Financial Services Solutions
Data Engineering & AI Systems for Financial Services
From core banking modernization to fraud detection. We build data infrastructure for banks, capital markets and payment gateways. Deployment, access and data handling requirements are agreed before work starts.
< 35ms
Fraud Detection Latency
Agreed controls
Infrastructure requirements
100%
Audit Trail Accuracy
14 Days
To Project Kickoff
Solving Core Financial Services Bottlenecks
Legacy Core Banking Silos
On-premise mainframes lock data away, making real-time analytics impossible.
Cloud Data Modernization
Migrate core banking data to the agreed cloud environment. Access, validation and cutover controls are defined in advance.
Batch Processing Vulnerabilities
Identifying fraudulent transactions hours after they occur due to outdated ETLs.
Event-Driven Streaming
Deploying Apache Kafka to analyze and block anomalous transactions in milliseconds.
Regulatory Reporting Overhead
Manual data gathering for KYC/AML audits leads to fines and human error.
Pipelines for regulatory reporting
Creating immutable, automated data trails that satisfy regulators.
Engineering & AI Capabilities
Real-Time Fraud Prevention
We build ML pipelines that analyze behavioral biometrics and transaction velocity to block fraud without increasing false positives.
Let's talkAlternative Credit Scoring
Ingesting non-traditional data streams to train highly accurate risk models, expanding your lending portfolio safely.
Let's talkHigh-Frequency Data Pipelines
Architecting ultra-low latency infrastructure to ingest market feeds and power predictive algorithmic trading.
Let's talkMission-Critical Infrastructure Monitoring
We don't build generic apps. We engineer the underlying data pipelines that power real-time financial decisions with absolute mathematical accuracy.
Transaction TPS
12.4K
Real-time transaction throughput across payment gateways.
Fraud Pipeline Latency
< 35ms
Sub-millisecond anomaly detection response time.
Risk Model ROC-AUC
0.94
Credit risk model performance curve.
AML/KYC Flags
847
Compliance flags requiring manual review.
Provisioned via: Kafka • Databricks • Python
How We Integrate
Architecture & Security Audit (Week 1)
We review your infrastructure, access controls and stated compliance requirements to define the engineering scope.
Pipeline & Streaming Setup (Weeks 2-4)
Deploying data pipelines and core banking connections with agreed access controls.
ML Deployment & Handover (Weeks 5-8)
Rolling out fraud detection models, credit scoring algorithms, and full documentation handover to your team.
Who it is for
For banks, payment companies and FinTechs that need reporting and data infrastructure built under regulatory constraints.
A good fit
- Customer and transaction data sit in on-premise core banking systems.
- Fraud checks run on batch jobs, hours after the transaction.
- KYC and AML audit data is still gathered by hand.
- Security and compliance must approve where data lives and who can access it.
Not a fit if
- You need engineers inside your own team. Staff Augmentation covers that.
- You want an off-the-shelf fraud or compliance product rather than infrastructure your team owns.
Expected ROI Within the First Quarter
Latency
To detect and block anomalous transactions.
Data Loss
During legacy-to-cloud migrations.
False Positive Reduction
Preventing blocked valid cards.
Proof
Results from governed reporting and data platform work
See all case studiesFrequently Asked Questions
Get in touch
Start with one problem worth solving
Tell us the outcome you need and the systems involved. We reply within one business day and tell you whether there is a useful first step, or why there is not. No pitch either way.
Email us directly
sales@3alica.com