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Financial Data Systems: Integrated Infrastructure for Real-Time Reporting

Innovative use of technology to improve and automate the delivery of financial services, leveraging AI in the finance industry.

Let your data create value

AI-Driven Fintech Services

We manage vast amounts of financial data—securely, efficiently, and thoughtfully —using AI in finance.

Financial Data Integration and Visualization

Unite data from legacy systems, different standards, and APIs. AI in banking and finance 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 agents in finance for real-time fraud detection and prevention identify unusual transaction patterns, verify user identity, and implement anti-money laundering (AML) measures.

Fintech Scalability and Performance

Scale your infrastructure to manage large volumes of transactions and ensure 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, with AI/ML in finance enhancing scalability.

Big Data Analytics

Derive insights from large volumes of financial data. Generative AI in finance 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 in corporate finance tools automates data entry, transactions, and report generation; analyzes workflows to suggest improvements and automate unstructured data processing. AI in accounting and finance 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 in personal finance algorithms, particularly those in machine learning, analyzes historical customer data. They segment customers into distinct groups and predict the outcomes of different marketing strategies.

Benefits of AI In Finance

Benefits of using our services and solutions:

Informed and timely decisions for investments using AI in the finance industry.

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Greater accuracy in reporting and analytics with AI use in finance.

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Reducing manual efforts and improving operational efficiency through AI agents in finance.

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Gaining immediate insights into market trends.

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Acting on opportunities and threats swiftly via AI in finance.

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Reducing the risk of non-compliance and penalties, enhancing security with AI in banking and finance.

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Higher customer satisfaction and loyalty with AI/ML in finance.

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Enhancing overall operational efficiency using AI in corporate finance.

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Boost Work Efficiency and Accuracy with Expert Machine Learning Support.

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Cases of Using Artificial Intelligence and Machine Learning in Finance

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

All Success Stories

Finance

Web and mobile development

Fintech

Chargeback Management B2B SaaS Platform

Chargeback is a powerful B2B SaaS platform designed to revolutionize chargeback management. With enterprise-grade API integrations (Ethoca, Verifi), seamless payment processor connections (Stripe, Shopify, Braintree and others), and real-time dispute alerts, it empowers merchants to stay ahead of chargebacks before they happen. Featuring dynamic billing and intuitive dashboards, Chargeback helps businesses retain more revenue, reduce operational costs, and enhance trust with payment processors and customers.

of transactions related to the incoming alerts are successfully identified

of identified transactions are automatically refunded

View case study

Chargeback Management B2B SaaS Platform

Web and mobile development

Finance

Fintech

Streamlining Investment with AI-Powered Scoring

Dataforest developed an AI-powered web application for an investment firm focused on sustainable tech ventures. The platform streamlines workflows by collecting and integrating data on European start-ups into a dynamic database. Using AI-driven scoring and predefined criteria, it ranks companies, helping investors easily identify and evaluate top opportunities.

companies scraped

2200

parameters used for scoring

View case study

Streamlining Investment with AI-Powered Scoring

All Success Stories

Would you like to explore more of our cases?

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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. 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, delivering effective data engineering solutions. AI in finance has been a key focus for many of our clients, ensuring the solution aligns with the latest industry needs.

Step 2 of 7

Discovery

So, you have finally decided that you are ready to cooperate with DATAFOREST. Data engineers gather requirements, analyze existing data 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. This includes a strong emphasis on using AI in finance, which helps to ensure the project is cutting-edge and relevant to current trends, such as AI/ML in finance.

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. We also consider generative AI in finance to innovate within the solution, ensuring it brings advanced capabilities to your system.

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. For AI in banking and finance, this allows us to integrate complex algorithms that handle a range of financial operations, including risk assessment, transaction processing, and personalized finance solutions.

Step 5 of 7

Project Wide QA

Quality Assurance 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. This also includes testing solutions that leverage AI in the finance industry to ensure accuracy in financial transactions and accounting practices.

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. The deployment often includes modules that handle AI in personal finance, ensuring a smooth and secure implementation for end-users.

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. We focus on AI agents in finance to automate tasks, boosting efficiency, reducing human error, and ensuring that the solution adapts with the latest financial innovations.

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

All publications

November 27, 2023

13 min

Data Science Illuminates Finance

November 20, 2023

14 min

Financial Risk Management in 2025: Walking on the Tightrope

April 3, 2023

17 min

Big Data Analytics in Finance: Navigating Data for Financial Success

April 29, 2025

12 min

Predictive Insurance: Historical Data for Actionable Foresight

March 31, 2025

19 min

Digital Transformation Trends in Insurance — Customer Expectations

December 6, 2023

12 min

Data Science in Insurance: A Healthy Person Approach

All publications

Still have questions about data science services?

How does data engineering help finance make better investment decisions or manage risks?

Data engineering helps finance professionals collect, process, and integrate diverse financial data, enabling them to access comprehensive and accurate information for making better investment decisions and managing risks effectively, using AI in finance.

What are some significant data engineering challenges specific to the finance industry?

Some primary data engineering challenges specific to the finance industry include handling large and complex financial datasets, ensuring financial data security and privacy compliance, and integrating data from disparate sources while maintaining accuracy and real-time processing. Also, there are challenges in data science for finance.

How does data engineering contribute to regulatory compliance in the finance industry, such as data privacy or anti-money laundering regulations?

Data engineering in finance solutions ensures regulatory compliance in the finance industry by implementing robust data governance, data lineage tracking, and data quality processes, ensuring the secure handling and seamless integration of data to meet data privacy and anti-money laundering regulations. It’s the work for data science and finance collaboration for risk analytics in banking, e.g., AI/ML in finance.

What are the benefits of implementing data engineering solutions for financial institutions regarding operational efficiency and cost savings?

Implementing data engineering solutions for financial institutions results in improved operational efficiency and cost savings by automating data processes, streamlining data integration, and optimizing data storage, leading to reduced manual efforts, faster data access, and minimizing infrastructure costs, especially with AI in corporate finance.

Are there any emerging trends or innovations in data engineering specifically impacting the finance industry?

Emerging trends and innovations in data engineering include real-time data processing, the use of distributed ledger technology (DLT) like blockchain for secure transactions, and the adoption of cloud-based data solutions. They are explicitly impacting the finance industry by enabling faster, safer, and scalable data management and AI in the finance industry.

Do data engineering solutions work with big data in finance?

Data engineering solutions handle big data in the finance industry by employing advanced data processing techniques, distributed computing frameworks, and efficient data storage systems, ensuring that large volumes of financial data are ingested, processed, and analyzed swiftly with AI in the finance industry.

What is financial data aggregation?

Financial data aggregation is the collection and consolidation of financial information from multiple sources into a unified view, facilitating comprehensive analysis and reporting.

How is data transforming the financial industry, and what are the key driving factors behind this transformation?

Big data finance is revolutionizing how financial institutions manage and analyze vast amounts of information. The unity between finance and data analytics is driving more innovative investment strategies. The role of big data in the finance industry is expanding as businesses seek more insights from their data, with AI in finance leading the charge.