FinLend
An AI credit-risk simulation system that combines a neural-network risk model with fuzzy logic to recommend credit decisions, limits, and interest.
Overview
FinLend is an academic AI credit-intelligence project built with a Laravel interface and a FastAPI AI engine. It turns 25 structured applicant signals into a credit-risk recommendation using a feedforward neural network and fuzzy logic decision layer.
Problem
Credit evaluation needs structured applicant intake, explainable risk signals, and decision support that can connect model output to a readable recommendation.
Target Users
Credit analysts, fintech builders, and academic AIML evaluators
My Role
AI and full-stack developer
Built the Laravel web interface, connected it to a FastAPI AI engine, designed the 25-feature intake flow, and shaped the ML plus fuzzy decision pipeline into a polished credit simulation experience.
Key Features
- Five-step credit application flow
- 25-feature applicant and loan intake
- FastAPI AI engine integration
- Feedforward neural-network risk scoring
- Fuzzy logic for credit limit and interest recommendation
- Laravel result interface for approval or rejection output
AI System Notes
Python FastAPI AI engine using MLPClassifier plus a fuzzy logic decision layer.
Laravel handles the web flow and sends a structured request to FastAPI; the Python service returns risk and fuzzy decision outputs to the result page.
Inputs are constrained to the 25 model features, then scaled and processed before recommendation output.
The web flow depends on the FastAPI service being available on port 8000; connection issues are surfaced as a demo/runtime dependency rather than silently faking a result.
Academic simulation using loan-default data and USD-scale assumptions. It is not a production credit approval system and would need fairness, calibration, and compliance validation.
Pipeline
- User enters 25 applicant, financial, loan, and collateral signals in the Laravel form.
- Laravel sends the structured payload to the FastAPI /hitung-kredit endpoint.
- The neural-network model estimates default risk from the scaled feature set.
- Fuzzy logic translates risk and financial context into limit and interest recommendations.
- Laravel renders the decision output as an approval or rejection simulation with readable context.
Challenges & Trade-offs
- Keeping the model input structure aligned between Laravel and FastAPI
- Presenting model output as a readable credit recommendation rather than a black-box score
- Handling academic dataset constraints while keeping the demo believable
Results
Delivered an end-to-end credit simulation flow where applicant, finance, loan, and collateral data are processed into risk, approval, limit, and interest recommendations.
Proof Artifacts
