The Evolution of Commercial Underwriting
In an industry issues roundtable, NeuralMetrics chief technology officer, Sathish Kumar Manimuthu, chief underwriting officer, Chris Schrenk, and senior product manager, Neha Yadav, discuss how underwriters can benefit from artificial intelligence (AI) and cognitive technologies – to improve risk assessment and pricing accuracy, accelerate quoting, and enable data provenance for consistent regulatory compliance.
Q: What value does the latest generation of technology bring to underwriting?
Sathish Kumar Manimuthu (SK): Insurance organizations are increasingly embracing the digitization of commercial underwriting, recognizing that outdated manual processes and legacy systems are insufficient to compete in the modern economy. Innovations in underwriting technology are well underway. A prime example is the use of AI and the machine learning capabilities of large-language models to combine human expertise, data, and automation for faster and more accurate commercial risk evaluation.
Collecting timely and comprehensive exposure information is a critical aspect of insurers’ operations, as it informs various insurance processes to facilitate precise risk assessment, as well as policy pricing, and quoting. With the aid of AI and machine learning, insurers can now leverage potent analytic tools to transform a wide range of data sources into actional underwriting insights.
Neha Yadav (NY): Insurance organizations are continuing to streamline underwriting processes, enhance the accuracy and transparency of data sources, and differentiate their offerings with exceptional agent and policyholder experience during submission interactions, as well as policy and claims services. In pursuit of those objectives, they are exploring the power of intelligent technology to deliver dependable and validated data and analytics for underwriting and other insurance workflows.
In commercial underwriting, gathering expedient data can be time-consuming, and often involves meticulous but time-consuming research into a range of factors such as the location, surroundings, on-site or remote property inspection, business practices, and more. Now, with just a business name and address, intelligent technology and its abilities to obtain a wide range of data sources allow underwriters to quickly access extensive layers of risk information about insurable and in-appetite entities.
Q: What modern technologies can enrich underwriting capabilities and deliver a superior policyholder experience?
SK: At NeuralMetrics, we specialize in advanced technologies such as artificial intelligence, natural language processing, vectors, computational graphs, and other cutting-edge tools to extract reliable and qualitative risk insights from small data. Through these methods, we establish meaningful connections among and within a range of public data, enabling us to derive real-time actionable intelligence for underwriters. We can tap into extensive sources of structured and unstructured data, a significant portion of which is publicly available and can be curated, organized, and tailored specifically for underwriters. AI-powered data engines from NeuralMetrics allow insurance organizations to strengthen industry classification, precisely identify risk attributes, and makes information decisions about expanding or competing in various business sectors.
Chris Schrenk (CS): Having a comprehensive underwriting system and fresh, contextual data is vital for insurers to evaluate commercial business quickly and efficiently. By presenting all the required data about the business in one central location, underwriters can make informed decisions without having to access multiple systems or scour the internet for information. Consolidation of data streamlines the decision-making process, resulting in improved overall efficiency across underwriting operations.
For insurers, it is crucial to work with technology and data partners who can tailor their platforms to specific business needs. Third-party platforms and solutions should be flexible and responsive in real-time, and should reinforce current processes, while being adaptable in expanding data requirements for improved risk evaluation. It’s especially important for data engines to provide transparency into the data sources used for risk assessment, and such platforms should be easily customizable to conveniently accommodate new or additional classification and exposure questions from underwriting teams. A knowledgeable technology partner should offer practical and valuable suggestions to swiftly advance underwriting proficiency beyond the scope of traditional risk attributes and evaluation methodologies.
SK: Furthermore, it is imperative for insurer systems and data platforms to communicate seamlessly, boosting the policyholder experience. Insurance organizations often have separate systems for evaluating risk, administering policy service, conducting premium audits, submitting claims, and more. If interoperability is constrained or unreliable and data is scattered and replicated the result is layers of inefficiency, inaccuracy, and cost in care workflows. For example, in the commercial business sector, policyholders may need to be contacted and required to provide the same information multiple times within the same policy period.