Credit Risk Assessment Platform
Pranit Patil
AccuNode — Credit Risk Assessment Platform
A production-grade, multi-tenant SaaS platform for ML-powered company default risk prediction.
Overview
AccuNode is a multi-tenant SaaS platform that uses machine learning to predict company default risk from financial ratios. It supports both real-time individual company analysis and large-scale batch processing via CSV/Excel uploads, alongside analytics dashboards for portfolio-level insights.
Built for financial institutions, lenders, and risk teams who need fast, explainable credit risk assessments at scale.
The Problem
Credit risk workflows in most organizations are slow, inconsistent, and hard to scale:
Spreadsheet-heavy and largely manual
Not designed to handle hundreds of companies at once
Lack ML-powered scoring or customizable models
No multi-tenant access control for teams and organizations
This leads to slow decision-making, inconsistent risk evaluation, and poor visibility across portfolios and sectors.
Target Users
Credit & Risk Analysts — need fast, reliable default scoring
Lending & Fintech Teams — require scalable batch processing
Investment & Research Teams — want portfolio-level insights
Platform Admins — manage tenants, organizations, users, and models
How the Platform Works
1. Individual Company Analysis
Users input financial ratios to receive real-time default probability scores and risk
categories, powered by ML models trained on annual or quarterly data.
2. Bulk Portfolio Analysis
Users upload CSV or Excel files. The system processes jobs asynchronously, runs batch
ML inference, and provides downloadable results with live job status tracking.
3. Analytics Dashboard
Aggregated insights across all analyzed companies:
Default rate by sector
Risk category distribution
Default rate vs. market cap
High-risk companies and top performers
4. Role-Based Access Control
Multi-level roles —
super_admin, tenant_admin, org_admin, org_member, and user— control access to data and operations across tenants and organizations.Architecture & Core Components
AccuNode uses a multi-tenant SaaS backend with async processing for ML workloads and background jobs. Every request is scoped by tenant and organization, with full data isolation across APIs, workers, and analytics — no cross-tenant leakage by design.
API Service (FastAPI) — Async REST APIs handling auth, tenant/org management, predictions, analytics, and job orchestration.
ML Inference Layer — Annual and quarterly default prediction models served via dedicated backend pipelines.
Background Workers — Handle bulk CSV/Excel uploads, feature extraction, and batch inference jobs asynchronously.
Cache Layer — Redis manages job state, caching, and performance optimization across requests.
Relational Data Store — PostgreSQL stores tenants, organizations, users, jobs, predictions, and analytics data.
Tech Stack
Frontend
Framework: Next.js 15 (App Router)
UI Library: React 19 + TypeScript
Styling: Tailwind CSS + Radix UI
State Management: Zustand + TanStack Query
Deployment: Vercel
Backend
API Framework: FastAPI (Python, async)
Database: PostgreSQL + SQLAlchemy ORM
Auth & Access: Role-based access control
Containerization: Docker & Docker Compose
Machine Learning
Libraries: scikit-learn, LightGBM, pandas, numpy
Annual Model: Logistic Regression
Quarterly Model: Ensemble (LightGBM + Logistic Regression)
Infrastructure & DevOps
Compute: AWS ECS Fargate (API + workers)
Database: AWS RDS (PostgreSQL)
Cache: AWS ElastiCache (Redis)
Load Balancing: AWS ALB
CI/CD: GitHub Actions → ECR → ECS
My Role — End-to-End Ownership
Built entirely as a solo engineer, with ownership across the full stack:
Designed the full system architecture and multi-tenant data model
Built the complete FastAPI backend — auth, RBAC, tenants, orgs, users, jobs, predictions, and analytics
Implemented async batch processing pipelines for CSV/Excel uploads
Integrated ML inference workflows into production APIs
Designed tenant-scoped data isolation across APIs, workers, and analytics
Built frontend dashboards, forms, analytics views, and admin panels
Implemented role-based access control across frontend and backend
Set up Dockerized local dev environment and AWS production deployment
Configured the full CI/CD pipeline (GitHub Actions → AWS ECS)
Outcome
AccuNode shipped as a production-ready multi-tenant ML platform with the following capabilities:
Real-time and batch default risk predictions
Scalable async job processing for large portfolios
Secure role-based access and strict tenant isolation
Actionable analytics dashboards for financial decision-making
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Posted Feb 19, 2026
AccuNode is a production-grade, multi-tenant SaaS platform that uses machine learning to predict company default risk from financial ratios
