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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.


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DATAFOREST’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.
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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.
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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.
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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.View page
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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.
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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.

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
02
Cost savings and improved
customer service
03
Enhanced customer experiences
and satisfaction
04
Raised customer experience
and retention
05
Improved customer loyalty
and profitability
06
Comprehensive
data analysis
07
Competitive pricing and
risk management
08
Reduced customer churn and
acquisition cost savings
09
Increased cross-selling and
upselling opportunities
10
Proactive issue resolution and
reputation management
11
Informed decision-making and
operational efficiency
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Success stories

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

Insurance Sales Automation

An insurance agency was struggling with a slow lead intake process and a demotivated sales team. Their customer retention rate was stuck at 32%, and they urgently needed more customers. By implementing tailored solutions including automated lead intake from top carriers, seamless internal data synchronization, integration with quote providers, and the unification of all communication channels in a single Live Chat platform, we supercharged their growth! Their customer numbers shot up 2x, and they're back in the game!
2x

increase in new policy sales

+26%

Customer retention

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Peter N.

Head of Sales U.S. Insurance Agency
View case study
Insurance Sales Automation preview
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The DATAFOREST team truly understood the issues we were facing and came up with solutions that have completely transformed our insurance agency.

Insurance Profitability Analysis Tool

This project involved developing a tailor-made data analysis tool for a U.S. insurance provider who were facing challenges analyzing a significant volume of data. The Client needed a professional and customized solution which would enable effective analysis of their data and provide actionable insights to improve their business operations. Our solution delivers real-time processing of data, flexible filtering capabilities through dashboards, and also supports dashboards detailing the evaluation of insurance loss or profit by industry vertical. Additionally, a predictive model for profitable insurance cases was built using historical data, and a reporting system was created to show significant factors and profitability based on different metrics.
> 10TB

data processed

89%

accuracy improvement

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Sean B.

CEO Insurance provider
View case study
Insurance Profitability Analysis Tool case preview image
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Great work! The team provided an excellent solution for consolidating our data from multiple sources and creating valuable insights for our business.

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.
5%

customer retention boost

25%

profit growth

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Christopher Loss

CEO Dayrize Co, Restaurant chain
View case study
Client Identification preview
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The team has met all requirements. DATAFOREST produces high-quality deliverables on time and at excellent value.

Entity Recognition

The online marketplace for cars wanted to improve search for users by adding full-text and voice search, as well as advanced search with specific options. We built a system application using Machine Learning and NLP methods to process text queries, and the Google Cloud Speech API to process audio queries. This helped greatly improve the user experience by providing a more intuitive and efficient search option for them.
2x

faster service

15%

CX boost

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Brian Bowman

President Carsoup, automotive online marketplace
View case study
Entity Recognition preview
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Technically proficient and solution-oriented.

Employee Tracker

The large Retail company was facing a significant challenge in managing and tracking our employees' working hours and needed a solution that would automate the process and ensure accuracy. We developed a system for counting employees' working hours. Employees simply approach the device upon arrival and the system automatically identifies them and records their check-in time.
100h+

manual work reduced

13%

work experience boost

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Bernd Herzmann

CTO Retail company
View case study
Employee Tracker preview
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DATAFOREST has an excellent workflow and provide constant and close communication. The team brings in a range of technical talent to address issues as they arise.

AWS Cost Reduction

This project optimized the cloud infrastructure of a U.S. IT services company to reduce costs and improve performance. Our investigation identified several areas for optimization, including unused computing resources, inconsistent storage, and a lack of savings plans. We helped to optimize resources, implemented better policies for storage, and improved internal traffic flow through architecture redesigns and dockerization.
23k+

monthly savings

8%

performance optimization

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Harris N.

CTO IT Services & Consulting
View case study
AWS Cost Reduction case image preview
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The team's deep understanding of our needs allowed us to achieve a more secure, robust, and faster infrastructure that can handle growth without incurring exorbitant costs.

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Financial Intermediation Platform

The project aims to develop a deal origination platform for private equity investments in infrastructure-related sectors and involves building a secure, interactive B2B platform from scratch, with sign-up functionality to connect investment firms to proprietary investment opportunities. DATAFOREST built a highly-loaded platform and applied AI functionality to empower the application's development.
98%

model accuracy

100%

timely development

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Enrico Cattabiani

Founder & CEO IDN, Infrastructure Deals Network
View case study
Financial Intermediation Platform preview
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They understood our requirements, translated into actions rapidly, and adapted to requests easily.

Store heatmap

The electronics retailer, wanted to improve their sales and customer service by analyzing the flow of people into their stores. We created a system using Machine Learning, image detection, and face recognition. The system tracks visitors' movements and the most viewed shelves and products. This information helps the store to focus on selling popular products and to avoid unpopular ones, ultimately improving the sales process.
9%

increase in sales

100%

dead zones removed

Jared D. photo

Jared D.

CEO Consumer Electronics Retail
View case study
Store heatmap case image
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DATAFOREST provides meaningful shopper-behavior Insights. They are very responsive and effective, trying to engineer and offer the best fit solution.

Demand forecasting

We built a sales forecasting system and optimized the volume of goods in the warehouse and the range of goods in different locations, considering each outlet's specifics. We set up a system that has processed more than 8 TB of sales data. These have helped the retail business increase revenue, improve logistics planning, and achieve other business goals.
88%

forecasting accuracy

0.9%

out-of-stock reduced

Andrew M. photo

Andrew M.

CEO Luxury Goods Retail
View case study
Store heatmap case image
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I think what is really special about the DATAFOREST service is its flexibility, openness, and level of quality and expertise.

Show all Success stories

Steps
Towards Good Development

These data engineering development stages ensure that solutions are well-designed, thoroughly tested, and aligned with business objectives.

How we help companies?
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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.
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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.
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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.
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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.
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Step 5 of 7

Project Wide QA

After completing all sprints, the project enters the Quality Assurance phase. The entire solution is rigorously tested to identify and fix any bugs or issues. This stage ensures the solution functions smoothly, meets requirements, and aligns with quality standards. The answer is ready for release once QA is complete and necessary adjustments are made.
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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.
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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.

What Data Science technologies do we use?

Pandas icon
Pandas
SciPy icon
SciPy
TensorFlow icon
TensorFlow
Numpy icon
Numpy
ADTK icon
ADTK
DBscan icon
DBscan
G. AutoML icon
G. AutoML
Keras icon
Keras
MLFlow icon
MLFlow
Natural L. AI icon
Natural L. AI
NLTK icon
NLTK
OpenCV icon
OpenCV
Pillow icon
Pillow
PyOD
PyOD
PyTorch icon
PyTorch
FB Prophet icon
FB Prophet
SageMaker icon
SageMaker
Scikit-learn icon
Scikit-learn
SpaCy icon
SpaCy
XGBoost icon
XGBoost
YOLO icon
YOLO

Related articles

All publications
Article image preview
September 22, 2023
19 min

Discover how insurers adapt to digital channels and strive to provide the same level of service customers expect in 2024.

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December 6, 2023
12 min

Data Science in Insurance: A Healthy Person Approach

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All publications

Still have questions about data science services?

How do insurance companies use big data in the insurance sector?
Companies use insurance big data to analyze vast volumes of information from various sources to assess risks more accurately, price policies, detect fraud, improve customer experiences, and make data-driven decisions. It’s the place for big data analytics in insurance.
How can data engineering help insurance companies effectively manage and analyze large volumes of data?
Data engineering automates repetitive data management tasks, reducing manual effort and improving efficiency. It enables insurance companies to streamline data processes, ensuring data is processed, updated, and analyzed on time. It’s a part of data analytics for insurance.
What challenges does the insurance industry face in harnessing the power of big data?
Big data in insurance brings challenges in terms of data governance and compliance. Insurance companies must establish policies and processes to ensure proper data usage and access controls with industry regulations such as GDPR or HIPAA. It’s crucial for data analytics in the insurance industry.
How does data engineering ensure data quality and integrity in insurance operations?
Data engineering sets up automated data quality checks and monitoring. They validate data against predefined rules and quality thresholds, flagging any data inconsistencies or anomalies. By automating checks, data engineering helps promptly identify and rectify data quality issues.
What role does data integration play in improving operational efficiency and decision-making in insurance?
Consolidating data into a centralized repository creates a single source of truth that eliminates data silos and raises the data integrity insurance services. This unified data view enhances operational efficiency by providing a holistic customer understanding.
How can advanced analytics and predictive modeling enhance insurance risk assessment and underwriting processes?
Insurers use sophisticated algorithms and machine learning techniques to identify patterns, correlations, and hidden relationships within the data. It leads to a more comprehensive understanding of risk factors and helps find potential risks more accurately.
What are the key considerations when implementing real-time data processing and reporting capabilities in insurance?
Real-time electronic data processing insurance services require integration from internal and external sources. It establishes robust pipelines that efficiently collect, cleanse, transform, and integrate data in real-time. It also includes ensuring compatibility between different data formats and systems. The insurance data scientists will work with this.
How can cloud-based solutions empower companies to leverage big data analytics in the insurance sector effectively?
Cloud-based solutions can empower insurance companies to leverage big data insurance analytics effectively by providing scalable and flexible computing resources, enabling seamless data storage, processing, and analysis, and facilitating collaboration and innovation cost-efficiently.
What measures are taken to ensure data security and privacy in data engineering for insurance?
Measures taken to ensure data security and privacy in data engineering for insurance include encryption of sensitive data, access controls, regular security audits, compliance with data protection regulations, and employee training on data handling best practices.
How can data engineering drive innovation and competitive advantage in the insurance sector?
Data engineering can drive innovation and competitive advantage in the insurance sector by enabling the power of insurance data analytics to develop innovative products, enhance customer experiences, and make informed decisions, ultimately staying ahead of the competition. It’s also an insurance data analyst's job.

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