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 struggled with slow and inconsistent loan approvals. To fix this, we built an AI-powered credit scoring and risk analytics model that automatically collects customer financial data (bank transactions, income history, credit bureau reports), processes it, and gives clear, explainable loan decisions with reason codes.
With this system, loan approval time improved by 40%, quality decisions became more consistent, and less drift in the model was continuously monitored.
We also created a Power BI financial dashboard that acted as a live command center — showing application flow, SLA performance, auto vs manual decision rates, early risk alerts, and borrower trends.
Role-based access and real-time alerts helped credit, operations, and compliance teams work together with better transparency and control.
Main KPIs Tracked
The Credit Scoring & Risk Analytics model monitored several important performance metrics:
- Loan Approval Turnaround Time - How quickly a loan application moves from submission to final decision.
- Credit Risk Score Accuracy - How correctly the model predicts whether a customer is low-risk or high-risk.
- Loan Default Rate - The percentage of customers who fail to repay their loans.
- Customer Creditworthiness - How financially reliable a customer is based on income, history, and repayment ability.
- Approval vs. Denial Rate - The ratio of how many loan applications get approved versus rejected.
- Loan Repayment Behavior - How consistently customers pay back their loan EMIs on time.
- Risk Assessment Efficiency - How fast and accurately the system identifies the risk level of each loan application.
- Fraud Detection Rate - How effectively the system catches fake or suspicious loan applications.
Solution Offered — AI-Driven Credit Scoring & Risk Analytics Development
Data Integration & Preprocessing
- Combined historical loan files, customer financial details, credit bureau reports, repayment behavior data, and external datasets into one secure data warehouse.
- Cleaned and standardized the data so the AI model could learn accurately and analyze default patterns, repayment trends, and fraud signals.
Machine Learning Model Development
- Built ML models to predict credit risk, customer creditworthiness, probability of default, and repayment behavior using factors like payment history, income, spending behavior, and debt levels.
- Tested multiple algorithms (Random Forest, Gradient Boosting, Neural Networks) to improve accuracy and strengthen prediction of approval/denial outcomes.
Real-Time Credit Scoring Engine
- Created a real-time scoring engine that calculates a customer’s credit score in seconds and evaluates approval vs denial likelihood instantly.
- Integrated the engine into the loan origination system, reducing turnaround time and improving risk assessment efficiency for every incoming application.
Risk Analytics & Decision Automation
- Added tools that evaluate application risk instantly, detect suspicious or fraudulent patterns, and highlight customers with higher default probability.
- Automated decisions based on risk levels so low-risk customers get instant approvals while high-risk or abnormal cases are flagged for manual review.
Continuous Model Monitoring & Optimization
- Built automated pipelines to retrain the AI model regularly with new loan performance data, repayment behavior trends, and updated customer patterns.
- Provided dashboards to track approval vs denial trends, fraud detection rates, default risk shifts, and overall score performance for proactive decision-making.
Main Features of the Dashboard
The dashboard gives teams a simple and clear view of the entire loan process:
Business Benefits of Credit Scoring & Risk Analytics
This AI-powered solution delivered several powerful advantages:
01
Faster Loan Approvals
Approval time dropped by 40%, improving customer satisfaction and reducing processing delays.
02
Better Risk Assessment
More accurate scoring led to safer lending decisions and fewer defaults.
03
Stronger Risk Management
Early identification of high-risk applicants helped protect the organization from losses.
04
Higher Operational Efficiency
Automation reduced manual work and made the entire loan process smoother.
05
Lower Fraud Risk
AI-based pattern detection helped catch fraudulent applications early.
All these improvements are monitored through a CFO dashboard for real-time visibility, SLA tracking, and board-level reporting.
Who Gains Actionable Insights from This System?
Tech Stack Used
A powerful and scalable tech stack supported the AI-driven solution:
Python (with Scikit-learn, TensorFlow, Keras)
SQL & Data Warehousing
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 credit scoring and strengthening risk analytics, the institution was able to approve loans faster, reduce risk, and improve overall governance.
To maintain these results, role-based dashboards and proactive monitoring were implemented through our Power BI consulting service, ensuring full transparency for credit, risk, and compliance teams.