D
Dufaa
A Bank Al Bilad Company
Banking Intelligence Platform — CEO Briefing

Turn Existing Loans
Into New Revenue

Dufaa's AI-powered intelligence system identifies Bank Al Bilad customers who are ready for a Loan Top-Up — before they even ask for one.

Two intelligent engines work in sequence: first scanning the entire personal loan portfolio to find the right candidates, then predicting exactly when each customer will need a top-up, how much, and at what profit rate.

The Business Opportunity

The Problem We Solve

Bank Al Bilad has thousands of personal loan customers. Most of them will eventually need more financing — but without intelligence, we wait for them to come to us.

Reactive, Not Proactive

Today, customers approach the bank when they need money. By then, they may have already gone to a competitor. Dufaa flips this — we reach out first.

Wait for customer to apply
Predict need 3–12 months early

One-Size Pricing

All customers are offered the same profit rate, leaving revenue on the table for low-risk customers and taking on unpriced risk for high-risk ones.

Flat profit rate for everyone
10%–18% dynamic pricing per risk

Hidden Portfolio Value

The existing loan portfolio contains millions in untapped top-up potential that is invisible without data intelligence.

Portfolio value unknown
SAR 14.3M identified & quantified
About Dufaa

Dufaa is Dufaa's
Loan Top-Up Engine

Dufaa is a Non-Bank Financial Institution (NBFI) wholly owned by Bank Al Bilad. It operates with a single, highly focused product: the Loan Top-Up.

Rather than acquiring new customers from scratch, Dufaa leverages Bank Al Bilad's existing personal loan portfolio as its customer base — identifying customers who already have a loan and are strong candidates for additional financing.

This creates a zero-acquisition-cost model where every customer is already known, verified, and has an established repayment history.

Product
Loan Top-Up
Customer Source
Bank Al Bilad Personal Loan Portfolio
Pricing Model
Dynamic Risk-Based (10% – 18%)
Compliance
Shariah-Compliant Profit Rate Structure
Ownership
100% Bank Al Bilad
The Intelligence System

Two Engines. One Pipeline.

The system runs sequentially — Engine 1 identifies who, Engine 2 predicts when, how much, and at what price.

Engine 1

Candidate Identification

Engine 1 scans the entire Bank Al Bilad personal loan portfolio and applies a multi-factor scoring model to identify which customers are strong candidates for a Dufaa Loan Top-Up. It evaluates credit score, Debt-to-Burden Ratio (DBR), employment stability, payment history, and salary transfer status.

Input
Full BAB loan portfolio
Scoring Factors
6 weighted criteria
Output
Qualified candidate list
Eligible Amount
SAR per customer
Engine 1 Output → Engine 2 Input
Qualified candidates passed to Engine 2
Engine 2

Top-Up Predictor & Dynamic Pricing

Engine 2 takes the qualified candidates from Engine 1 and performs deep predictive analysis. It forecasts when each customer will need a top-up, how much they will need, and then applies a dynamic Shariah-compliant profit rate based on their individual risk profile — ranging from 10% for the safest customers to 18% for higher-risk profiles.

Input
Engine 1 candidates
Prediction
Amount + timing
Pricing Range
10% – 18% profit rate
Output
Actionable proposals
Business Impact

What the Intelligence Finds

From a portfolio of 100 Bank Al Bilad personal loan customers, the system identifies:

0
Customers Scanned
0
Qualified Candidates
0.3M SAR
Total Eligible Amount
0%
Average Profit Rate

Dynamic Profit Rate Tiers

Each customer receives a personalised profit rate based on their risk profile — maximising revenue while remaining competitive

Very Low Risk
10% – 11%
Excellent credit, government employee, zero missed payments
e.g. SAR 180,000 top-up
Low Risk
11% – 12%
Good credit, stable employment, clean payment record
e.g. SAR 150,000 top-up
Medium Risk
12% – 14%
Average credit, private sector, minor payment delays
e.g. SAR 100,000 top-up
High Risk
14% – 16%
Below-average credit, high DBR, some missed payments
e.g. SAR 70,000 top-up
Very High Risk
16% – 18%
Poor credit, elevated DBR, multiple payment issues
e.g. SAR 50,000 top-up

The End-to-End Process

From raw portfolio data to actionable customer proposals in seconds

01
Portfolio Ingestion
Engine 1 connects to Bank Al Bilad's personal loan portfolio and loads all active loan customers with their full financial profile — salary, outstanding balance, credit score, DBR, and payment history.
02
Multi-Factor Qualification Scoring
Each customer is scored across 6 criteria: credit score, DBR ratio, employment type, payment history, salary transfer status, and remaining loan tenure. Customers scoring above the threshold are marked as Qualified candidates.
03
Candidate List Output (Engine 1 → Engine 2)
Engine 1 produces a ranked list of qualified candidates with their eligible top-up amounts. This list is automatically passed as the input to Engine 2 — no manual intervention required.
04
Predictive Need Analysis
Engine 2 analyses each candidate's behavioral signals — spending patterns, salary growth, remaining loan balance trajectory — to predict when they will need a top-up and how much they are likely to request.
05
Dynamic Profit Rate Pricing
Each candidate receives a personalised Shariah-compliant profit rate between 10% and 18% based on their risk score. Lower risk = lower rate (competitive offer), higher risk = higher rate (appropriate risk premium).
06
Actionable Proposals for Relationship Managers
The final output is a prioritised list of customer proposals — each with a recommended action, predicted need date, top-up amount, profit rate, and assigned Relationship Manager for outreach.

Ready to See It in Action?

Launch the intelligence pipeline and watch Engine 1 scan the portfolio, then Engine 2 generate predictions and pricing — live.