approved, rejected, or manually reviewed and even manual review team (branch, region, head office)
- Configure different levels of automation or AI assistance for different segments, sectors, products and credit levels
- Guide credit origination teams to most important factors influencing credit level definition and automation
Claim probability estimation
Challenges
- Traditional, manual or rule-based systems to understand the client's claim probabiliy
- Slow processing and poor customer experience
- No risk based pricing based on the risk of the client
Opportunities
- AI is ideal for anomaly detection which helps to capture unusual events in the behaviour of the customer
- Convex can streamline processing by scoring claims for issues like fraud and allowing claims with low probability of an issue to be processed automatically while higher probability claims are routed to investigators for review.
- Convex can help to explain the reasoning behind the scoring thanks to Machine Learning Explainability algorithms (Shap values)
Pricing
Challenges
- One-fits-all pricing for each customer
- Lack of competition in the market for pricing
- Loss of price-sensitive customers due to pricing
- Loss of revenue for non-sensitive clients
Opportunities
- Develop pricing models with the best-in-class AI algorithms
- Differentiate pricing for each segment, product and customer
- Increase through-the-door customer flow with the campaign prices
- Increase revenue with the right pricing
Churn prediction
Challenges
- Loss of customers without prior notice
- No action for the customers based on their leaving probability or value to the company
Opportunities
- Identify customers who tend to leave in the near future with the AI algorithms
- Take actions to retain valuable existing customers based on churn prediction models
- Maintain revenue from the existing portfolio
Propensity prediction
Challenges
- Limited number of products and limited revenue from the customers
- No proactive cross-sell or up-sell actions
- One-fits-all product campaigns
Opportunities
- Identify customers who tend to buy additional products or increase usage in their existing products
- Take proactive cross-sell or up-sell actions to get share in the market for specific products from the valuable customers
- Grow the portfolio and product coverage
Segmentation
Challenges
- Lack of granular customer segments
- Traditional customer segmentation without analytical methods
Opportunities
- Granular customer segmentation including risk segmentation or marketing segmentation concepts utilizing advanced clustering AI algorithms
- Customized customer management or risk activities based on customer segments
Fraud
Challenges
- Lack of strong fraud models
- Lack of adequate number of fraud events in the data
Opportunities
- AI is ideal for anomaly detection which helps to capture unusual events in the behaviour of the customer
- Fraud data events can be increased within the portfolio sample with oversampling, undersampling or class weight features