AI-Powered Demand Forecasting Retail Dashboard
Project category
UrbanLeaf Market
Retail
Canada
3.5 Months
Unlock insights and drive smarter decisions with Power BI CFO dashboards.
Project Overview
A leading U.S. financial institution faced slow, error-prone loan approvals. We built an AI credit scoring and risk analytics model integrated via APIs into origination, automating data ingestion (bureau, bank transactions, income), feature engineering, and explainable decisions with reason codes.Â
Turnaround time improved by 40% with consistent, auditable outcomes and drift monitoring. A companion power bi financial dashboard functioned as a real-time command center: application funnel and SLA tracking, auto-decision versus manual-review rates, score distributions, segment drill-downs, and early-delinquency views.Â
Role-based access and breach alerts aligned credit, operations, and compliance—enabling faster, more accurate approvals with strong governance and transparent oversight.
Main KPIs Tracked
The Credit Scoring & Risk Analytics model focused on the following key metrics:
- Loan Approval Turnaround Time
- Credit Risk Score Accuracy
- Loan Default Rate
- Customer Creditworthiness
- Approval vs. Denial Rate
- Loan Repayment Behavior
- Risk Assessment Efficiency
- Fraud Detection Rate
Solution Offered — AI-Driven Credit Scoring & Risk Analytics Development
Our team implemented a structured approach to provide a comprehensive solution for the financial institution:
Data Integration & Preprocessing
- Integrated historical loan data, customer financial history, credit reports, and external data sources (e.g., credit bureaus) into a centralized data warehouse.
- Cleaned and normalized data to ensure consistency and quality for model training.
Machine Learning Model Development
- Built machine learning models to predict credit risk based on multiple parameters such as credit history, income, debt-to-income ratio, and other financial indicators.
- Implemented various algorithms (e.g., Random Forest, Gradient Boosting, Neural Networks) to optimize model accuracy.
Real-Time Credit Scoring Engine
- Developed an AI-powered engine that calculates credit scores in real-time, enabling faster loan approvals without compromising on risk assessment quality.
- Automated the loan approval process by integrating the AI model into the institution’s core systems.
Risk Analytics & Decision Automation
- Integrated risk analytics tools to analyze loan application risk in real-time, providing decision-makers with actionable insights.
- Automated decision-making workflows based on predefined risk thresholds and scoring criteria.
Continuous Model Monitoring & Optimization
- Implemented automated model retraining pipelines to continuously update and optimize the model based on new data and changing market conditions.
- Provided real-time dashboards for monitoring credit risk metrics and loan approval efficiency.Â
Main Features of the Dashboard
Designed to empower decision-makers with real-time insights, the dashboard combines advanced analytics and intuitive visualization to simplify demand planning. It provides a unified view of performance metrics, helping teams act faster and smarter across the retail network.
Business Benefits of Credit Scoring & Risk Analytics
The implementation of the AI-powered credit scoring and risk analytics solution brought several significant benefits to the financial institution:
01
Improved Loan Approval Turnaround
Reduced approval time by 40%, enabling faster decision-making and improved customer experience.
02
Enhanced Credit Risk Accuracy
Increased accuracy of credit risk assessments, leading to more reliable loan decisions and reduced default rates.
Efficient Risk Management
Enabled more accurate risk profiling and early identification of high-risk borrowers, enhancing the institution’s ability to mitigate potential losses.
04
Optimized Operational Efficiency
Automated key steps in the loan approval process, reducing manual workload and improving overall operational efficiency.
05
Reduced Fraud Exposure
Leveraged AI-powered fraud detection to identify and mitigate fraudulent loan applications, reducing risk for the institution.
All outcomes are tracked in a cfo dashboard for real-time visibility, SLA monitoring, and board-ready reporting.
Who Gains Actionable Insights from This System?
Tech Stack Used
A robust, scalable tech architecture powered the AI-driven credit scoring and risk analytics solution:
Python (with Scikit-learn, TensorFlow, Keras)
SQL & Data Warehousing
Azure Machine Learning
Power BI
Results Achieved
The implementation of the AI-driven credit scoring and risk analytics model resulted in measurable improvements in both operational efficiency and loan decision-making accuracy
Key Results:
By automating scoring and strengthening risk analytics, the institution accelerated approvals while reducing risk. To sustain these outcomes, we added role-based dashboards and proactive monitoring through our power bi consulting service, ensuring governance, KPI tracking, and transparent reporting for credit, risk, and compliance teams.