While many organizations have some type of rules-based underwriting platform in place, handling mostly lower-risk cases, AI has the potential to “supercharge” underwriting operations. Let’s review the differences between rules-based underwriting and Artificial Intelligence (AI)-assisted underwriting.
Rules-Based Underwriting began to transform the insurance industry 10-15 years ago. Rules-based underwriting involves the application of predefined rules and decision-making criteria to assess the risk of potential clients and determine their eligibility, or pricing level, for a variety of insurance types. With this approach, underwriters set explicit guidelines and the system follows a structured process to evaluate applicants, sometimes without the need for underwriter intervention or review. The rules-based system adheres strictly to these predetermined rules when analyzing factors such as credit scores, IT infrastructure, motor vehicle records, electronic health records, income levels, and other relevant data.
Rules-Based Underwriting Benefits:
Transparency: The decision-making process in Rules-Based Underwriting is explicit, transparent, and easily interpretable, as it follows pre-established rules.
Consistency: The application of consistent rules ensures a standardized approach to underwriting, reducing bias and ensuring equitable treatment of applicants.
Compliance: Rules-Based Underwriting can aid in complying with industry regulations and company policies when these are explicitly encoded into the decision-making process.
Rules-Based Underwriting Challenges:
Limited Adaptability: Rules-Based Underwriting may not account for subtle or emerging patterns in data, making it less adaptable to changing market conditions or unusual situations.
Manual Rule Updates: Any modifications or additions to underwriting rules require manual intervention, which can be time-consuming and resource intensive.
Risk of Omissions: Rigid rules may overlook certain nuances and context-specific variables that could affect the accuracy of risk assessments.
Learning-Based Underwriting AI addresses some of the challenges of Rules-Based Underwriting. Learning-Based Underwriting AI is applied to the top of the underwriting funnel and leverages machine learning algorithms to automatically identify patterns and relationships within historical data. The AI model learns from vast datasets, continuously improving its risk assessment capabilities by adapting to new information and market dynamics.
Learning-Based Underwriting AI Benefits:
Adaptability: Learning-Based AI Underwriting can respond to changing market conditions, learning from new data and adjusting risk assessments accordingly.
Improved Accuracy: Machine learning models can consider a broader range of variables and interactions, potentially leading to more accurate risk predictions.
Automation: Once the model is trained, it can operate autonomously, significantly reducing the need for manual intervention in routine underwriting tasks.
Learning-Based Underwriting AI Challenges:
Complexity: Implementing Learning-Based AI Underwriting requires expertise in machine learning, data science, and computational resources for training and validation.
Black Box Nature: As models become more complex, understanding the reasoning behind specific decisions can be challenging, raising interpretability concerns.
These are just some of the benefits and challenges that are associated with Learning-Based Underwriting AI. When considering a technology partner to help you analyze, launch and monitor AI-based underwriting, be sure that they have the infrastructure and underwriting toolkit to skillfully guide you through the process. DRC helps insurers focus on where AI can have the most impact with the least disruption within your organization.
