AI & Data Analytics
Artificial Intelligence is reshaping financial services, yet many institutions remain stuck in fragmented use cases, isolated pilots and technology-driven experimentation. Our mission is to move AI in banking from selective optimization to industrialized impact.
We believe that sustainable value creation requires more than algorithms. It demands a structured operating model, scalable architecture and a clear transformation path. At our Magpie Projects AI & Data Analytics Innovation Lab, we help financial institutions transition from experimentation to execution, from isolated use cases to AI factories, and ultimately to front-to-end reimagined processes. We do not implement AI for innovation’s sake. We industrialize it to create measurable business impact.
What we do
AI Strategy & Value Creation
- AI strategy & roadmap development
- Use case identification and prioritization
- Business case modelling & ROI quantification
- Data-driven business model design
AI Workshops & Enablement
- LLM & Agentic AI fundamentals
- Prompt & context engineering
- AI architecture & solution design
Data & AI Architecture
- Modern data platforms
- AI & GenAI reference architectures
- Cloud & hybrid setups
- Integration & MLOps concepts
Advanced Analytics, Generative & Agentic AI
- Machine learning & predictive analytics
- Generative & Agentic AI applications
- Automation of analytical processes
- Business impact validation
AI Industrialization & Scaling
- Rapid prototyping & MVP development
- Production deployment
- Integration into core systems
- AI Factory setup
- Monitoring & continuous optimization
AI Governance & Risk
- EU AI Act compliance
- AI risk & control frameworks
- Governance TOM design & implementation
- AI inventory & reporting
Where AI pays off in capital markets
What we bring to production
Agentic AI: autonomous digital co-workers
We develop advanced Agentic AI solutions that go beyond simple automation. Our solutions act as autonomous digital co-workers – capable of reasoning, taking initiative, and executing multi‑step processes.
They integrate seamlessly into existing workflows, enhance decision-making, and accelerate operations across financial services.
AI Factory: industrialized capability
Our AI Factory provides a scalable, end‑to‑end framework for building, deploying, and operating AI solutions across the enterprise. With standardized methods, reusable components, and clear governance, we reduce time-to-value and ensure consistent quality from prototype to production.
The AI Factory enables organizations to industrialize their AI initiatives efficiently, reliably, and with measurable impact.
Agentic and GenAI Evaluation & Configuration
To ensure responsible and high-performing AI systems, we offer a structured approach to evaluating and configuring agentic and generative AI applications.
Our methodology assesses accuracy, consistency, safety, and regulatory alignment, while targeted configuration measures optimize system behavior. This ensures that GenAI solutions are not only powerful but also trustworthy, compliant, and ready for production use.
AI Governance & EU AI Act
We accompany banks in navigating the interplay between new regulation such as the EU AI Act, existing frameworks such as MaRisk and EBA guidelines and internal risk management requirements regarding AI.
Enabling transparency, regulatory compliance and effective AI integration are our main objectives. As part of our portfolio, we support clients in setting up governance frameworks, AI inventories, classification and reporting procedures, as well as validation and control processes.
Selected projects
Email Automation
Exchange (Market Control)
At our client, multiple departmental mailboxes received large volumes of heterogeneous emails: some purely informational, others requiring follow‑up. Employees struggled to consistently classify, store, and respond to messages while ensuring transparency and compliance across the communication process.
Magpie designed and implemented an automation solution using Microsoft Copilot Studio, combining intelligent flows with specialized AI agents. Incoming emails were automatically classified, informational messages filed into predefined structures, and draft responses generated for to‑do emails, incl. human‑in‑the‑loop review. Microsoft Teams notifications kept stakeholders aligned on new items and processing status.
The organization achieved markedly faster response times, less manual sorting effort, and transparent end‑to‑end communication handling. The automated workflow strengthened governance and ensured consistent, high‑quality processing across all mailboxes.
- AI-POWERED EMAIL PROCESSING
- FASTER RESPONSE TIMES
Trade Surveillance
German Commercial Bank
Our client’s trade surveillance functions suffered from incomplete coverage across asset classes, low alert quality, and fragmented data landscapes. The result: high volumes of false positives, inefficient investigations, and compliance risks.
We introduced a quantitative parameterization framework, combining data profiling, statistical analysis, clustering algorithms (e.g. k-means) and backtesting. Additional activities included target‑state documentation, data quality improvements, and optimization of the alert lifecycle within the existing surveillance platform.
The result was a significant reduction in false positives, enhanced detection quality, and a more efficient investigation process. Compliance teams benefited from audit‑ready documentation and a surveillance setup that is both more accurate and more cost‑efficient.
- FEWER FALSE POSITIVES
- IMPROVED DETECTION QUALITY
Sales Intelligence
German Universal Bank
With the shift toward digital onboarding, early customer interactions have become scarce. This limits the ability to identify high‑value (affluent) prospects early in the relationship. Traditional segmentation struggles because transaction histories and behavioral signals are still sparse, causing our client to miss valuable cross‑ and upselling opportunities.
We developed an AI‑driven Affluent Score leveraging internal transaction, interaction, and master data, enriched with external information sources. A machine‑learning model evaluates hidden patterns associated with affluent behavior and is continuously retrained for accuracy. The model was fully integrated into the bank’s marketing infrastructure to generate daily prioritized customer lists for targeted outreach.
The solution led to 12x higher identification precision, 92% fewer calls per confirmed affluent customer, and significantly more relevant product recommendations. The bank now engages the right customers with the right offer at the right time, improving revenue potential and advisor efficiency.
- 12X Higher Identification
- 92% Fewer calls
White PAPERS
What we publish
Quantum-Safe Digital Assets & Crypto Agility
In digital assets, cryptography is the asset itself. Signatures prove ownership and authorise every transfer. In 2026, Google and IonQ published resource estimates for breaking Bitcoin and Ethereum signatures far below earlier figures. The risk is authenticity rather than secrecy, because an exposed public key can be turned into a valid, signed transaction. Deadlines also became concrete: GBIC expects a cryptographic inventory at every German bank by end-2027, and BSI, US Executive Order 14412 and the G7 have set dates of their own. The paper separates what is required from what is merely recommended, explains why German entities inside US groups may face earlier deadlines, and sets out a six-step path to crypto agility.
Sales Intelligence 2.0: Harnessing the Transformative Power of Generative AI
Generative AI (GenAI) creates new content by learning patterns from training data, surpassing classical machine learning in flexibility and output quality.
In Sales Intelligence, it enables hyper-personalization, idea generation, campaign implementation, client interaction analysis, and economic tracking.
Adoption progresses through four stages: browser-based chatbots, API integration, in-house context-sensitive solutions, and fully integrated enterprise systems. Successful deployment requires data security training, enterprise-grade APIs, and effective prompt engineering.
Key challenges include hallucination, context size limits, and intellectual property risks. Financial institutions adopting GenAI early — through strategic evaluation, infrastructure investment, staff training, and tech partnerships — can secure significant competitive advantage.
AI and Advanced Analytics in Anti-Financial Crime Compliance
In today’s complex regulatory landscape, financial institutions face unprecedented risks from money laundering, fraud, and corruption.
Traditional anti-financial crime (AFC) measures are heavily plagued by high false-positive rates, poor data quality, and labor-intensive manual workflows that exhaust valuable resources. This comprehensive whitepaper explores how artificial intelligence and advanced analytics can drive a paradigm shift in compliance management.
Discover how modern technologies automate data preprocessing, optimize parameter tuning, and dramatically refine alert scoring mechanisms. Learn how leveraging advanced AI solutions can boost employee efficiency by 40% to 60% and empower your organization to mitigate financial risks effectively while maintaining bulletproof regulatory compliance.
Machine Learning in Trade Surveillance
Legacy trade surveillance systems produce too many false-positive alerts, driving high manual costs while letting actual market abuse go undetected.
Machine Learning and AI offer a more accurate, cost-effective alternative. Better model parameterization reduces false positives; AI-based alert scoring helps compliance officers prioritize critical cases; and systems trained on historical trading, market trends, and compliance practices generate more meaningful alerts.
NLP standardizes unstructured data, and synthetic data fills availability gaps. Generative AI promises further gains in development, calibration, and investigations. A successful transition requires understanding the technology, careful vendor selection, structured implementation, and proper staff training.
Agentic AI in Compliance
Agentic AI frameworks extend large language models with planning, memory, and tool-use capabilities, enabling autonomous execution of complex, multi-step tasks beyond single-turn interactions.
Frameworks like LangChain, LangGraph, Semantic Kernel, AutoGen, CrewAI, and OpenAI’s Swarm differ in architectural emphasis, from modularity to enterprise orchestration to multi-agent paradigms.
These systems are well-suited to compliance workflows that are multi-step, high-volume, and require traceability. Specific use cases include gap analysis of changing regulations, insider trading detection and investigation, market event materiality assessment, and name list screening.
Limitations include hallucination, fragile tool integration, and transparency gaps, but rapid iteration is addressing these challenges.
Get In Touch
We turn AI and data into measurable business value for financial institutions.
Our team is ready to connect with you and answer all your questions.