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Finance

Data-Driven
Finance

Innovative use of technology to improve and automate the delivery of financial services.

Let your data create value

AI-Driven Fintech Services

Managing huge amounts of financial data — securely, efficiently, and thoughtfully.

Financial Data Integration
and Visualization

Unite data from legacy systems, different standards, and APIs. Artificial Intelligence automatically harmonizes data coming from different third-party sources, ensuring real-time data syncing. So, you get financial data in real-time, and it’s visualization — for easy decision-making.

Fraud and Anomaly
Detection

Prevent unauthorized activities that can lead to data breaches. AI-based systems for real-time fraud detection and prevention identify unusual transaction patterns, verifying user identity, and implementing anti-money laundering (AML) measures.

Fintech Scalability
and Performance

Scale your infrastructure, managing large volumes of transactions and ensuring the system’s stability. We analyze how the existing system processes data, optimize queries and redesign the cloud infrastructure for cost-effectiveness and improved performance. Efficiently written queries consume less computational resources.

Big Data Analytics

Derive insights from large volumes of financial data. AI algorithms — in machine learning and deep learning — are adept at processing and analyzing vast amounts of data much more quickly and accurately than traditional methods.

Internal Process Automation

AI-driven tools automate data entry, transactions, and report generation; analyze workflows to suggest improvements and automate unstructured data processing. AI also processes the data within the ERP system: financial entries, payroll data, and purchase orders.

Predictive Analytics for
Financial Marketing

Enhance marketing effectiveness by predicting customer preferences. AI algorithms, particularly those in machine learning, analyze historical customer data. They segment customers into distinct groups and predict the outcomes of different marketing strategies.

Provide Personalized Investment Options

Our customized solutions are designed to address your specific business needs.

Transforming Financial Data with Real-Time Integration

Have you ever been disrupted by delays in critical processes? The solution optimizes data preparation.

Legacy System Integration

Have you faced challenges integrating legacy systems with modern applications? Or struggled with API and cloud mergers? Our solution ensures data flow and compatibility.

Fraud Detection

Are you afraid that fraudulent activities will waste your work? The solution prevents fraudulent activities, safeguarding financial assets.

Financial Services Pricing with Data-Driven Analysis and Reporting

Are you having difficulty adjusting lending rates? With our solution, you’ll make immediate, data-driven pricing decisions.

Leveraging Predictive Analytics for Accurate Demand Forecasting in Finance

Need help understanding how to calculate an investment demand? The solution creates and optimizes your investment strategies.

Don’t make the process harder than it is.

Jack Welch, American business executive

Simplify data acquisition with VOLTERA

Benefits of Data-Driven Finance

Benefits of e-commerce using our services and solutions:

01 Informed and timely decisions
for investments.

I want it!

02 Reducing manual efforts and
improving operational efficiency.

I want it!

03 Acting on opportunities
and threats swiftly.

I want it!

04 Higher customer satisfaction
and loyalty.

I want it!

05 Greater accuracy in reporting
and analytics.

I want it!

06 Gaining immediate insights into market trends.

I want it!

07 Reducing the risk of non-compliance
and penalties.

I want it!

08 Enhancing overall
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

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 VOLTERAGROUP.

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

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

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Still have questions about data science services?

How does data engineering help finance make better investment decisions or manage risks?
What are some significant data engineering challenges specific to the finance industry?
How does data engineering contribute to regulatory compliance in the finance industry, such as data privacy or anti-money laundering regulations?
What are the benefits of implementing data engineering solutions for financial institutions regarding operational efficiency and cost savings?
Are there any emerging trends or innovations in data engineering specifically impacting the finance industry?
Do data engineering solutions work with big data in finance?
What is financial data aggregation?
How is data transforming the financial industry, and what are the key driving factors behind this transformation?