About

BOX Solution is the flagship solution of the group, designed to automate and improve critical insurance workflows — ranging from data preparation to detecting pricing anomalies and correcting them instantly via API calls.

At the heart of BOX Solution is the proprietary NeuroCore Network, an ensemble of advanced AI models created to overcome inefficiencies in legacy actuarial practices. Unlike conventional approaches based on GLM or GBM, this methodology combines strong predictive accuracy with transparency and operational robustness.

Pain Points Addressed

• Legacy actuarial models lack precision and adaptability.
  Traditional actuarial approaches, such as GLM or GBM, often struggle to capture non-linear patterns in risk assessment and pricing. This leads to underperforming models that cannot easily adapt to changing market conditions or evolving customer behaviors.

• Manual processes delay responses to market deviations.
  Insurers frequently rely on human intervention to detect and correct mispricing, which creates significant lag times. By the time adjustments are made, the company may already face losses or lose competitiveness in fast-moving markets.

• Fragmented data pipelines create inefficiencies.
  Insurance data typically originates from multiple, unconnected systems, resulting in duplication, inconsistencies, and errors. These fragmented pipelines limit the ability to build reliable predictive models and slow down decision-making.

• Difficulty ensuring compliance and transparency in pricing.
  Regulators increasingly demand not only accurate but also explainable pricing methodologies. Many existing models lack transparency, making it challenging to justify pricing decisions or demonstrate compliance with evolving insurance regulations.

• Slow onboarding of new models and scenarios.
  Deploying new risk models or testing alternative pricing scenarios often requires long IT and actuarial cycles. This delay prevents insurers from capitalizing on new opportunities or responding quickly to emerging risks.

Product Advantages

• Proprietary explainable neural network.
  NeuroBox is powered by a unique ensemble of AI models specifically designed for the insurance sector. Unlike “black box” approaches, the system delivers predictive accuracy while maintaining full transparency, allowing insurers to validate results and meet regulatory standards.

• Real-time corrections through APIs.
  The platform connects directly to operational systems, enabling pricing adjustments and corrections to be applied instantly via API. This ensures that insurers can react to market changes on-the-fly, minimizing revenue leakage and competitive disadvantages.

• Integrated scenario analysis and risk simulation.
  NeuroBox empowers insurers to run complex “what-if” simulations and scenario-based analyses. This allows teams to test alternative strategies, stress-test models, and evaluate the potential impact of decisions before implementing them.

• Early alerts on deviations and mispricing.
  The system continuously monitors key performance indicators and model outputs, flagging deviations from expected results. This proactive alerting reduces the need for manual oversight and enables faster corrective action, protecting profitability.

• Streamlined connection with other ecosystem agents.
  NeuroBox seamlessly integrates with complementary AI agents for data enrichment, cleansing, ETL, and reporting. This interconnected ecosystem reduces operational friction, ensures consistent data quality, and accelerates end-to-end insurance workflows.

Main Use Cases

• Validation and Optimization of Risk Models for Motor Insurance Portfolios. Leverage AI to re-evaluate historical data and reduce Loss Ratios by up to 10% for car insurance fleets and mobility providers in the DACH & European markets.

• Real-Time Dynamic Pricing for General Insurance & Mobility. Implement instant pricing corrections and personalized risk assessments via API to capture volatile market segments and prevent revenue leakage.

• High-LTV Customer Acquisition for Life & Pensions. Utilize deep portfolio segmentation to identify and target high-value customer segments, optimizing acquisition costs for long-term Life & Pension products.

• Automated Underwriting Prioritization for High-Volume Retail Lines. Accelerate decision-making by predicting deal-closure probabilities, allowing underwriters to focus on high-intent leads in Health and P&C insurance.

• Cross-Selling Strategy Optimization for Multi-Line Insurers. Apply AI-driven probability scoring to existing portfolios (e.g., bundling Health with Motor) to increase retention and average revenue per user (ARPU).

Functionality

• Risk Calculation and Modelling: Enables insurers to build mathematical models and scenario-based simulations for informed risk management and decision-making.

• Data Augmentation and Processing: Supports data enrichment from the internal data lake while simplifying file ingestion and processing through configurable rules.

• Real-Time Accessibility: Allows models to be deployed and accessed through APIs for instant adjustments and dynamic use.

• Efficiency in Data Handling: Provides early alerts on potential negative deviations and access to a structured data lake, reducing manual workload.

• Support for Business KPIs: Delivers price correction models and independent “second-opinion” insights to enhance confidence, accuracy, and compliance.

Latest version updates

Last Update December 2025