You will get a credit risk model audit
Project details
You will get a senior-level credit risk model audit focused on business impact, model quality, explainability, and governance readiness. I will review your scorecard, ML model, features, validation results, segmentation, stability, and monitoring approach, then provide clear recommendations that risk, product, and leadership teams can act on.
Machine Learning Tools
pandas, Python, Python Scikit-Learn, scikit-learn, SQL, XGBoostWhat's included
| Service Tiers |
Starter
$950
|
Standard
$2,500
|
Advanced
$5,000
|
|---|---|---|---|
| Delivery Time | 3 days | 7 days | 14 days |
Number of Revisions | 0 | 0 | 0 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 3 | 6 | 10 |
Number of Graphs/Charts | 3 | 6 | 10 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | - | ||
Source Code | - | - |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$10 - $30
Additional Revision
+$50
Additional Graph/Chart
+$50Frequently asked questions
About Anil
Enterprise AI & GenAI Architect | Banking, Credit Risk, AI Agents
Indore, India - 10:05 pm local time
I am an AI & Data Science leader with 16 years of experience across banking, fintech, credit risk, fraud, collections, customer analytics, and enterprise AI systems. My work has delivered $10M+ in documented business impact across credit acquisition, default prediction, fraud detection, churn reduction, and GenAI-powered voice-agent automation.
My strongest niche is the intersection of banking domain expertise and modern AI engineering:
- Credit risk scorecards and underwriting models
- Fraud detection and recall optimization
- Collections analytics and debt recovery AI
- GenAI / LLM applications for enterprise workflows
- AI voice agents using STT, TTS, and dialogue orchestration
- Model governance, explainability, and MRM-ready documentation
- Production ML pipelines, dashboards, and executive decision systems
Recent examples of my work include:
- Designed and deployed an AI voice agent for loan recovery across Southeast Asia using LLM dialogue management, speech-to-text, text-to-speech, and ML risk segmentation. The system improved promise-to-pay conversion and delivered $4M impact in the first quarter.
- Led development of a credit card acquisition scorecard for a major Indian bank, improving customer acquisition by 4% while maintaining risk profile, with INR 800M business impact.
- Built credit card default prediction and settlement models that added INR 500M annual collections value.
- Developed fraud detection models for a leading US bank, reducing false negatives and saving $3M+ in fraud losses.
- Founded and scaled AI Centers of Excellence, shaping multiple enterprise AI MVPs and production deployments.
I bring both executive-level AI strategy and hands-on technical execution. I have built and led large data science teams, worked with 50+ professional teams, and partnered with senior stakeholders such as CROs, CDOs, CTOs, and product heads.
Typical projects I can help with:
- Build or review credit scoring, fraud, churn, or collections models
- Design GenAI copilots or AI agents for banking and enterprise workflows
- Create production-ready Python ML pipelines
- Develop explainable AI and model governance documentation
- Audit existing models for performance, stability, segmentation, and business impact
- Build interactive dashboards and data visualization assets for leadership
- Convert notebook prototypes into maintainable, deployable systems
- Advise leadership on AI transformation roadmap and use-case prioritization
Technical stack: Python, SQL, XGBoost, LightGBM, Random Forest, SHAP, WOE/IV, NLP, GenAI, LangChain, FastAPI, Tableau, Spark, Hadoop, Hive, Dataiku, DataRobot, AutoML, LLM applications, voice AI workflows, and production ML systems.
Education: MS Data Analytics, Carnegie Mellon University; BTech, IIT Roorkee.
If you need someone who can combine deep banking context, senior AI leadership, and practical production implementation, I can help you move from AI idea to measurable business outcome.
Steps for completing your project
After purchasing the project, send requirements so Anil can start the project.
Delivery time starts when Anil receives requirements from you.
Anil works on your project following the steps below.
Revisions may occur after the delivery date.
Review inputs and objectives
I review your data, model outputs, business goals, current decision process, and any governance or validation requirements.
Audit model performance and risk logic
segmentation, stability, explainability, feature quality, cutoffs, monitoring gaps, and business alignment.