Deborah Koens, Global Head of Go-To-Market, Cloud Studios at Amdocs Cloud Studio shares actionable insights on the effective implementation of machine learning frameworks.
Insurers hold vast reserves of valuable data, including claims figures, risk model outputs, customer behavior analytics, and insights from wider business functions. Machine learning (ML) can unlock value from this data at scale. Yet only a small percentage of ML models developed by industry ever get deployed.
Challenges often centre on issues with data quality and governance or model bias and underperformance. These matters are dealbreakers in highly regulated sectors like insurance. Many businesses are actively experimenting with ML, but few manage to progress concepts into fully functioning solutions.
The true frontier is not simply adopting ML but making it operational at scale. And this is where ML operations (MLOps) comes to the fore.
MLOps: the bridge between experimentation and operation
Creating, training, and validating an ML model is just the start of the process. Once a model is built, it must be deployed, monitored, maintained, and scaled reliably in a production environment. MLOps provides a framework to achieve this, using automation, testing, validation, and monitoring to ensure efficiency, accuracy, and compliance.
In other words, successful deployment of ML models demands a combination of cloud-native capabilities, agile processes, and cross-functional collaboration. It also requires clear objectives, measurable targets, and a robust, relevant data foundation.
So, what does it take to bring MLOps to life in insurance? Three critical factors help boost ease, speed, and compliance:
1. A robust, cloud-based infrastructure Cloud-native systems like Kubernetes, when integrated with CI/CD pipelines, streamline automation of model deployment and management. But more importantly, insurers need a cloud infrastructure that supports experimentation and scaling without compromising security.
2. Relevant experience and expertise MLOps isn’t just a fusion of data science and DevOps. Successful implementation also demands close collaboration between actuaries, developers, data engineers, and business leaders. Insurers need cross-functional teams that bring together industry expertise, technical skills, and domain knowledge to solve complex challenges.
3. Secure governance, traceability, and explainability In the heavily regulated insurance market, companies must prioritise responsible and ethical use of ML. Clear documentation, model versioning, explainability tools, and comprehensive monitoring are key to avoid bias and ensure models don’t drift over time.
Setting the stage for MLOps
Before getting started with MLOps, it’s important to have a strong cloud presence. While MLOps can run in on-premises environments, cloud platforms reduce complexity and help accelerate deployment. Built-in tools and scalable infrastructure make everything more straightforward and often more cost-effective.
The cloud foundation should include secure, well-governed pipelines, scalable storage and compute resources, and clear identity and access controls. These factors create the fundamental conditions for compliant ML that offers tangible business value.
It’s critical to consider whether your cloud environment is primed for a wide range of performance benefits, including MLOps-readiness, and if it’s not, consider putting the right resources in place to make this a reality.
In practice, a leading UK-based insurance company engaged Amdocs to scale and streamline its cloud operations on Azure, boosting security and compliance with as-code approaches. By modernising and centralising the cloud estate, we enabled the insurer’s in-house developers to work more independently and innovatively within defined organisational guardrails. This has created a secure environment for introducing MLOps frameworks that support responsible ML innovation and deployment.
Why now is the time to harness ML
Applied well, ML lets insurers use their data to secure measurable commercial gains. As competition intensifies and underwriting margins tighten, this is a business imperative. ML deployed through MLOps helps protect profitability and support growth despite the economic headwinds.
One insurance application ripe for ML is claims processing automation, with models trained to streamline claim validation, flag fraud, and optimise settlement time and costs. Additional high impact areas where ML can deliver measurable gains for insurers, banks and the wider financial services value chain include risk modelling, customer engagement, and credit scoring.
However, it’s not just core financial services products and processes that can be improved with ML. Wider business applications can also benefit from greater efficiency and cost optimisation, delivering significant bottom line benefits.
For example, Amdocs’ data scientists helped an InsurTech increase revenue by boosting the performance of field sales ambassadors using an algorithm deployed using MLOps practices. The sales team visits mobile phone retailers to support device protection sales, and the manual approach previously used for route planning meant travel was inefficient and costly. Our route optimisation algorithm covered all variables to minimise travel time and maximise time spent productively in mobile phone stores. It was deployed using best practice MLOps methodologies. Since implementation, travel time and costs are down, and revenue is up. The InsurTech can now use the same deployment pathway for future ML models, reducing cost, complexity, and time to value.
Scaling ML responsibly in a GenAI world
When it comes to leveraging value from data, the biggest risk is doing nothing. With the rise of generative AI (GenAI) ML can no longer be treated as a technical discipline, it’s a strategic business enabler.
To stay competitive, insurers must move beyond ML pilots, adopt MLOps, and integrate emerging technologies like GenAI.
MLOps accelerates deployment, improves accuracy through continuous monitoring, and ensures compliance, transparency, and explainability. These critical factors underpin effective, ethical use of ML and GenAI, with processes and outputs governed for fairness and bias.
Done right, MLOps enables insurers to drive data-led innovation, boost regulatory confidence, and respond quickly to evolving market conditions. Those who invest in robust, scalable cloud-native ML foundations now will be well placed to thrive in a GenAI-powered insurance future.
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