The use of algorithm underwriting is increasing throughout the insurance industry. With enhanced decision-making and improved risk assessment, an algorithmic approach to underwriting can optimize the operation of insurance companies and customer experience.
In this post, we will explore the evolution and benefits of algorithmic underwriting and share insights into building and scaling algorithmic underwriting platforms.
evolution…
Algorithms have always been part of the underwriting process, but are generally limited to evaluation. For example, when determining risk factors for car insurance, algorithms, or mathematical formulas, it is used to set rates based on the vehicle, model, driver age, location, and previous history. Simple or complex, algorithms have long been our core evaluation tool.
The use of algorithms in other areas of the underwriting process is limited because they fear that they will overlap with these factors and rate creation, or simply overlap the lack of data and analytical capabilities in other parts of the underwriting process to make these decisions. Instead, the insurance industry typically relies on complex rules engines for risk acceptance, risk tier, and report order decisions.
With advances in data access and analytics tools, carriers are now rethinking the use of algorithms and using them alone or together with traditional rules engines to enhance decision-making throughout the underwriting process.
How it works…
Underwriting algorithms employs analytical models to automate decision-making in the underwriting process and provide insights to assist underwriters. For more uniform risk, it can be fully or partially automated underwriting.
Important decisions made using algorithm underwriting:
- Determine if the submission fits your career risk appetite
- Identify important risk characteristics such as the correct SIC/NAIC code
- Account prioritization based on desirability and win rate
- Determine risks by partial or risk overall
Through this approach, carriers can achieve faster risk acceptance or rejection and reduce their underwriting workload. It also helps to provide customers with more personalized risk assessment, real-time risk management and a seamless experience.
Five Benefits of Algorithm Underwriting
Underwriting algorithms brings great benefits to the insurance industry in five key areas.
- Process Efficiency: By automating the underwriting process, you can see that algorithmic underwriting reduces processing time by up to 50%, streamlines operations, speeds tests, and simplifies maintenance of complex decision-making systems. Additionally, an automated process of algorithm underwriting helps handle an increase in reviewed applications of up to 25%, allowing insurers to increase their premiums without additional operating costs.
- Accuracy: The accuracy of risk assessments can be improved through analysis of a wider dataset. These analyses help identify patterns and correlations that only human underwriters may miss. This enhanced underwriter insights and judgment makes it possible to minimize risk assessment errors and detect fraud more easily. Some insurers estimate that fraud losses could be reduced by up to 30%.
- price: By enhancing risk assessments, pricing decisions may be more accurate. Underwriting the algorithm helps to tailor premiums to individual risk profiles, increasing customer satisfaction and competitiveness. Additionally, it supports dynamic pricing and adjusts premiums in real time based on changing risk factors. This improves underwriting profitability by up to 20%.
- Proactive risk management: The algorithms help insurers proactively identify emerging risks and coordinate their underwriting and risk management strategies. This helps reduce potential losses, reduce loss rates, and improve overall portfolio performance.
- Customer Experience: Algorithm underwriting allows for immediate or proximity determination regarding coverage eligibility, pricing, and personalized offers. Predictive and normative analysis allows insurers to create real-time contextualized offers, making insurance more accessible and relevant to individual customer needs. Additionally, customers and segments that may be marginalized by past underwriting methods make insurance more suitable for insurance.
Building a large-scale algorithm underwriting platform
Algorithm underwriting platforms require a multi-layered approach that takes into account future scalability. Advanced features needed when considering an algorithm underwriting platform include machine learning models, real-time risk assessments, and dynamic pricing models.
Issues to consider when optimizing your data and algorithm underwriting platform:
- Data Quality and Availability: Data may be fragmented, incomplete or outdated.
- Model interoperability: The complex machine learning algorithms used for underwriting are not transparent and interoperable, and can be difficult to explain the results.
- compliance: As algorithmic models and AI regulations increase, insurers need to go ahead of guidance and adjust the models as needed.
- Fairness and bias: If not addressed actively, algorithm underwriting indicates the risk of perpetuating unfair practices and historical bias.
- Data Privacy and Security: Algorithm underwriting involves collecting, processing and storing large quantities of personal and sensitive data. Protecting customer data is essential to maintaining customer trust.
Success Story…
We look at examples of success involving algorithm underwriting across the industry. For example, in P&C KI Insurance Exploit AI and algorithms for immediate commercial insurance estimates and issuance of automated insurance contracts. Hiscox We have worked with Google Cloud to develop an AI model that automates the underwriting of a particular product. On the other hand, on the life insurance side, spirit It employs machine learning to guarantee risk and provide a simplified insurance application.
Conclusion
Algorithm underwriting is not a novel concept in insurance, but it is innovative in accessing new data sources, improving data quality, and enhancing access to better analytical tools. These enhancements allow underwriters to insight from other areas of the value chain, allowing them to extend capabilities beyond archaic models and knockout rules.
Despite its refinement, insurers should be aware of the possibility of bias and the lack of transparency in their algorithmic underwriting models. Ethics and compliance, such as data privacy, consumer protection, and fair lending laws, pose challenges that insurers will address from the start.
As technology continues to evolve and data analytics capabilities expand, we witness how algorithmic underwriting will revolutionize the insurance industry, drive innovation and financial institutions make more informed, data-driven decisions.
