The Power of AI in Insurance: Existing Opportunities and Upcoming Trends

The Most Effective AI Use Cases in Insurance

The complexity of today’s insurance tasks is too much for traditional technology. This is why innovative solutions are needed to create customized plans, automate tasks like payroll, invoicing, and fraud prevention. However, AI-driven Insurtech development services can change the way insurance companies operate. Below we describe how the companies from the insurance industry can leverage the power of AI for streamlining their business processes.

AI for Insurance Companies Product Management

Insurance policies can become much more personalized when machine learning handles customer demand, sentiment, and potential insurance scenario analysis. ML can assess the following data to satisfy the individual demands of different consumer groups: 

• Real-time data, such as location or weather patterns; 
• Consumer data, such as demographics, preferences, and behavior;
• Historical data, such as claims or insurance cancellation reasons.

Natural language processing (NLP) also plays its role in crafting better insurance policies. This technology can extract data from chats, emails, surveys, and reviews so that later analyze it and understand the demand or sentiment concerning specific insurance products.

Serhii Leleko

AI & ML Engineer at SPD Technology

“Fair and accurate pricing for insurance products is also a decision-making point when presenting those products to customers. And we can actually achieve a great level of price fairness with deep learning models. They have the ability to adapt, when new data is fed to them. This enables dynamic pricing, where market conditions and customer behavior that change rapidly can be taken into account.”

Marketing and Sales

Machine learning techniques allow analyzing vast volumes of customer data, including location, demographics, online behavior, and purchase history to craft targeted marketing campaigns. Plus, there are scoring AI models that make it possible to analyze lead data from any campaign and see what actually triggered a lead to engage with a specific insurance company. This can help correctly allocate sales efforts. 

Another tool that is incredibly helpful for insurance marketing campaigns is generative AI. With its assistance, companies can create unique emails, social media posts, and ad copies. Additionally, companies can personalize these marketing materials for different customer segments. Hence, generative AI is capable of increasing engagement and click-through rates.

Using chatbots and virtual assistants in insurance also contributes to superior customer engagement. AI technology serves as a foundation for chatbots that can understand contextual information, outline customer needs, and provide responses in a natural language. Thus, insurance companies can utilize such conversations, for example, to suggest recommendations as part of sales efforts. In our project for HaulHub, we leveraged OpenAI’s GPT for the tasks involving NLP and created the chatbot that understands contextual data, processes customer inquiries, and facilitates conversations.

Artificial Intelligence Underwriting and Risk Management

AI tools with deep learning and predictive analytics algorithms can automatically collect and analyze vast amounts of both customer data and potential future events with specific risk pools. When an insurance business is able to anticipate possible hazards and provide precise risk assessments for customer-relevant insurance coverage, this helps with predictive underwriting.

On top of that, underwriting processes can also be supported by the use of AI for customer behavior analysis. For instance, insurance agents can rely on evaluation of driving behavior, when elaborating car insurance plans, or health metrics, when crafting life insurance coverages.

Fraud Protection and Cybersecurity

In insurance, anomaly detection with machine learning helps to ensure fraud prevention. For that, ML algorithms examine vast data sources, so they could identify patterns that seem like unusual user behavior and can signal suspicious activity. 

Detecting anomalies, for a fraud detection software development company, involves more than just looking for unusual activities. Before a claim is even filed, early warning indicators of fraud can be recognized by utilizing AI approaches like predictive risk modeling. At the same time, behavioral biometrics examines user actions in parallel to look for warning signs of account takeover. Anomaly detection also carefully examines personal data for irregularities, which aids in the detection of attempts to create false identities.

AI technology also offers enhancements for cybersecurity. While deep learning helps with identity verification through facial recognition and document authentication, machine learning and NLP monitor systems to discover emerging cyber dangers.

AI in Insurance Claims Management

The Accenture report reveals the result of how an AI-driven system integrated in the processes of a Polish insurance company. That result is a 10% reduction in claims errors, which is a great improvement considering how many claims insurance companies need to process daily. 

Similar or even better outcomes for claim processing can be guaranteed, when insurers add the following capabilities to their systems:

• ML-powered demand forecasting that aids in predicting the volumes of claims and commit resources to fulfill them;
• Computer vision along with object detection to analyze images in case damage assessment is required;
• Fraud detection that can spot irregularities in claims, detect discrepancies in repair costs, or check a history of false claims;
• NLP to fine-tune chatbots to answer inquiries concerning claims status and gather customer information. 

Moreover, with automation brought to claim processing with AI, insurance companies have the ability to simplify the process of data collection and analysis. In this manner, we completed a project for a B2B Intelligence Services company. Our team integrated NLP and the YOLO model to ensure the extraction of data from PDFs with precision. Camelot or AWS Textract streamlines the extraction, and GPT verified data accuracy for smooth downstream analysis. This resulted in 5x cost reduction.

Billing and Payroll

Predictive analytics, anomaly detection, and automation algorithms in AI applications offer the capabilities for preparing and sending invoices and bills for customers. 

With the use of predictive analytics, artificial intelligence technologies may anticipate revenue based on billing data and trends, as well as examine payment data to enhance billing tactics. Also, insurance agents can monitor spending data and spot odd patterns with the help of anomaly detection. This makes it possible to identify possible inefficiencies that could result in fraud or overspending and implement cost-cutting solutions.

Automation, in its turn, can further simplify claim management. We have proved this when completing the project for our client, where machine learning models for NLP, text classification and embeddings to predict the master service code for invoices based textual description. Consequently, the client’s system was set up to have automated invoice processing, which helped to accelerate the process of the aggregation of the information on operational costs from weeks to hours.