At the request of VVP, the platform for financial service providers, our CTO and AI strategist Dennie van den Biggelaar explains how to apply specific AI and machine learning to 'advice in practice'. In various editions, the following topics will be highlighted:

• Getting started with specific AI and ML

• Operationalizing in business processes

• Integrating into existing IT landscapes

• Measuring = learning: KPIs for ML

• Ethics, regulation, and society

• AI and ML: a glimpse into the near future

In this third edition, we answer the question: how do you integrate a trained algorithm with your existing IT landscape and tools?

Read the article text here:

Integrating AI software

The insurance industry is on the brink 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 predict churn, calculate customer lifetime value (CLV), and provide recommendations for cross-selling and upselling, enabling advisors to make better-informed decisions. But how do you integrate these algorithms into your existing IT landscape? How do you ensure that your employees have these predictions and suggestions at their fingertips to work more easily and effectively? In this article, we will discuss some concrete technical tips for a successful integration of AI decision engines into insurance systems.

Define a successful integration

I firmly believe that IT challenges should always serve a business goal. A successful integration always begins with asking the question: ‘when is this integration successful?’ Drafting a user story can help with this, an example:

“As a [digital marketer of agency X] I want to [know weekly which customers need an extra product Y], so that I can [launch a targeted automatic marketing campaign for this group] with the goal of [generating (new) leads for my field advisors weekly].”

This is a great starting point to present to the technical experts what is expected of them. Usually, follow-up questions arise:

• What specific information does the user want to see?

• How often should it be refreshed?

• How will we measure the success of these automatic campaigns?

By asking and answering these questions, the team naturally identifies the framework of a successful integration. It’s not a one-man job: it’s essential that both the business/users and technical experts participate in this exercise!

Analyze existing IT landscape

A successful integration begins with a thorough analysis of the existing IT infrastructure. Many integration attempts fail because there is no clear insight into the current systems, leading to compatibility issues. With which existing IT systems, databases, and interfaces does the AI algorithm need to ‘collaborate’? What volumes of data need to be transferred? When and how quickly?

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

Clear, scalable, and agile

I’ve unfortunately often seen organizations with innovative plans, but their IT landscape was too rigidly set up. Therefore, design a modular and scalable architecture to allow for future expansions and changes, so you remain agile as an organization. Nowadays, it is best practice to use microservices architectures, where each functionality operates as a separate service. This makes it easier to add, replace, or update elements without overhauling the entire infrastructure.

Consistency and quality

Data quality is crucial for AI’s success. Many AI systems perform poorly due to inconsistent, incomplete, or outdated data. Therefore, implement a data cleaning and preprocessing pipeline that ensures all data sent to the AI decision engine is clean and up-to-date. Automated tools for data integration and validation can help with this, thus guaranteeing the reliability of the 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 the successful training and use of AI models.

Testing, validating, and monitoring

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

Use APIs

APIs (Application Programming Interfaces) are crucial to connect AI decision engines with existing systems. Without standardized interfaces, communication between systems can be inefficient and problematic. By developing and implementing APIs that can receive and send data, the 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 given the sensitive nature of insurance data. Insufficient security can lead to data breaches, resulting in a loss of customer trust and privacy violations. Therefore, only use data that is actually 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.

Conclusion

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

First, make sure that you or someone within your own organization clearly understands the business framework of a successful integration. Make this explicit so that it can be communicated. Then appoint someone who feels responsible for the implementation and associated project management. If you don’t want or aren’t able to allocate resources for this yourself, you can easily engage one of your trusted IT partners. That way, you can focus on your own core business!