- Legacy System Modernization
- AI & ML
- Automation
- Real-Time Solution
- Cloud Solutions
- Platform Development
AI‑driven modernization of legacy fraud detection systems for a fast‐growing digital bank. Creation of a unified, real‑time fraud monitoring platform spanning accounts, payments, lending, and cryptocurrency trading. Leveraging big data processing, behavioral analytics, and automated risk scoring with scalable workflows and instant alerts for heightened security and operational efficiency.
client
NDA Protected
Sweden
50-100 employees
A fully digital bank offering online financial services, which includes current accounts, instant payments, digital lending, and cryptocurrency trading. The company relies on AI‑powered automation and embedded finance solutions to deliver seamless, secure, and scalable banking experiences to its customers.
Since the company manages a growing number of transactions with sensitive customer information, the company faced a growing volume of critical data breach attempts. To safeguard against digital threats, the company wanted to adopt artificial intelligence fraud detection in banking operations.
request background
Digital Bank Fraud Detection Software
As transaction volumes surged and cyber threats evolved, the bank faced mounting fraud risks, from identity theft and account takeovers to sophisticated payment scams.
To safeguard assets, ensure regulatory adherence, and maintain customer trust, the client sought an AI fraud detection solution. It had to be capable of real‑time anomaly detection, behavioral risk scoring, and seamless integration with existing systems.
challenge
Automated, Compliant & Scalable AI Fraud Detection System
As the bank grew rapidly and transaction volumes surged, it began to experience a sharp uptick in sophisticated fraud attempts, ranging from identity theft and account takeovers to complex payment scams. Its legacy, rule‑based detection systems struggled to keep pace: fraudulent transactions often slipped through unnoticed or were caught too late to prevent financial loss.
At the same time, stringent Swedish and EU regulations around AML and KYC added another layer of complexity. Thus, the AI fraud detection banking software had to balance rigorous compliance checks with the need for real‑time threat interception.
Maintaining customer trust also became a critical concern: any false positives or service interruptions risked eroding confidence in the bank's digital‑only model. Finally, with operations scaling quickly, the existing solution showed its limits, unable to adapt dynamically to emerging fraud patterns without sacrificing performance or introducing delays.
These intertwined challenges highlighted the need for a more intelligent, scalable, and compliant approach to fraud prevention.
goals
- Optimize fraud detection by implementing an AI‑driven system to identify suspicious activity in real-time, reducing losses.
- Enhance scalability and build a solution capable of handling growing transaction loads with low latency.
- Ensure compliance with embedded AML and KYC checks to meet regulatory mandates and minimize audit risks.
- Elevate customer confidence and maintain seamless, uninterrupted service while strengthening security.
- Automate workflows through AI-driven investigation of response processes with behavioral analytics and risk scoring.
solution
Fraud Detection Systems Used by Banks: Best Practices Implemented
.NET Core, C#, ASP.NET Web API, Entity Framework Core, TensorFlow.NET, ML.NET, Apache Kafka, Apache Spark, PostgreSQL, Redis, Azure Kubernetes Service, Azure Monitor, Docker, RESTful APIs, Prometheus, Grafana, Keycloak
Ongoing- 9 specialists
We designed and built a unified fraud detection platform integrating cutting‑edge AI and machine learning models to preemptively identify fraudulent activities across all channels. We leveraged a distributed architecture that ingests, processes, and analyzes transactional data, ensuring immediate response to emerging threats.
By implementing a horizontally scalable data pipeline, the system accommodates surges in transaction volumes without any latency spikes. Regulatory compliance workflows for AML and KYC were embedded directly into this pipeline to automate checks and maintain full auditability.
The platform's modular design allows the client to introduce new fraud patterns and custom business rules without needing a full system redeployment. We complemented the technical implementation with expert consulting to align the solution with the bank's internal security policies and operational workflows.
To fully adopt artificial intelligence fraud detection in banking operations, we implemented comprehensive training sessions and detailed documentation. The services were delivered to empower the client's security team in managing, maintaining, and evolving the platform.
- Supervised & unsupervised ML models trained on historical and real‑time transaction data to detect both known and novel fraud patterns.
- Behavioral risk scoring engine assigns dynamic risk scores based on multi‑dimensional user behavior analytics and contextual transaction attributes.
- A low‑latency data pipeline utilizes Apache Kafka and Spark Streaming to process millions of events per hour with sub‑second latency.
- Compliance automation fraud detection AI modules seamlessly integrate AML screening and KYC verification into transaction workflows, flagging suspicious activities automatically.
- API‑first integration exposes RESTful endpoints for easy connectivity with core banking systems, payment gateways, and third‑party analytics tools.
- Custom alerting dashboard provides security analysts with real‑time monitoring, case management capabilities, and automated incident escalation.
- Scalable cloud infrastructure deployed on Azure Kubernetes Service with autoscaling, high availability, and disaster recovery configurations.
- Continuous model retraining implements feedback loops and automated retraining pipelines to refine predictive accuracy and adapt to evolving threat landscapes.
outcome
Banking Fraud Detection Software with High-End Data Protection
- 40% reduction in fraudulent transactions through proactive anomaly detection.
- 30% increase in operational efficiency by automating case triage and alert workflows.
- 25% decrease in compliance costs via embedded AML/KYC checks and streamlined audit reporting.
client feedback
Working with Acropolium has been a great experience. They really know their stuff when it comes to AI and real-time inefficiency detection. The new system is super quick and easy to use, and it has already helped us cut down on fraud in a big way. Our team feels way more in control now, and our customers definitely feel safer, too.
