Home page / Finance
Data-Driven Finance
Innovative use of technology to improve and automate the delivery of financial services.
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.View page
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.
Benefits of Data-Driven Finance
Benefits of e-commerce using our services and solutions:
01
Informed and timely decisions
for investments.
for investments.
02
Reducing manual efforts and
improving operational efficiency.
improving operational efficiency.
03
Acting on opportunities
and threats swiftly.
and threats swiftly.
04
Higher customer satisfaction
and loyalty.
and loyalty.
05
Greater accuracy in reporting
and analytics.
and analytics.
06
Gaining immediate insights
into market trends.
into market trends.
07
Reducing the risk of non-compliance
and penalties.
and penalties.
08
Enhancing overall
operational efficiency.
operational efficiency.
Boost Work Efficiency and Accuracy with Expert Machine Learning Support.
Get in Touch Now!
Enra Group is the UK's leading provider and distributor of specialist property finance.
Bank Data Analytics Platform
The Bank Data Analytics Platform project aims to develop a web-based application for the Client and its customers to query and analyze data relating to various banks and other financial institutions. The project involves building an interactive B2B web app with custom dashboards and analytics features, as well as using AI functionality to empower the application's development and analytics capabilities.
They've had a quick grasp of what we are trying to do and delivered to our spec without a fuss.
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.
Initial Project Assessment and Definition
Discovery
Tech Design and Backlog Planning
Development Based on Sprints
Project Wide QA
Deployment and Rollout
Support and Continuous Improvement
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
Related articles
All publications
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.
December 6, 2023
12 min
Data Science in Insurance: A Healthy Person Approach
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.
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.
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.
Are there any emerging trends or innovations in data engineering specifically impacting the finance industry?
Yes, emerging trends and innovations in data engineering, such as real-time data processing, the use of distributed ledger technology (DLT) like blockchain for secure transactions, and the adoption of cloud-based data solutions, are explicitly impacting the finance industry by enabling faster, safer, and scalable data management and analytics.
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 within big data analytics in finance.
What is financial data aggregation?
Financial data aggregation is collecting and consolidating 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 information. The synergy 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.
Let’s discuss your project
Share the project details – like scope, mockups, or business challenges.
We will carefully check and get back to you with the next steps.
Contact us to discuss
your project goals.
your project goals.