Tech4Biz

Building a Scalable AI-Driven Fintech Platform for a Digital Bank

Client

A rapidly growing digital bank (under NDA) serving over 5 million users across Southeast Asia, offering services like personal banking, micro-loans, wealth management, and insurance.

Business Challenge

The client faced three core challenges:

  1. Personalized Customer Engagement: Generic product recommendations led to poor upsell conversion (~2.1%).
  2. Real-Time Risk Management: Traditional rule-based credit scoring could not keep pace with fraud or loan risk in real-time.
  3. Scalable Infrastructure: Daily transactions had grown to 2M+ per day, overwhelming their monolithic core system during peak load.

Objectives

  • Build a cloud-native, microservices-based architecture for horizontal scalability.
  • Implement AI-driven recommendation engines for personalized financial products.
  • Develop real-time risk and fraud detection using ML and graph analytics.

Ensure compliance with global banking standards (PCI-DSS, GDPR, MAS TRM).

Solution Delivered by Tech4Biz

1. Scalable Cloud-Native Infrastructure

  • Deployed a containerized microservices architecture using Kubernetes on AWS.
  • Leveraged Kafka for real-time stream processing of user transactions.
  • Used PostgreSQL + Redis + MongoDB hybrid storage for structured and semi-structured data needs.

2. AI-Powered Recommendation Engine

  • Built a deep learning model (based on transformer encoders) to personalize:
    • Credit card offers
    • Wealth portfolios
    • Insurance bundles
  • Input data: transaction history, app behavior analytics, geolocation, and spending categories.
  • Result: Recommendation CTR jumped from 2.1% → 7.6% within 6 weeks.

3. Real-Time Risk & Fraud Detection

  • Developed a Graph Neural Network (GNN) model to detect anomalous patterns in transactions.
  • Enabled credit score prediction in under 200ms using ensemble models (XGBoost + LSTM for temporal signals).
  • Integrated with a real-time alerting system and rule engine for human compliance checks.

4. Regulatory and Compliance

  • Implemented end-to-end data encryption at rest and in transit.
  • Enabled AI explainability modules to ensure compliance with AI governance guidelines.
  • Built an internal audit trail dashboard for all data flows and decision logs.

Results & Impact

KPI Before After Implementation Improvement
Recommendation CTR 2.1% 7.6% +262%
Loan Default Rate 8.3% 5.1% -39%
Fraud Detection Precision 67% 91% +36%
Infra Cost per Transaction $0.06 $0.027 -55%
System Downtime ~14 hrs/month <1 hr/month -93%

Tools & Tech Stack Used

  • Cloud: AWS (EKS, Lambda, S3, RDS, SageMaker)
  • AI/ML: TensorFlow, PyTorch, Hugging Face Transformers, Neo4j, XGBoost
  • Orchestration: Kubernetes, Argo Workflows
  • Monitoring: Prometheus + Grafana, ELK Stack
  • Compliance: HashiCorp Vault, OpenPolicyAgent, ISO/IEC 27001 controls

Key Differentiators Brought by Tech4Biz

  • Integrated custom AI frameworks for hybrid modeling (NLP + tabular + graph-based).
  • Delivered an end-to-end solution from data ingestion to model deployment in production.
  • Designed the platform to support future blockchain integration for smart contracts.