Hi Marco,
1. How has AI transformed your approach to data analytics?
AI has significantly transformed the approach to data analytics by enabling more efficient data processing, enhancing efficiency, accuracy and real-time decision-making. There are some aspects where we can particularly notice such advantages. One of the most important is the Advanced Data Processing & Pattern Recognition which stands for the capability of AI to analyze massive amounts of vehicle including GPS, accelerometer, and gyroscope readings, far more efficiently than traditional methods. Machine Learning (ML) models can especially detect patterns in driving behavior, fuel consumption, and vehicle wear, leading to more accurate insights. Also, AI enables predictive models to help in forecasting vehicle failures by analyzing historical sensor data, allowing fleet managers to schedule maintenance proactively, reducing downtime and repair costs. Similarly, AI-driven risk assessment models evaluate driver behavior to predict accident likelihood, enabling insurers to offer dynamic pricing. Another remarkable aspect is the AI capability to compress and transmit telematics data efficiently, ensuring minimal loss of information: techniques like Autoencoders and Neural Compression allow real-time data transmission even in low-bandwidth environments. At the same time, AI enhances real-time monitoring by detecting anomalies such as harsh braking, aggressive acceleration, or unusual vehicle movements. It can differentiate between normal and risky behaviors, triggering alerts or automated actions. Finally, AI models identify inconsistencies in claims by comparing accident data with historical patterns. Computer vision and sensor anomaly detection help particularly detect staged accidents, exaggerated damages, or fraudulent claims.
2. How do you see the role of data analytics evolving in the next five years?
The role of AI and data analytics in telematic companies is poised to evolve significantly over the next five years, driven by advancements in technology, regulatory changes, and market demand. We will see AI and data analytics shift from being support tools to core strategic assets in telematics. Plus, companies that leverage AI for real-time decision-making, predictive analytics, and automation while ensuring regulatory compliance and data privacy will gain a significant competitive advantage.
Really speaking, AI will improve even more risk assessment and driving behavior analysis, making the benefits of a data-driven approach more evident. Connected cars will enable easier access to the data needed for this type of analysis, facilitating the adoption of these services within the insurance market. The insurance premium will be estimated, based on evidence related to the individual user’s driving data, in a fairer manner, and no longer calculated using data such as the postal code of residence or the engine capacity of the car. We become familiar with the word “mutuality”, what does it mean at the end of the day? If I am not able to assess your risk with high accuracy, safest users will pay the risk of the riskier ones, so that the whole insurance portfolio is still profitable. Telematics is the key to becoming fairer, we are seeing the market going in that direction, the last proof is the Product Oversight and Governance (POG), regulatory from IVASS.
In the long term, the increasing number of connected cars, we will see a shift to edge AI (processing data closer to the source, i.e., in the vehicle) will enable faster, real-time decision-making for applications like accident detection, traffic optimization and fleet management. This will reduce reliance on centralized cloud processing, improving efficiency and reducing latency.
3. What are the biggest challenges in implementing AI within data analytics teams?
It deals with an aspect which leads to several challenges. First and foremost, data sources are fragmented since Telematics data comes from multiple sources such as GPS, sensors, and onboard diagnostics which are often in different formats and levels of granularity. To make things worse, there is no regulatory so that each OEM has its own data model, sensors, and detection algorithms. Then, AI models require continuous data streams, but not all companies have the infrastructure to handle real-time ingestion and analysis: processing, in fact, massive volumes of real-time telematics data require significant cloud and AI infrastructure investments. Moreover, AI models must be continuously updated with new driving data, requiring ongoing monitoring and retraining to stay relevant. They also need to be integrated into existing workflows, from risk assessment to claims processing, without proceeding to any disrupting operations. Overcoming these challenges requires a strong synergy among data governance, advanced machine learning pipelines, collaboration between AI and business teams, as well as a strategic approach to regulatory compliance. Companies that address these hurdles effectively will gain a competitive edge in the evolving telematics landscape.
4. What Kind of advancements in terms of solutions and products do you envisage in the near future?
I expect several key advancements in solutions and products within the telematics industry, driven by AI, IoT, and data analytics. The telematics industry is evolving from passive data collection to real-time, AI-powered decision-making, therefore, future solutions will focus on personalized, fair and dynamic insurance pricing, proactive vehicle safety & maintenance, seamless mobility services beyond insurance and Privacy-first AI implementations. Yet, AI-driven models will move beyond mileage-based insurance to predict the risk considering car safety systems and technology, traffic conditions, and even driver fatigue. Policies will become truly personalized, adjusting pricing dynamically based on risk factors whilst insurers will implement “Pay-How-You-Drive” (PHYD) and “Pay-As-You-Drive” (PAYD) models with increased precision. Another challenge will concern the cost reduction of AI-powered dashcams, they already have the capability to process video data in real time to detect risky behavior (e.g., distracted driving, tailgating, drowsiness) and Edge computing allows on-device AI models to analyze video feeds instantly, reducing reliance on cloud computing. The challenge will be to reduce the footprint of AI models, and optimize hardware platforms, allowing to overcome the current cost adoption barrier.
We already have technology on telematics platforms to automatically detect crashes, assess damage using AI-powered image recognition, trigger emergency responses, and generate instant AI-based reports to help speed up claims processing and reduce frauds. What will be advancing is the maturity of this solutions, that will be able to reach the highest level of service, and will become even more explainable and bias-free, aligning with EU AI regulations.
5. Can you share a best case of a successful AI-driven analytics project?
A notable example of a successful AI-driven analytics project is the partnership between ABAX Group and OCTO Telematics, aimed at enhancing risk assessment and enabling hyper-personalized pricing not only for commercial fleets. In February 2025, following a comprehensive six-month RFP process, ABAX Group’s insurance division, “Fair,” selected OCTO Telematics as its risk scoring partner for their Usage-Based Insurance (UBI) customers. This strategic collaboration was established to leverage OCTO’s advanced AI-powered analytics to improve risk assessments, optimize pricing models, and enhance the overall customer experience.
The partnership focuses on integrating OCTO’s sophisticated risk scoring capabilities into ABAX’s insurance offerings. OCTO’s technology collects and analyzes extensive telematics data, including driving behavior, road types, trip timing, duration, and average speed. This data is processed into actionable statistics, forming the basis for developing UBI programs that reflect individual driving styles and behaviors.
By adopting OCTO’s AI-driven risk assessment tools, ABAX aims to provide more accurate and personalized insurance pricing for small fleet operators. This approach not only aligns premiums more closely with actual risk but also incentivizes safer driving behaviors among policyholders. The collaboration exemplifies how telematics data and AI can transform traditional insurance models, offering tailored solutions that benefit both insurers and insured parties.
This case demonstrates the potential of AI-driven analytics in the telematics industry, particularly in creating customized pricing strategies for commercial fleets. By leveraging detailed driving data and advanced risk scoring, insurers can offer fairer, usage-based policies that promote safety and efficiency.
Thank you