IT Staff Augmentation for a European FinTech Investment Platform
Bitpanda needed more engineering capacity without slowing its fintech platform. Through IT staff augmentation, Acquaint Softtech embedded developers directly into its product squads and supported software development across the web platform and fraud detection. The result was steadier feature delivery, a more responsive investment experience, and faster, more focused fraud investigations.
7M+
USERS ON THE BITPANDA PLATFORM10,000+
STOCKS, ETFS AND ETCS AVAILABLE2+
PARALLEL ENGINEERING WORKSTREAMS
CLIENT STORY
Bitpanda is an Austrian investment platform on a mission to make investing safe, easy and accessible to everyone. More than 7 million users trade over 650 cryptocurrencies and more than 10,000 stocks, ETFs and metals, starting from just €1. The company is licensed by the FMA in Austria and BaFin in Germany, is MiCAR compliant across the EU, and holds ISO 27001 certification.
Bitpanda wanted to move faster on customer-facing web features without slowing ongoing maintenance. It also wanted to go beyond static, rule-based fraud checks. Bitpanda partnered with Acquaint Softtech for IT staff augmentation and software development across two parallel workstreams: embedded web engineering and fraud detection and risk intelligence.
Country
Austria
Industry
FinTech
Team Size
6-10 Professionals
Hours Invested
1,208 Hours
Technology
Next.js
Project Background
In a fast-moving fintech environment, Bitpanda’s internal teams were balancing new features, platform upkeep, and fraud prevention at the same time. Acquaint Softtech stepped in to add engineering bandwidth and to support development of a more intelligent transaction monitoring layer.
Engineering Bandwidth: Adding embedded engineers so internal teams could accelerate customer-facing web features without slowing platform maintenance.
Investment Platform Experience: Enhancing the web interface for portfolio management, transaction tracking, and account dashboards.
Smarter Fraud Detection: Strengthening the fraud detection framework with behavioral analytics and machine learning that identify suspicious patterns better than static rules.
Their engineers were capable of working inside a complex fintech architecture while maintaining clean code practices and strong security awareness.
Research & Ideation
Acquaint Softtech structured the engagement around Bitpanda’s existing agile practices. The aim was to add predictive intelligence to the existing monitoring framework while maintaining system performance and regulatory compliance.
Two Parallel Workstreams
Running embedded web engineering and a data-driven fraud detection initiative side by side, so neither slowed the other.
Built Into the Squads
Working within Bitpanda’s sprint cycles, stand-ups, and code reviews, alongside internal architects and QA teams.
Compliance Checkpoints
Setting structured review points where risk and compliance teams checked model outputs before deployment.
Creative Design
For investors, design meant a portfolio experience that feels quick and clear. The team enhanced the web interface for managing investment portfolios and optimized transaction history and account dashboards. For risk analysts, design meant context: flagged transactions arrive with richer behavioral detail and dedicated dashboards for investigation.
Project Challenges
Stretched Engineering Capacity
Internal teams had to deliver new customer-facing features while keeping ongoing platform maintenance on track. Without extra bandwidth, one would slow the other.
Performance Under Trading Pressure
Transaction history and account dashboards needed to stay responsive and reliable, including during periods of increased trading activity.
Legacy Code Maintainability
Certain legacy components were harder to maintain. They needed careful refactoring inside a complex, live fintech architecture.
Limits of Rule-Based Fraud Checks
Static rule-based systems struggled to identify suspicious transaction patterns effectively. Bitpanda needed predictive intelligence without hurting performance or compliance.
Our Solutions
Acquaint Softtech’s IT staff augmentation model placed engineers inside Bitpanda’s teams as full contributors rather than external contractors, delivering both web development and fraud detection work.
Embedded Engineering Support
Developers integrated directly into Bitpanda’s internal product squads and contributed to several ongoing platform initiatives.
Portfolio and Dashboard Enhancements
Enhancements to the portfolio management web interface, plus performance optimization for transaction history and account dashboards.
Secure API Integrations
Support for secure API integrations between backend financial systems and the web platform, improving responsiveness and reliability during busy trading periods.
Legacy Component Refactoring
Refactoring of selected legacy components to make the codebase easier to maintain.
Advanced Transaction Monitoring Layer
A more advanced monitoring layer that identifies suspicious behavior patterns and adds predictive intelligence to Bitpanda’s existing framework.
Custom Functionalities
The fraud detection workstream introduced custom features built for real operational use rather than theoretical experimentation.
Near Real-Time Risk Scoring Engine
A risk scoring engine that analyzes transaction behavior in near real time and adapts scores to highlight genuine risk.
Behavioral Anomaly Detection
Statistical models that detect unusual activity patterns across user accounts, using device usage, transaction timing, and trading activity.
Risk Analyst Dashboards
Internal dashboards that help risk analysts investigate flagged transactions more efficiently.
Automated Model Retraining
Automated pipelines that retrain models as new data becomes available, keeping detection current.
On Going Support
Throughout the engagement, Acquaint Softtech’s embedded engineers supported Bitpanda’s platform beyond new feature work, keeping day-to-day engineering steady in a fast-moving fintech environment.
Platform Maintenance Support
Added engineering bandwidth kept ongoing platform maintenance on track while internal teams moved faster on new features.
Reliability During Trading Peaks
Work on responsiveness and reliability helped the platform perform during periods of increased trading activity.
Model Upkeep
Automated retraining pipelines keep fraud detection models learning as new data becomes available.
Project Outcome
Bitpanda’s engineering teams began delivering new platform features more consistently. The added developers reduced backlog pressure, freeing internal engineers to focus on strategic components. Portfolio management became more responsive and easier to navigate, and support teams reported fewer tickets related to transaction visibility.
On the risk side, compliance analysts now receive alerts with richer behavioral context, making investigations faster and more focused. The adaptive risk scoring system also helped reduce unnecessary transaction blocks while still highlighting suspicious activity patterns.
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