Data solutions for financial service providers & insurance companies
We help banks, financial service providers and insurance companies use their data as a real competitive advantage. Our solutions ensure reliable reporting, automated processes, and data-based decisions. And thus for noticeable cost reductions, risk minimization and greater agility in the market.
Turn your data into a strategic advantage
Banks and insurance companies are facing unprecedented challenges. Regulatory requirements and ESG standards are constantly increasing. Customers expect real-time services and the highest level of data security. At the same time, fragmented legacy systems and manual processes are jeopardizing competitiveness. A lack of transparency in data flows increases the risk of compliance violations and makes it difficult to make informed management decisions in real time. Those who seize this opportunity now and leverage their data strategically will not only strengthen compliance but also secure margins and profitability in the long term.
We help you consolidate data across silos, automate processes, and make data actionable—for you, your team, and the entire company.
With deep industry expertise, we analyze your data landscape, establish robust processes, and make information available exactly where it’s needed. So you can unlock the full potential of your data.
Our established solutions for financial service providers & insurance companies
Questions our customers ask
- Which data initiatives have the biggest economic impact—from cost reduction to risk management to new business models?
- How do we design agile data governance that meets data protection, risk management and regulatory reporting requirements (e.g. GDPR, BCBS 239, MiFID II, Solvency II)?
- How do we avoid data silos and create a consistent understanding across departments and systems?
How we support your financial or insurance company
- Development of a holistic data strategy with a top-down and bottom-up approach that combines efficiency gains and regulatory security
- Establishment of use case roadmaps including prioritization according to business impact & compliance relevance
- Conducting data thinking workshops and maturity assessments
- Development of role & authorization models for data ownership & governance
success stories
For RheinEnergie, we have developed a central data strategy that is interlinked with corporate and divisional strategies. The strategy promotes a decentralized data culture and strengthens self-service and governance — as a basis for scalable, future-proof data use.
Our customers were concerned with these questions
- How do we integrate data from inventory management or core banking systems, claims or risk management, and CRM/DMS centrally and securely?
- Which architecture simultaneously meets data protection, auditability and future security requirements?
- How can regulatory-relevant data be automatically prepared for reporting and auditing?
How we support your financial or insurance company
- Build scalable, cloud-based data platforms
- Development of robust data pipelines for regulatory reporting and customer data integration
- Implementation of automated monitoring and logging to shorten audit processes and make risks visible at an early stage
success stories
For EVM (Energieversorgung Mittelrein), we have brought together, harmonized and enriched data from historically developed systems with external data. The modular data modeling approach enables future expansions, and the modern lakehouse structure provides a stable basis for AI initiatives.
Our customers were concerned with these questions
- How do we automate reporting processes to free up resources in controlling?
- How do we identify risks or cases of fraud before they cause high costs?
- How do we increase customer satisfaction through personalized, data-driven services?
- How do we support our departments with self-service analytics and role-based access?
How we support your financial or insurance company
- Development of interactive dashboards
- Implementation of advanced analytics use cases (churn prediction, fraud detection, ESG monitoring)
- Development of self-service BI that empowers teams and reduces external consulting costs, including training concept
- Automated reporting that saves time and increases the accuracy of regulatory reports
success stories
For RheinEnergie, we have laid the central basis for data-driven analyses. Data thinking workshops and structured use case development resulted in a clear roadmap for BI applications and self-service analytics. For evm, we created a 360° customer view by building a customer data platform.
How we have already successfully supported other financial service providers & insurance companies
Modern data architecture
Together with Aachener Grundvermögen, we have set up a modern, data-based infrastructure that supports the efficient handling of large amounts of real estate data. The use of the data vault approach ensures high data quality. At the same time, a flexible architecture is being created that enables both the connection of various source systems and future expansions without any problems.
More efficiency, less effort — made measurable
linked data points
fewer manual reports
Single Source of Truth
Our solutions for network providers
Transparency about network investments & maintenance measures
We provide an overview of investment planning and maintenance — from medium voltage to local network level. With comprehensible data and visualizations for technology, controlling and regulatory reporting.
Securing grid stability through intelligent processes
We support you in implementing legal requirements such as Redispatch 2.0 — with sophisticated data processes that reliably combine generation, network operation and planning.
Integration of geodata & spatial reference into BI landscapes
We integrate geodata into existing BI systems — e.g. for network expansion planning, capacity assessment or location decisions. This makes spatial reference an integral part of data-based decisions.
Reliable automation of market communication
We make market processes such as supplier changes, MaKo2024 or format changes robust and maintainable — through clean data structures, automated test tracks and clearly defined workflows.
Data-based control of network connections
We help structure and accelerate grid connection processes for PV systems, charging infrastructure or heat pumps based on data — with automated preliminary checks, bottleneck checks and transparent monitoring.
Cross-project reporting governance
We help build a consistent reporting landscape across projects and specialist areas — with coordinated key figures, clear processes and sustainable data management.
From strategy to self-service: This is how we support financial service providers and insurance companies on their data journey.
Define status quo & use cases
We develop a target image, prioritize use cases and develop a roadmap, tailored to your strategic goals.
Modernize infrastructure
We create the basis with a scalable, secure and maintainable data platform.
Automate processes
Data pipelines, reporting routes and quality assurance are automated — for stability, efficiency and compliance.
Empower teams
We make your departments fit for data — with targeted enablement in Power BI, Tableau & Co., so that you can work independently in the long term.
Scaling
Together, we look at the impact, close gaps and create structures for sustainable success.
FAQ
A uniform understanding of data is created by centrally combining, harmonizing and modelling data from various systems in a technically consistent manner. This results in comparable key figures that can be used for both operational and regulatory reports.
Automating regulatory reporting requirements requires that data processes, audit rules, and data models are standardized and embedded in repeatable workflows. Reports and messages can then be reliably automated, which reduces effort and minimizes error risks.
Modern analysis models combine transaction data, customer behavior, and historical patterns to identify unusual activities. Real-time analyses and defined outlier tests help to identify fraudulent activity more quickly and to be able to initiate appropriate measures.
A data strategy links use cases with business goals, creates clarity in roles and provides a roadmap for scalable data projects, taking compliance and risk management into account.
