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Reduce booking time by 95% and eliminate errors with intelligent invoice processing automation powered by Ishiros AI technology.
Challenge: Dynamic Stability
In the world of modern fintech, data is the new currency — but only when processed in real time. Our client, a growing neobank focused on micro-investments, faced serious problems as their user base surpassed 500,000 active members. Their existing fraud detection system was reactive — it identified suspicious transactions hours after they occurred, leading to significant financial losses and eroding user trust.
At the same time, operational costs (OPEX) were growing exponentially. The identity verification (KYC) and anti-money laundering (AML) process was partially manual, meaning that during periods of high user influx, the verification process could take up to 3 business days. In a digital age where users expect everything "now and immediately," this directly affected app abandonment rates and slower growth in a competitive market.
The primary goal was clear: transform a passive system into a proactive growth engine capable of processing 10,000 transactions per second with millisecond latency, while keeping the AML error rate at an absolute minimum.
Solution: AI-First Fintech Backbone
Ishiros designed and implemented a solution based on Event-Driven Architecture and highly optimized machine learning models. Our approach eliminated waiting and introduced autonomous decision-making at every critical stage of the customer journey.
1. Real-time Anomaly Detection
We implemented ensemble models that analyze over 200 factors for each transaction in under 50ms. The system recognizes patterns of unusual behavior even for completely new types of cyberattacks, blocking fraud before funds leave the account.
2. Autonomous KYC & AML
Using biometric verification and Computer Vision technology, we automated the onboarding process. The system automatically checks global sanctions lists and politically exposed persons, reducing the need for human review by 90%, with increased accuracy in detecting fraudulent documents.
3. Predictive Customer Support
Instead of classic chatbots, we introduced AI agents that predict user problems based on their behavior in the app (e.g., if a card is declined twice, AI proactively offers a solution before the user submits a support request).
Technical Engineering: Scalability Without Limits
To achieve the desired performance, we used technologies such as Apache Kafka for data orchestration and TensorFlow Serving for fast model inference. Special attention was paid to security (SOC2 compliance) and encryption of data at rest and in transit.
Exceptional success was achieved in reducing false positive rates in fraud detection, saving users thousands of hours of frustration and support calls. The AI engine became the operational core of the neobank, enabling it to aggressively expand into new markets without a proportional increase in the risk control team.
"With Ishiros, our platform became an impregnable fortress. The ability to block fraud attempts in milliseconds while delivering instant experiences to users is what sets us apart from the competition. This isn't just an IT project — this is the HEART of our business."
Next Steps
Deep Audit
We map your processes and plan integrations with your ERP system.
Training & Testing
We configure agents and train models on your specific documents and transaction patterns.