How can you use natural language processing to extract insights from unstructured data in insurance?

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Unstructured data, such as text, images, audio, and video, can provide valuable insights for insurance companies, but they are often difficult to analyze and process. Natural language processing (NLP) is a branch of artificial intelligence that can help extract meaning, sentiment, and patterns from unstructured data using various techniques and tools. In this article, we will explore how you can use NLP to enhance your insurance business in four areas: risk assessment, pricing models, customer service, and fraud detection.

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Prabhu Stanislaus

RheinBrucke IT Consulting | Generative AI | ADMS | Surround IT | Corporate IT Training
Gabriela Morandini

Senior+ Computational Linguist en emergya
Karen Saturday, MBA

AVP, Program Claims Director, Falls Lake Insurance Companies

1 Risk assessment

One of the main challenges for insurance companies is to assess the risk level of potential or existing customers based on various factors, such as their health, lifestyle, driving behavior, or credit history. Unstructured data, such as medical records, social media posts, or telematics data, can provide more granular and accurate information about these factors, but they need to be processed and interpreted by NLP algorithms. For example, NLP can help extract relevant information from medical records, such as diagnoses, symptoms, treatments, or prescriptions, and use them to calculate the risk score of a customer. NLP can also help analyze social media posts or telematics data to infer the customer's habits, preferences, or driving style, and adjust the risk assessment accordingly.

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Karen Saturday, MBA

AVP, Program Claims Director, Falls Lake Insurance Companies
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NLP offers the claims professional an opportunity to co-create a fuller picture of their clients as they use their experience and insights to determine which data to extract. This allows them the opportunity to "fill in the blanks," and develop a fuller picture of the subject. NLP has the potential to serve as a value-add to the everyday activities of the claims professional.

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Prabhu Stanislaus

RheinBrucke IT Consulting | Generative AI | ADMS | Surround IT | Corporate IT Training
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In Risk Assessment, NLP aids insurance firms through Text Mining and Sentiment Analysis. Text Mining extracts insights from diverse data sources like medical records or accident reports to assess associated risks. For example, analyzing accident reports helps understand risk levels of certain demographics or areas. Sentiment Analysis, on the other hand, gauges public perception on platforms like social media, providing a broader understanding of market sentiment and identifying potential risks. Real-time reactions to new policies or claims can also be monitored, offering an immediate feedback loop invaluable in risk management.

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2 Pricing models

Another challenge for insurance companies is to design pricing models that reflect the risk level and the value proposition of each customer segment. Unstructured data, such as customer feedback, reviews, or surveys, can help insurance companies understand the needs, expectations, and satisfaction of their customers, and use them to optimize their pricing models. NLP can help analyze the sentiment, tone, and topics of customer feedback, and use them to identify the key drivers of customer loyalty, retention, and churn. NLP can also help segment customers based on their feedback, and offer them personalized and dynamic pricing options that match their preferences and behaviors.

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Prabhu Stanislaus

RheinBrucke IT Consulting | Generative AI | ADMS | Surround IT | Corporate IT Training
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Predictive Modeling: Key to insurance pricing, predictive models are enhanced by NLP, analyzing unstructured data to forecast claims likelihood. Text analysis from claims or interactions identifies factors linked to higher claims, with insights integrated into pricing models, better reflecting risks across insurance products or customer segments. Trend Analysis: NLP aids in market trend analysis by processing text from news, blogs, social media, yielding insights into market dynamics and emerging risks, essential for adjusting pricing models. For instance, analyzing discussions on natural disasters or accidents provides real-time emerging risk insights, enabling insurance firms to modify pricing models accordingly.

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3 Customer service

Customer service is a crucial aspect of insurance business, as it can affect the customer experience, retention, and reputation of the company. Unstructured data, such as customer queries, complaints, or requests, can help insurance companies improve their customer service quality and efficiency, but they need to be handled and resolved by NLP systems. For example, NLP can help create chatbots or virtual assistants that can understand and respond to customer queries in natural language, and provide them with relevant information, solutions, or recommendations. NLP can also help automate the processing of customer documents, such as claims, policies, or contracts, and extract the key information, terms, and conditions from them.

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Gabriela Morandini

Senior+ Computational Linguist en emergya
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O processamento de linguagem natural pode, inclusive, automatizar processos inteiros que s?o comuns no atendimento na área de Seguros. Os assegurados podem, por exemplo, abrir incidências sem nem precisar esperar pelo atendimento humano, apenas descrevendo a um assistente virtual cada uma das informa??es necessárias para reportar a situa??o. Se conectado aos sistemas de gest?o da seguradora, essas informa??es podem ser repassadas à empresa por meio do chatbot e o usuário pode receber a confirma??o de que seu caso foi registrado e será tratado.

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4 Fraud detection

Fraud is a major problem for insurance companies, as it can cause significant losses and damage their reputation. Unstructured data, such as claim documents, photos, or videos, can help insurance companies detect and prevent fraud, but they need to be verified and analyzed by NLP techniques. For example, NLP can help compare and contrast the information from claim documents with other sources, such as public records, databases, or social media, and identify any inconsistencies, anomalies, or discrepancies. NLP can also help recognize and classify the features, objects, or events from photos or videos, and use them to validate or invalidate the claim.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can you use natural language processing to extract insights from unstructured data in insurance?

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1 Risk assessment

2 Pricing models

3 Customer service

4 Fraud detection

5 Here’s what else to consider

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