Deborah Koens, global head of cloud studios at AMDOCS Cloud Studio, shares actionable insights into effective implementation of machine learning frameworks.
Insurance companies have enormous reserves of valuable data, including claim numbers, risk model output, customer behavior analysis, and insights from a wide range of business functions. Machine learning (ML) can unlock large value from this data. However, only a small percentage of ML models developed by the industry will be deployed.
The challenges often focus on issues of data quality and governance or model bias and poor performance. These issues are contract breakers in highly regulated sectors such as insurance. Many companies are actively experimenting with ML, but are unable to move the concept into a fully functional solution.
The true frontier is not simply adopting ML, but rather making it work on a large scale. And this is where ML operations (MLOPS) come to the forefront.
MLOPS: The Bridge Between Experiments and OperatisAbove
Creating, training, and validating ML models are just the beginning of the process. Once the model is built, it must be deployed, monitored, maintained and scaled reliably in the production environment. MLOPS provides a framework to achieve this in order to ensure efficiency, accuracy, and compliance using automation, testing, validation and monitoring.
In other words, successful deployment of ML models requires a combination of cloud-native capabilities, agile processes, and sensual collaboration. It also requires a clear purpose, measurable target, and a robust and relevant data foundation.
So, what do you need to achieve MLOP with insurance? Three key factors help to increase ease, speed and compliance.
1. Robust cloud-based infrastructure cloud native systems like kubernetes, when integrated with a CI/CD pipeline, streamlines the automation of model deployment and management. But even more importantly, insurers need cloud infrastructure that supports experimentation and scaling without compromising security.
2. Related Experience and Expertise MLOPS is more than just a fusion of data science and DevOps. A successful implementation also requires close collaboration between actuaries, developers, data engineers and business leaders. Insurance companies need a sensual team to bring together industry expertise, technical skills, and domain knowledge to solve complex challenges.
3. The safety of governance, traceability, and explanationability in the regulated insurance market, companies should prioritize responsible ethical use of ML. Clear documents, model version descriptions, explanability tools, and comprehensive monitoring are key to avoiding bias and preventing models from drifting over time.
Set the stage for MLOPS
Before you start MLOPS, it is important to have a strong cloud presence. MLOPS can run in an on-premises environment, but cloud platforms help reduce complexity and accelerate deployment. Built-in tools and scalable infrastructure make everything easier and often more cost-effective.
Cloud Foundation must include secure, well-governed pipelines, scalable storage and computing resources, and clear identity and access control. These factors create the basic conditions for compliant MLs that provide concrete business value.
It is important to consider whether a cloud environment is ready for a wide range of performance benefits, including MLOPS forecasting. If not, consider putting the right resources in place to achieve this.
In fact, a large UK-based insurer has expanded to AMDOCS to expand and streamline cloud operations, increasing security and compliance with the AS code approach. Modernizing and centralizing cloud estates has enabled insurer developers to work more independently and innovatively within defined organizational guardrails. This has created a secure environment for implementing an MLOPS framework that supports responsible ML innovation and deployment.
Why is it time to use ML?
When applied well, insurers can use their data to ensure measurable commercial profits. As competition grows and underwriting margins get stronger, this is a business crucial. ML deployed through MLOPS helps protect profitability and support growth despite economic headwinds.
One of the ripe insurance applications in ML is that models are trained to streamline claim validation, flag fraud, settlement time and cost optimization, and automation processing. Additional high impact areas where ML can provide measurable benefits to the value chains of insurers, banks and broader financial services include risk modeling, customer engagement and credit scoring.
However, ML does not only improve on core financial services products and processes. The wider business applications can also benefit from improved efficiency and cost optimization, resulting in an ultimate benefit.
For example, AMDOCS data scientists helped Insurtech increase revenue by using algorithms deployed using MLOPS practices to improve field sales ambassador performance. Sales teams visit mobile phone retailers to support the protection sales of their devices. Additionally, the manual approach previously used in route planning made travel inefficient and expensive. The route optimization algorithm covered all variables to minimize travel time and maximize productive time spent at mobile phone stores. Deployed using Best Practice MLOPS Methodologies. Since implementation, travel time and costs have decreased, and revenue has risen. Insurtech can now use the same deployment pathway for future ML models to reduce cost, complexity, and value time.
Scaling ML responsibly in the Genai world
When it comes to harnessing value from data, the biggest risk is to do nothing. With the rise of Generated AI (Genai) ML, it is not treated as technical discipline and is a strategic business enabler.
To stay competitive, insurers will need to move beyond ML pilots, adopt MLOPs and integrate emerging technologies like Genai.
MLOPS accelerates deployment, improves accuracy through continuous monitoring, ensuring compliance, transparency, and explainability. These key factors underpin the effective and ethical use of ML and GENAI, with processes and production dominating fairness and bias.
MLOPS enables insurers to promote data-driven innovation, increase regulatory trust and respond quickly to evolving market conditions. Those investing in robust and scalable cloud-native ML foundations are now well suited to thrive in the future of insurance with genai.
