endorsedCompleted

Predictive Fraud Detection Platform & Staff Augmentation for Future Finance Poland

Services Covered:

Custom Software Development • Staff Augmentation

Industry

Financial Services

Duration

4 months

Budget

10K - 49K

Start Date

1 November 2025

Team Size

6-10 Employees

Engagement Model

Offshore

Project Capability Score (PCS)

90/100

Strong

Project Capability Score (PCS) estimates how capable Acquaint Softtech Private Limited is of successfully delivering a project like this, based on its past experience, track record, and ability to handle similar work. Learn more about PCS

Client

Future Finance Poland

Poland

Project Summary

Acquaint Softtech provided IT staff augmentation and custom software development for a financial-industry client (Future Finance Poland). The vendor built Python-based, data-driven systems — including predictive fraud detection models with real-time scoring, automated retraining pipelines, integration APIs, and operational dashboards — and embedded developers and data engineers into the client's teams. The engagement reduced false positives, allowed legitimate transactions to proceed smoothly, and gave leadership near real-time insights while demonstrating strong project management and responsiveness.

Key Challenges

Build robust data-driven systems to monitor and analyze financial transactions across multiple institutions
Implement predictive fraud detection tools that adapt in real-time, reducing false positives while ensuring genuine transactions aren’t blocked
Provide IT staff augmentation for Python development and data engineering to scale development velocity without sacrificing quality

Project Deliverables

End-to-end fraud detection models with real-time scoring
Automated retraining pipelines for models
Integration APIs connecting predictive analytics to transaction monitoring and external/internal systems
Operational dashboards with drill-down capability
Dedicated team augmentation supporting internal Python and analytics development

Project Solution

Acquaint Softtech approached the engagement with a full-stack mindset and delivered Python-based predictive models for transaction monitoring and anomaly detection. They implemented automated retraining pipelines so models could adapt to evolving fraud patterns, and built APIs to integrate predictive analytics with internal databases, external partners, and reporting systems. The vendor embedded Python developers, data engineers, and QA specialists into the client's teams and delivered intuitive reporting dashboards for leadership and operations.

Project Outcome

Delivered tangible improvements almost immediately, reducing the volume of false positives
Allowed fraud team to focus on genuinely risky transactions while legitimate transactions proceeded without unnecessary blocks
Automated retraining ensured risk scoring adapted to new fraud patterns over time
Leadership gained access to near-real-time insights, accelerating decision-making and operational efficiency
Milestones were met on time and the vendor adapted quickly to regulatory changes without interrupting operations

Platforms

Tech Stack

Python
REST API

Client Endorsement

Overall Review Rating

Timeliness

Cost Rating

Willing to Refer

Quality of Deliverables

“Acquaint Softtech Private Limited delivered tangible improvements, enabling the client to focus on genuinely risky transactions and allowing legitimate customer transactions to go through smoothly. The team demonstrated excellent project management and was responsive and proactive.”
Pawel Widawski
President
Date: 03/28/2026