The insurance industry is on the verge of a technological revolution. With the integration of AI decision engines, insurers can significantly enhance customer service and achieve better business outcomes. AI algorithms can, for example, predict churn, calculate customer lifetime value (CLV), and provide recommendations for cross-selling and upselling, allowing advisors to make more informed decisions. But how do you integrate these algorithms into your existing IT landscape? How do you ensure that your employees have access to these predictions and suggestions at the right time to work more easily and effectively? Here are some concrete technical tips for successfully integrating AI decision engines into insurance systems.

Define a successful strategy

I firmly believe that IT challenges should always serve a business purpose. A successful integration always begins with the question: ‘when is this integration successful?’ Creating a user story can help with this, for example: “As [digital marketer at agency X], I want to know [weekly which customers need an additional product Y], so that I can set up a targeted automatic marketing campaign for this group with the goal of [generating (new) leads weekly for my field advisors].”

This is an excellent starting point to present to the technical experts what is expected of them. More often than not, further clarifying questions will follow:

• What specific information does the user want to see?

• How often should this be refreshed?

• How are we going to measure the success of these automatic campaigns?

By asking and answering these questions, the team naturally identifies the framework for a successful integration. This is certainly not a one-man job: it is crucial for both business/users and technical experts to be represented in this exercise!

‘IT challenges should always serve a business purpose’

Analyze existing IT landscape

A successful integration starts with a thorough analysis of the existing IT infrastructure. Many integration attempts fail because there is no proper understanding of current systems, leading to compatibility issues. With which existing IT systems, databases, and interfaces must the AI algorithm 'collaborate'? What amounts of data need to be transferred? When and how fast?

In practice, this means cooperating and coordinating with various IT partners of backend and frontend systems. Therefore, start this inventory on time and include all (external) stakeholders in your plans. Don't have the time or resources for this yourself? Appoint one of your IT partners to manage this project for you. After all, it’s their expertise!

Organized, scalable, and agile

Unfortunately, I have often seen organizations with innovative plans, but their IT landscape was set up too rigidly. Therefore, design a modular and scalable architecture to enable future expansions and changes, so your organization can remain agile. That’s why it’s currently best practice to use microservices architectures, where each functionality runs as a separate service. This makes it easier to add, replace, or update new elements without overhauling the entire infrastructure.

Consistency and quality

Data quality is crucial for the success of AI. Many AI systems underperform due to inconsistent, incomplete, or outdated data. Therefore, implement a data cleaning and preprocessing pipeline to ensure all data sent to the AI decision engine is clean and up-to-date. Automated tools for data integration and validation can assist in this, guaranteeing the reliability of AI outcomes. Use ETL (Extract, Transform, Load) processes to extract data from different sources, transform it into a uniform format, and load it into a central data repository. This ensures a streamlined data flow essential for successfully training and using AI models.

Testing, validating, monitoring

Thorough testing and validation are essential to ensure that the AI models function correctly within the existing systems. Insufficient testing can lead to errors and unexpected problems post-launch. Therefore, conduct extensive tests in a simulated environment that mirrors the production environment. Validate the output of the AI models with historical data and scenario analyses. Involve end users in the testing phase to ensure the models meet business requirements and user needs.

Use APIs

APIs are crucial to connect AI decision engines with existing systems. Without standardized interfaces, communication between systems can become inefficient and problematic. By developing and implementing APIs that can receive and send data, integration becomes flexible and scalable. This ensures that the AI decision engine can robustly communicate with both back-office and front-office systems.

Security and privacy

Data security is crucial, especially considering the sensitive nature of insurance data. Inadequate security can lead to data breaches, resulting in loss of customer trust and breaches of consumer privacy. Therefore, use only data that is genuinely needed and anonymize as much as possible. Do you really need certain sensitive data? Then apply encryption.

Ensure all data transfers between systems and the AI decision engine occur over an encrypted connection. Use access controls and audit logging to safeguard data security and ensure compliance with regulations.

Successful landing

A sound integration is essential for successfully embedding AI in your organization. You must not only focus on a scalable business case and the user but also think about agility, security, privacy, and quality. Therefore, you need a team with various competencies and you will need to coordinate and collaborate with IT partners.

Ensure first that you or someone in your organization precisely understands the business frameworks for a successful integration. Make these explicit so you can pass them on. Then appoint someone who feels responsible for the realization and related project management. If you can't or don't want to allocate resources for this yourself, you can perfectly engage one of your trusted IT partners for this. Let you focus on your core business!

This article was originally published in VVP, read here the online version.