Insurance Industry Data
Solutions
Data engineering for the insurance industry is like creating a smooth and efficient highway for data so that agents do their jobs better and ensure everyone’s covered and happy.
VOLTERA’S Services in the Insurance Industry
Our data engineering services are the gears that drive the insurance industry forward in an increasingly data-driven world.
Customized Insurance Solutions
Our custom solutions empower insurers to use data-driven insights for enhanced customer engagement, improved risk assessment, optimized pricing, operational efficiency, and data management. All the solutions listed below are implemented using AI adoption, cloud computing, and automation. Each one is supported by an example from the US insurance practice.
Claims Fraud Detection System
ML platform analyzes historical claims patterns and identifies real-time anomalies. Flags suspicious claims for investigation while reducing false positives.
Personalized Premium Calculator
An AI engine evaluates individual risk factors beyond traditional demographics. Based on actual behavior patterns and IoT-collected data, it generates fairer pricing.
Policy Document Analyzer
An NLP system extracts key information from policy documents and legacy systems. It creates searchable databases from unstructured data while standardizing formats across platforms.
Customer Churn Predictor
Analytics tool identifying policyholders at risk of non-renewal based on engagement patterns. Triggers targeted retention campaigns before cancellation indicators appear.
Catastrophe Risk Modeler
A geospatial analytics platform assesses property portfolios against natural disaster scenarios. It provides granular risk scoring at the individual property level for more accurate underwriting.
Claims Severity Predictor
The predictive model estimates final claim costs based on the initial report information. It optimizes reserve allocation and enables faster settlement of straightforward claims.
Regulatory Compliance Monitor
Automated system tracking jurisdiction-specific insurance regulations and policy requirements. Flags non-compliant documents and suggests required modifications.
Agent Performance Dashboard
Analytics platform tracking sales metrics, retention rates, and customer satisfaction. Identifies top-performing practices while providing personalized coaching recommendations.
Don’t make the process harder than it is.
Jack Welch, American business executive
Empowering Insurance Businesses to Stay Competitive
Here are the specific benefits that insurance businesses derive from each of these custom data engineering solutions:
Improved resource allocation
and cost optimization
Cost savings and improved
customer service
Enhanced customer experiences
and satisfaction
Raised customer experience
and retention
Improved customer loyalty
and profitability
Comprehensive
data analysis
Competitive pricing and
risk management
Reduced customer churn and
acquisition cost savings
Increased cross-selling and
upselling opportunities
Proactive issue resolution and
reputation management
Informed decision-making and
operational efficiency
Boost Work Efficiency and Accuracy with Expert Machine Learning Support.
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Cases of Using Artificial Intelligence and Machine Learning
Check out a few case studies that show why VOLTERA will meet your business needs.
Stock relocation solution
The client was faced with the challenge of creating an optimal assortment list for more than 2,000 drugstores located in 30 different regions. They turned to us for a solution. We used a mathematical model and AI algorithms that considered location, housing density and proximity to key locations to determine an optimal assortment list for each store. By integrating with POS terminals, we were able to improve sales and help the client to streamline its product offerings.
Client Identification
The client wanted to provide the highest quality service to its customers. To achieve this, they needed to find the best way to collect information about customer preferences and build an optimal tracking system for customer behavior. To solve this challenge, we built a recommendation and customer behavior tracking system using advanced analytics, Face Recognition, Computer Vision, and AI technologies. This system helped the club staff to build customer loyalty and create a top-notch experience for their customers.
Performance Measurement
The Retail company struggled with controlling sales and monitoring employees’ performance. We implemented a software solution that tracks sales, customer service, and employee performance in real-time. The system also provides recommendations for improvements, helping the company increase profits and improve customer service.
Supply chain dashboard
The client needed to optimize the work of employees by building a data source integration and reporting system to use at different management levels. Ultimately, we developed a system that unifies relevant data from all sources and stores them in a structured form, which saves more than 900 hours of manual work monthly.
What Data Science technologies do we use?
Pandas
SciPy
TensorFlow
Numpy
ADTK
DBscan
G. AutoML
Keras
MLFlow
Natural L. AI
NLTK
OpenCV
Pillow
PyOD
PyTorch
FB Prophet
SageMaker
Scikit-learn
SpaCy
XGBoost
YOLO
Steps
Towards Good Development
These data engineering development stages ensure that solutions are well-designed, thoroughly tested, and aligned with business objectives.
Step 1 of 7
Initial Project Assessment
and Definition
In the early phases of our data engineering development process, we engage in a free consultation to gauge project compatibility. During the discovery and feasibility analysis, we adapt to your needs, whether it’s high-level requirements. We gather information to define project scope through discussions, including feature lists, data fields, and solution architecture. We craft a project plan to guide our progress, reflecting our dedication to achieving project goals and delivering effective data engineering solutions.
Step 2 of 7
Discovery
So, you have finally decided that you are ready to cooperate with DATAFOREST.
The discovery stage involves delving into the details of the project. Data engineers gather requirements, analyze existing systems, and understand the needs of the business. This step is crucial for laying the groundwork for development, as it ensures that the project aligns with business goals and user needs.
Step 3 of 7
Tech Design and Backlog Planning
In this stage, the technical architecture and design of the solution are formulated. Data engineers plan how data will be collected, stored, processed, and presented. Simultaneously, the project backlog is created — a list of tasks and features to be developed. This backlog is prioritized, ensuring that high-priority items are addressed first.
Step 4 of 7
Development Based on Sprints
Development takes place in iterative cycles known as sprints. During each sprint, the development team tackles tasks from the backlog. The team focuses on coding, testing, and integrating the components. At the end of each sprint, a functional part of the solution is ready for review.
Step 1 of 5
Free consultation
Step 5 of 7
Project Wide QA
Quality Assurance is an ongoing process that permeates the entire project development lifecycle. It ensures rigorous testing, identification, and resolution of any bugs or issues to guarantee the solution’s smooth operation, compliance with requirements, and alignment with quality standards. The solution is prepared for release as QA activities persist and necessary adjustments are continuously implemented.
Step 6 of 7
Deployment and Rollout
The deployment phase involves releasing the solution to the production environment, making it accessible to users. It requires careful planning to ensure a seamless transition and minimal disruption. After deployment, the rollout phase begins, involving training for users and ongoing support to address any hiccups.
Step 7 of 7
Support and Continuous Improvement
In the final stages, we ensure ongoing excellence. We guarantee optimal performance and swiftly address any issues. Simultaneously, our feedback process empowers us to continuously enhance the solution based on user insights, aligning it with evolving needs and driving continuous innovation.
