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Insurance

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.


Let your data create value

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.

Insurance Data Science: Unearthing
Insights for Informed Decisions

Data science services are the detective agency for insurance businesses, utilizing statistical analysis and machine learning to uncover hidden patterns, assess risks, and predict future trends. Insurance data science identifies fraud, analyzes customer behavior, and optimizes underwriting processes.

Data Integration: Storage and
Analytics in Insurance

Data Integration serves as the central hub that consolidates diverse data sources within insurance companies, unifying policyholder information, claims data, and underwriting records, and fostering a centralized storage and analytics environment. This centralized structure streamlines operations, breaks down data silos, and elevates customer service capabilities.

DevOps: Speeding
Up the Insurance Engine

DevOps services are the pit crew for insurers, ensuring that the systems and applications run smoothly. DevOps shortens time-to-market for new insurance products and services by automating development, testing, and deployment. It allows it to respond quickly to changing market demands and outmaneuver competitors.

Data Scraping: Harvesting
Insights from the Web

Data scraping is the information picker for insurance companies, collecting data from various sources: competitor websites, news articles, and customer reviews. This data can be analyzed to gain market intelligence, track industry trends, and monitor competitor activities. Insurers make strategic decisions and seize opportunities as they arise.

Web Applications:
Enhancing Customer Interactions

Web applications are the digital storefronts for insurance businesses, providing customers with user-friendly interfaces to access policies, file claims, and interact with support teams. These applications improve customer engagement, streamline processes, and enhance customer experience. Whether it’s a mobile app for policyholders or a web portal for agents, these tools make insurance more accessible.

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

Simplify data acquisition with VOLTERA

Empowering Insurance Businesses to Stay Competitive

Here are the specific benefits that insurance businesses derive from each of these custom data engineering solutions:


01 Improved resource allocation
and cost optimization

I want it!

02 Cost savings and improved
customer service

I want it!

03 Enhanced customer experiences
and satisfaction

I want it!

04 Raised customer experience
and retention

I want it!

05 Improved customer loyalty
and profitability

I want it!

06 Comprehensive
data analysis

I want it!

07 Competitive pricing and
risk management

I want it!

08 Reduced customer churn and
acquisition cost savings

I want it!

09 Increased cross-selling and
upselling opportunities

I want it!

10 Proactive issue resolution and
reputation management

I want it!

11 Informed decision-making and
operational efficiency

I want it!

Boost Work Efficiency and Accuracy with Expert Machine Learning Support.

Get in Touch Now!

Get in Touch

Cases of Using Artificial Intelligence and Machine Learning

Check out a few case studies that show why VOLTERA will meet your business needs.

All Success Story
Healthcare
Data Science
Data Insights & Forecasting

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.

10% productivity boost 7% increase in sales
Mark S.

Mark S.

Partner Pharmacy network,

View case study →
Stock relocation solution

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.

Christopher Loss

Christopher Loss

CEO Dayrize Co, Restaurant chain,

View case study →
Client Identification

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.

Amir R.

Amir R.

CEO, CEO Fashion Retailer

 View case study →
Performance Measurement

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.

Michelle Nguyen

Michelle Nguyen

Senior Supply Chain Transformation Manager Unilever, World’s Largest Consumer Goods Company

 View case study →
Supply chain dashboard

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.

How do we help companies?

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 1 of 5
Free consultation

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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 1 of 5
Free consultation

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 1 of 5
Free consultation

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 1 of 5
Free consultation

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 1 of 5
Free consultation

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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.

Step 1 of 5
Free consultation

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Still have questions about data science services?

How do insurance companies use big data in the insurance sector?
How can data engineering help insurance companies effectively manage and analyze large volumes of data?
What challenges does the insurance industry face in harnessing the power of big data?
How does data engineering ensure data quality and integrity in insurance operations?
What role does data integration play in improving operational efficiency and decision-making in insurance?
How can advanced analytics and predictive modeling enhance insurance risk assessment and underwriting processes?
What are the key considerations when implementing real-time data processing and reporting capabilities in insurance?
How can cloud-based solutions empower companies to leverage big data analytics in the insurance sector effectively?
What measures are taken to ensure data security and privacy in data engineering for insurance?
How can data engineering drive innovation and competitive advantage in the insurance sector?