You will get Credit Scoring Model with Scorecard and Automated Risk Decisions
Project details
I build credit scoring models that automate lending decisions with regulatory-grade precision. Not a black-box ML model — an interpretable scorecard that your compliance team can explain to auditors.
My approach follows banking industry standards:
• WoE/IV feature engineering — identifies which variables actually predict default
• Point-based scorecard (300-850) — just like FICO, intuitive for your team
• Multi-model comparison — I test LogReg, RF, and XGBoost, pick the winner
• Stability monitoring — PSI tracking ensures your model doesn't degrade silently
Results from my latest project: AUC 74.9%, KS 35.8%, top 3 deciles capture 61.1% of defaults, PSI < 0.02 (very stable).
I've built scoring models for banks, fintechs, and lending platforms processing thousands of applications. Every model comes with full regulatory documentation — no surprises during audit season.
Deliverables: Trained model + point scorecard + validation report + source code + monitoring setup. Your team can retrain on new data without me.
Ideal for: Banks, fintechs, BNPL companies, lending platforms, microfinance, insurance (claims scoring).
My approach follows banking industry standards:
• WoE/IV feature engineering — identifies which variables actually predict default
• Point-based scorecard (300-850) — just like FICO, intuitive for your team
• Multi-model comparison — I test LogReg, RF, and XGBoost, pick the winner
• Stability monitoring — PSI tracking ensures your model doesn't degrade silently
Results from my latest project: AUC 74.9%, KS 35.8%, top 3 deciles capture 61.1% of defaults, PSI < 0.02 (very stable).
I've built scoring models for banks, fintechs, and lending platforms processing thousands of applications. Every model comes with full regulatory documentation — no surprises during audit season.
Deliverables: Trained model + point scorecard + validation report + source code + monitoring setup. Your team can retrain on new data without me.
Ideal for: Banks, fintechs, BNPL companies, lending platforms, microfinance, insurance (claims scoring).
Machine Learning Tools
Microsoft Power BI, MLflow, NumPy, pandas, Python, Python Scikit-Learn, scikit-learn, SciPy, SQL, XGBoostWhat's included
| Service Tiers |
Starter
$600
|
Standard
$1,500
|
Advanced
$3,000
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 21 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 3 | 3 |
Number of Scenarios | 1 | 3 | 5 |
Number of Graphs/Charts | 3 | 8 | 15 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | - | |
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$100 - $400
Additional Revision
+$100
Additional Model Variation
(+ 2 Days)
+$150
Additional Scenario
(+ 3 Days)
+$200
Additional Graph/Chart
(+ 3 Days)
+$300
Model Documentation
(+ 2 Days)
+$200
Data Source Connectivity
(+ 4 Days)
+$400
Reject Inference
(+ 3 Days)
+$300Frequently asked questions
About Michel
AI & Machine Learning | Data Analytics & Engineering
Dores de Campos, Brazil - 4:33 pm local time
Steps for completing your project
After purchasing the project, send requirements so Michel can start the project.
Delivery time starts when Michel receives requirements from you.
Michel works on your project following the steps below.
Revisions may occur after the delivery date.
Data Analysis & Feature Engineering (Days 1-5)
I perform WoE/IV analysis on every variable, identify the most predictive features, check monotonicity, handle missing values, and create the feature set that will power the scorecard.
Model Development & Validation (Days 6-10)
I train and compare multiple algorithms, select the best performer, generate the point scorecard (300-850), define risk tiers, and produce the full validation suite (AUC, KS, Gini, PSI, decile analysis).