Integrating AI in Insurance: Our Approach

Insurance Systems in the 2020s: The Software Landscape

In the 2020s, the insurance industry is increasingly leveraging artificial intelligence to improve insurance systems. The most common benefits of AI-driven insurance systems include:

• Automation of manual insurance processes
• Enhanced customer experience
• Improved risk assessment
• A much more accurate underwriting process
• Boosted employee productivity
• Better risk evaluation
• Faster claims settlement
• Enhanced scalability capabilities

Verisk and Guidewire are among the many companies leveraging AI to enhance their insurance processes.

Verisk uses AI to analyze vast amounts of data, gaining insights that enable more informed decisions for underwriting and claims. Their AI-driven insurance system also improves fraud detection, optimizes pricing, and accurately predicts potential losses.

Conversely, Guidewire employs AI to enhance claims processing, policy administration, and customer service. Using machine learning algorithms, Guidewire automates routine insurance tasks to reduce errors and speed up response times. AI helps them offer personalized policies, which improves customer satisfaction, maintains a competitive edge, and drives more business.

While these are just some of the benefits every insurance company aims for, there’s still a challenging implementation process before a company can leverage AI to achieve the benefits.

AI Opens up New Possibilities in Insurance

AI helps insurance companies in two ways:

1. Transforming traditional processes
2. Innovating new solutions

Transformation of traditional processes can help:

• Automate claims processing, reducing the time required to assess and settle claims.
• Enhance the underwriting process by analyzing vast amounts of data from various sources to assess risk accurately.
• Provide 24/7 customer service using chatbots and virtual assistants, handling routine inquiries and claim status checks.
• Detect fraudulent activities by analyzing transaction patterns and flagging suspicious claims.
• Assess risk by analyzing historical data, market trends, and external factors.
• Automate data entry, policy renewals, and compliance checks.
• Tailor policies to individual customers by analyzing behavioral and demographical data.

Innovation of new solutions can help:

• Analyze driving behavior data from telematics devices to offer personalized auto insurance premiums based on individual driving patterns.
• Predict potential failures or maintenance needs, offer proactive services, and reduce claims.
• Model the impact of climate change on assets to help insurers develop products that address environmental risks and offer better coverage.
• Detect fraud in real time with advanced AI algorithms.

But do you really need to replace the whole system to gain these benefits?

Many insurance companies believe replacing their entire insurance system is needed to achieve these benefits. This isn’t true because upgrading the existing system can bring the same benefits and results.

We’re big fans of the API-first approach, which helps integrate AI into an existing insurance system, reducing the pain of replacing the whole system.

Replacing Insurance Systems is Painful, Time-Consuming, and Expensive

Many insurance companies find replacing the entire insurance system daunting because it is complex, time-consuming, and expensive.

The painful process includes numerous challenges that can lead to various problems if not managed correctly.

These challenges include:

• Complexity of integration: Legacy systems within an insurance company have existed for years or decades. Replacing such a system with a new one requires ensuring data consistency and seamless operations, which is technically challenging and resource-intensive.
• Data migration: Moving data from the old to the new system involves transferring vast amounts of sensitive information, which needs to be done accurately and safely. This risky process can lead to data loss, corruption, and breaches. Data integrity and security through this process step are crucial but difficult.
• Customization requirements: Most insurance companies have unique processes and requirements that off-the-shelf systems won’t meet. Therefore, significant additional customization is required, which is time-consuming and costly. If the existing system is already customized to fit the company's needs, the new system won’t fit the company's needs unless it’s fully customized again.
• Regulatory compliance: With strict data privacy and security laws, ensuring the new system complies with all regulations adds another layer of complexity. If it’s not done correctly, this can lead to legal penalties and loss of customer trust.
• Operational disruption: Replacing the system can disrupt day-to-day operations, which additionally requires companies to train employees on the new system, transition workflows, and handle potential downtime, impacting business continuity.

When an insurance company overcomes these challenges, problems and costs are still associated with a full system replacement. Additional issues and expenses include:

• Financial costs: software purchase, licensing, hardware upgrades, consulting fees, data migration, customization, and compliance efforts.
• Time investment: fully implementing a new system can take months to a year.
• Operational risks: downtime, loss of productivity, disruption in customer service, and financial losses.
• Long-term commitment: ongoing maintenance, updates, support, and support personnel.
• Employee training: developing training programs, conducting sessions, and hiring external trainers and AI consultants.

Hiring AI consultants is unavoidable, as it helps navigate complex replacement. However, consultancy fees can be substantial. Dependance on external expertise may also lead to a loss of internal knowledge and control over system customization and implementation.

Such over-reliance on consultants for ongoing support and updates can increase the long-term operational costs.

All of these challenges are reasons why upgrading an existing system is a much better option. This is possible with an API-first approach, which helps minimize risk, reduce cost, and speed up the integration time. Here’s why we advocate for it.

Our Solution Is an API-First Approach

Application Programming Interface (API) is like a messenger that allows software and applications to communicate and interact with each other. It defines a set of rules and protocols that programs can follow.

In terms of AI integration into an existing system, APIs can help:

• Provide access to pre-trained AI models, algorithms, and data processing capabilities.
• Integrate AI with existing data sources and systems. It can fetch data from existing databases or systems, process it using AI algorithms, and return insights or predictions to the system.
• Scale AI capabilities within existing systems and workflows.

We prioritize the design and development of our solutions using APIs. That’s one reason our API solutions seamlessly integrate AI capabilities into existing insurance systems.

Such an approach ensures insurance companies don’t have to replace their existing system, retrain their employees, or change their workflow.

API is all about freedom and flexibility, which allows companies to use our AI solutions across different platforms and interfaces.

Our solutions can integrate into web applications, mobile apps, or backend systems to help handle policy risk assessment, optimize business processes, process and analyze detailed risk data, and enable quicker risk assessments.

How Our Solutions Can Fit Your Insurance Processes

For example, by integrating our Document AI, you can extract data precisely from documents. Our solution is tailored to your specific requirements and your existing system. This way, our API can communicate with your insurance system and allow you to enhance operational efficiency, make claims processing easier, and reduce human error.

Our other solutions, like Database AI or Conversational AI, can help you quickly retrieve policy details, coverage specifics, and claim statuses to make better decisions using information from insurance databases.

At the same time, you can deliver immediate and accurate responses to support queries, free up your staff, and access practical insights from large amounts of unstructured data.

These are just some of the reasons why our API-first approach is beneficial for insurance companies: It streamlines integration, supports flexibility across platforms, and minimizes disruption in existing systems.

Our founder, Ankur Patel, discussed this with Niko Fotopoulos, CEO of Sparx, during a conversation about the impact of AI on the insurance industry in our podcast.

However, the API-first approach and integrating AI into an existing system have many more benefits, so let’s examine them.

Benefits of Our API Approach vs. Replacing Your Existing Insurance System

1. Modularity and Flexibility

The API-first approach allows modular integration of AI capabilities into specific parts of an existing insurance system. This way, insurance companies can integrate AI gradually without disrupting their workflow.

This is ideal for insurance companies that want to address immediate needs without overcomplicating the implementation.

For example, by implementing our solution, Document AI, your company can benefit from enhanced operational efficiency, easier claim processing, and reduced human error and inconsistencies.

Combine it with our Decision AI solution, which will help you assess claims using many data points, verify loan application authenticity, and improve the accuracy of claim evaluations.

You can choose the solutions that would fit your business needs without disrupting your current system.

2. Cost Efficiency

The API-first approach is far more cost-efficient than replacing the whole insurance system. It requires a lower up-front cost as it can leverage the existing structure and software investments a company has made.

Overall, this reduces the costs associated with the implementation of new technology.

3. Compatibility

APIs ensure compatibility with existing systems and software. This enables seamless integration of AI services without needing to redevelop your existing insurance system extensively.

This way, you can take advantage of your existing software stack while enhancing the system efficiency.

4. Risk Mitigation

By integrating artificial intelligence through APIs, insurance companies mitigate the risk associated with the replacement of the whole system. Therefore, you won’t have to worry about data migration, operational disruption, potential regulatory issues, or data breaches.

This approach minimizes business continuity risks and ensures smoother operations.

5. Time Savings

We mentioned earlier that a full system replacement can take months up to a year. Integrating AI with APIs is much quicker and much cheaper in the long run.

Developers can focus on integrating specific functionalities, accelerating deployment, and minimizing downtime.

Integrate AI in Your Existing Insurance System With Us

Would you like to speed up claims settlement, improve customer experience, boost employee productivity, improve risk evaluation, or efficient policy administration?

All of these functionalities are possible with our AI solutions that can integrate directly into your insurance system. If that’s what you’re looking for, schedule a 30-minute call with our experts. We can discuss your needs, find the best way to integrate AI into your system and show you how our insurance AI solutions work live.