CredOra
Predict.Protect.Prosper
The problem CredOra solves
Today, banks and NBFCs still rely on outdated, inconsistent, and manual methods to evaluate a borrower’s creditworthiness. Credit decisions often depend on fragmented data, incomplete bank statements, slow analysis pipelines, and subjective human judgment.
This results in:
❌ Delays in loan processing
❌ High chances of human error
❌ Inaccurate risk evaluations
❌ Hidden liabilities going unnoticed
❌ Financial losses due to incorrect credit decisions
For customers, this means unfair rejections, long waiting periods, and no transparency about why a loan was approved or denied.
For banks, this means higher NPA risk, slow workflows, and no unified way to analyze financial behaviour.
CredOra solves all of this.
CredOra is an AI-powered creditworthiness engine that instantly analyzes a customer’s financial health using bank statements and behavioural patterns. It transforms raw CSV data into meaningful, actionable insights in seconds.
With CredOra:
⚡ Banks get instant AI-generated risk scores, EMI stress predictions, and loan eligibility insights.
🧠 Hidden debts, risky transactions, and behavioural red flags are detected automatically.
🔍 Manual work reduces drastically, increasing speed and accuracy.
🏦 Bank admins can register institutions and onboard teams securely.
👤 Customers can understand their own financial health with transparency.
In short
CredOra makes credit evaluation faster, smarter, and safer — reducing risk for banks and giving users a fairer, more transparent financial experience.
Challenges we ran into
Building CredOra felt like creating a real fintech platform from scratch — and with that came several challenges across architecture, security, ML, and UI.
- Designing a Real Banking Workflow
Creating a system where:
banks register institutions,
bankers get onboarded separately,
users log in with strict access control,
was much harder than expected.
We solved it with a role-based JWT authentication system, separate routes, and protected pages in the frontend.
- Building Accurate Financial Risk Analysis from Raw Bank Statements
Extracting reliable insights from unstructured CSV data was challenging.
Bank statements are inconsistent, formats vary, and many fields needed normalization.
We fixed this by:
writing a transaction classifier,
building a preprocessing pipeline,
creating a rules + ML–based scoring model,
and validating outputs using sample datasets.
- Backend–Frontend Integration
Since this is a full-stack fintech application, syncing:
FastAPI backend
React frontend
protected routes
login flows
often created CORS issues, auth mismatches, and blank-screen errors.
We resolved this by:
isolating role-based APIs,
rewriting the routing structure,
fixing all import paths,
and improving error-handling and client-side validation.
- Time Constraints with Complex Fintech Features
A real credit-evaluation engine involves many moving parts:
CSV ingestion, ML scoring, dashboards, data visualization, report generation, and admin flows.
Balancing all of them within hackathon timelines required prioritization and rapid iteration.
We planned a minimal-but-real workflow first, then polished the UI and added animations.
- Ensuring a Clean, Deployment-Ready Codebase
Managing large folders, multiple teams, and rapidly changing prototypes caused structure issues.
We reorganized the entire project into a production-grade structure, removed unused components, and ensured everything was deployment-ready.
- UX/UI Complexity for a Real Fintech Dashboard
Creating a modern, professional banking dashboard with:
animations
clean workflow
data cards and charts
was challenging.
We overcame it by redesigning the components from scratch and using reusable UI blocks for consistency.
In the end…
Each challenge made the project better.
We turned a basic idea into a working, polished, real-world fintech product, and the process taught us how actual digital banking systems are designed, secured, and deployed.
Tracks Applied (1)
Best AWS Hack
Amazon Web Services
Technologies used