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Case Study · Digital Lending · BNPL

EasyBuy VIP Loan —
AI That Scales Digital Lending
for Mass Markets

AdmireTech designed and deployed an AI-powered BNPL and device financing platform — enabling instant credit decisions, automated collections, and fraud prevention for millions of underserved Nigerians.

Nigeria · Mass-Market BNPL
Device Finance · Digital Lending
12-Month Engagement
Live · Scaling Nationally
5M+
Potential customers reached
<60s
Credit decision time
40%
Default reduction
3 Phases
12-month rollout

The Challenge

Scaling device finance without drowning in defaults

A fast-growing Nigerian BNPL provider offering smartphone and device financing to low-income consumers was hitting a ceiling. Manual credit checks delayed sales, rudimentary scoring missed both good borrowers and bad actors, and collections relied on costly call-centre operations with no intelligent prioritisation.

Pain Points

  • Manual credit checks causing 48-hour delays and lost sales at point of purchase
  • High default rates on device loans due to rudimentary scoring that ignored behavioural signals
  • No real-time fraud detection — synthetic identities and repeat defaulters slipped through
  • Collections dependent on call-centre agents with no prioritisation or automation
  • Retail partners churning due to slow approvals and poor post-sale customer experience

What We Built

Five AI capabilities powering end-to-end lending

01

AI Credit Scoring Engine

Real-time ML model ingesting telco data, device metadata, BVN history, and repayment behaviour to approve or decline in under 60 seconds — even for first-time borrowers with no formal credit history.

02

Automated Loan Origination

End-to-end digital workflow from retailer tablet to disbursement. KYC verification, e-signature, and device-lock activation happen in a single session — zero paper, zero branch visits.

03

Real-Time Fraud Detection

Behavioural biometrics, device fingerprinting, and graph-based anomaly detection flag synthetic IDs, collusion rings, and repeat defaulters before funds are released.

04

Smart Collections & Recovery

ML-prioritised outreach via SMS, WhatsApp, and USSD with dynamic payment plans. Remote device-lock escalation for non-responsive accounts. Recovery rates up 35%.

05

Portfolio Analytics Dashboard

Live dashboards tracking PAR, vintage curves, channel performance, and cohort analysis. Auto-alerts for risk threshold breaches give management real-time visibility.

Outcomes

Measurable impact at scale

Speed
<60s

Credit decisions at point of sale — from 48 hours to under one minute

Defaults
−40%

Default rate reduced through behavioural scoring and early-warning models

Fraud
95%

Synthetic identity fraud caught before disbursement with ML detection

Recovery
+35%

Collections recovery improved through smart prioritisation and automation

Growth Engine

AI-powered marketing that compounds growth

Beyond lending operations, AdmireTech built an integrated marketing intelligence layer that turns customer data into acquisition and retention fuel.

Predictive Lead Scoring

ML models identify high-conversion prospects from telco and retail data, focusing marketing spend where it matters.

Automated Campaign Engine

Personalised SMS and WhatsApp campaigns triggered by repayment milestones, upgrade eligibility, and seasonal demand.

Referral & Loyalty AI

Dynamic referral incentives and loyalty tiers driven by customer lifetime value predictions, boosting organic growth.

Implementation

12-month phased roadmap

1
Foundation
Months 1–4

Core scoring engine, loan origination, and device-lock integration deployed with pilot retail partners.

2
Scale & Optimise
Months 5–8

Fraud detection, smart collections, and portfolio dashboards live. Model retraining on production data.

3
Growth Engine
Months 9–12

Marketing AI, referral system, and national retail rollout. Continuous A/B testing on scoring and campaigns.

Compliance & Responsible AI

Built for trust at every layer

  • Data QualityAutomated validation pipelines flag incomplete BVN, telco, or device records before they enter scoring models.
  • Fairness & InclusionRegular bias audits ensure scoring doesn’t discriminate by geography, gender, or device type. Human override always available.
  • Data PrivacyNDPA-compliant data handling with end-to-end encryption, purpose limitation, and transparent consent flows.
  • Responsible CollectionsContact frequency caps, hardship detection, and escalation protocols prevent aggressive recovery practices.

Ready to scale your lending platform with AI?

AdmireTech builds intelligent, responsible lending infrastructure that grows with your business — from pilot to millions of customers.