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Big Data in FinTech: From Transaction Records to Trading Decisions

Q: Can your solution work as a microservice alongside our current fintech infrastructure?

We architect financial technology solutions as containerized microservices with REST/GraphQL APIs that integrate through standard protocols. Each service maintains independent scaling and deployment while communicating via message queues or direct API calls. Your existing systems continue operating while new services handle specific FinTech data analytics or AI functions.

Q: Do you provide API-first solutions that can be embedded into our existing fintech marketplace?

Our development starts with API design, creating endpoints that embed directly into marketplace workflows. We provide SDKs, webhooks, and real-time data streams with authentication and rate limiting built in. Integration typically requires minimal changes to your existing architecture supporting FinTech data operations.

Q: How quickly can you deploy a data analytics dashboard for your financial services portfolio?

Basic dashboards can be connected to existing customer data in finance within 1–2 weeks using our templated approach. Custom visualizations and complex transformations extend timelines to 4–6 weeks. We prioritize essential metrics first, then iterate based on user feedback, ensuring quick ROI from data analytics in FinTech.

Q: How do you ensure compliance with PCI DSS, GDPR, and other financial regulations across different jurisdictions?

We build compliance into the architecture through encryption, access logging, and FinTech data privacy and residency controls. Our team maintains certifications in major financial regulations and implements jurisdiction-specific requirements. Automated monitoring alerts on policy violations before audits occur—supporting effective data governance in FinTech.

Q: Can your fraud detection models be customized for your specific types of financial transactions (e.g., lending payments/trading)?

Models train on your transaction history to learn patterns specific to your business model and customer base. We adjust feature selection, risk scoring, and alert thresholds based on your tolerance for fraud and the types of transactions you are willing to accept. Continuous learning adapts to new fraud techniques targeting your specific FinTech data protection needs.

Q: How do you optimize database performance for high-frequency trading and payment processing workloads?

We implement columnar databases with in-memory caching and connection pooling for sub-millisecond response times. Query optimization encompasses custom indexing strategies and FinTech data storage techniques. Load balancing across multiple database instances prevents bottlenecks during peak trading hours.

Q: Can you rebuild your existing data pipelines to handle increased transaction volumes without downtime?

We create parallel pipeline infrastructure that processes FinTech data alongside your current system during migration. Traffic gradually shifts to new pipelines with real-time monitoring and automatic rollback capabilities. This ensures resilience and supports rapid scale, typical of modern FinTech and big data analytics solutions.

DATAFOREST offers AI-driven FinTech services that integrate, analyze, and visualize financial big data. We automate processes and streamline API integration for FinTech to reduce manual efforts while ensuring regulatory compliance. Unified data systems provide real-time insights through custom analytics solutions and high-impact dashboards, empowering FinTech data analysts to act more quickly.

Let your data create value

Complete FinTech Services

We automate everything from customer support to fraud detection, cutting manual work by up to 70% while improving financial data security, compliance, and customer satisfaction across your mobile payment systems and FinTech operations.

Agentic AI Solutions

Our agentic AI solutions use conversational interfaces and NLP to automate 70% of routine customer support tasks, reducing call center costs while improving satisfaction. The solution scales personalized assistance through intelligent task automation and machine learning in finance.

Data Management & Analytics

DATAFOREST's analytics platform integrates non-traditional data sources with ML forecasting to predict market volatility and customer behavior. This enhances investment strategies through advanced risk assessment algorithms, improving portfolio performance by leveraging FinTech data science.

Customer Segmentation & Personalization

AI analyzes FinTech transactional data enrichment and behavior patterns to create dynamic profiles and deliver a personalized banking experience. This targeted approach achieves 25% higher retention and 30% better conversion rates through tailored user experiences in digital banking trends.

Predictive Market Modeling

Our machine learning in finance models forecast market trends and assess investment risks through algorithms that analyze market volatility and customer behavior patterns. The solution mitigates market risks while enabling data-driven decision-making powered by predictive analytics in FinTech.

Custom Client-Facing Portal Development

We build unified custom client portal experiences with personalized dashboards and secure FinTech data storage to replace fragmented digital tools. The platforms reduce support calls by 40% enhancing the efficiency of cloud computing in FinTech and big data analytics.

Custom Financial Operations Platforms

DATAFOREST automates fund distribution, chargeback management, and internal reporting workflows specific to each client's business logic. These systems cut manual reconciliation work by 60% and speed up closures through better accuracy, enhancing the efficiency of FinTech and big data analytics.

Payment Processing Integration

Our team connects payment gateways through APIs and automates settlement reconciliation across multiple providers. This eliminates disconnected systems and reduces the operational overhead of manually tracking FinTech data in real-time financial analytics flows.

Fraud Detection & Prevention

We monitor transactions in real-time using ML algorithms that learn fraud patterns and customer behavior. The system catches 95% of fraud attempts while cutting false alarms that annoy legitimate customers, leveraging big data FinTech and behavioral signals.

Regulatory Compliance Automation

DATAFOREST automates compliance reports and tracks regulatory changes so you don't miss deadlines. The system maintains audit trails and automatically updates requirements, cutting audit preparation time to near zero and ensuring compliance automation and FinTech data privacy.

AI-Driven Financial Services Development: Data Engineering & AI Solutions

Turn FinTech data into profit-driving automation that cuts costs, catches fraud, and keeps customers coming back.

AI personalization increases cross-sell rates by matching offers to individual customer needs, using FinTech data science.

I want it!

Predictive churn models and 24/7 custom client portals help keep clients longer, boosting their lifetime value.

I want it!

Automated compliance, reconciliation, and support cut costs by eliminating manual work.

I want it!

Dynamic segmentation and behavioral insights deliver tailored experiences that improve loyalty through data science in FinTech.

I want it!

Real-time FinTech data and predictive models enable instant responses to market changes.

I want it!

ML-powered fraud detection and risk algorithms protect assets and reputation.

I want it!

Automated regulatory reporting reduces compliance time, cost, and risk.

I want it!

Intelligent platforms accelerate month-end closes and improve financial visibility with big data in FinTech.

I want it!

Risk models built in 2018 can't predict 2025 markets.

We rebuild them with FinTech innovation that matters now. Old models lose money.

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Fintech And Big Data Analytics Cases

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

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Big Data Fintech Technologies

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 Big Data in Fintech

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 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 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.

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 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.