Internal Platform · Risk & Credit Intelligence

Know every customer's
creditworthiness
before you decide.

HitchPay Credit turns your transaction data, bureau records, and open banking signals into a real-time credit score for every Nigerian customer — built into the tools your team already uses.

credit.hitchpay.ng / dashboard
H
Credit
📊Overview
👥Customers
⚠️Risk flags
📋Compliance
⚙️Model config
📈Analytics

Customer credit scores

Live · Updated 4 min ago
Scored customers
18,420
↑ 12% this week
Average score
661
Band C · Fair
High-risk flags
247
Needs review
CustomerScoreBand
AO
Adaeze Okafor
782
Excellent
View →
EI
Emeka Ibe
724
Good
View →
FK
Fatima Kwami
658
Fair
View →
BU
Bola Usman
541
Poor
View →
300–850
Score range
5
Scoring pillars
96%
Customer coverage
< 3s
Compute time
30 days
Auto-refresh cycle
The problem

HitchPay is flying
blind on credit risk.

Every day, HitchPay serves thousands of customers with no view of their creditworthiness. Revenue opportunities are missed. Risk goes undetected. Decisions are gut-feel.

🚫

No credit product capability

BNPL, overdrafts, and loan referrals are off the table without a scoring layer — a major revenue gap.

👁️

Risk is invisible

High-risk customers blend in with good ones. No fraud signals, no early warning, no segmentation.

💸

Transaction data sits unused

Months of rich behavioural data — income patterns, savings rates, spending discipline — generating zero credit insight.

🤝

No lending partner leverage

MFBs and fintech lenders won't refer business without creditworthiness data. No score, no partnership.

Platform overview

Everything your team needs,
in one internal platform.

🔄

Four-layer scoring engine

Identity, bureau, transaction data, and alternative signals combine into a single accurate credit score for every customer.

📊

Internal risk dashboard

Customer profiles, portfolio overview, risk flag queue, model configuration — all in one place for your operations team.

🛡️

Built-in compliance

CBN, NDPC, NDPR, and FCCPC requirements handled from day one. Consent logs, audit trails, and data deletion workflows included.

Ready to score your
customers?

Request access to HitchPay Credit — built for the risk, operations, compliance, and product teams inside HitchPay.

How it works

Four layers.
One accurate score.

HitchPay Credit pulls from four distinct data sources, each adding a layer of precision to the final score. Every step is consent-gated, encrypted, and auditable.

Step 01
🪪

Identity layer

BVN and NIN verification anchors every customer to a confirmed government identity. Cross-referenced to detect mismatches that may indicate fraud.

Step 02
🏛️

Bureau layer

Credit history from CRC and FirstCentral — Nigeria's two CBN-licensed bureaus — retrieved via BVN. Covers loans, repayment records, delinquencies, and loan stacking.

Step 03
💳

Transaction layer

HitchPay's own platform data: income regularity, savings rate, balance consistency, spending discipline, and recurring payment success. The most powerful signal we hold.

Step 04
🌐

Alternative signals

Open banking data via Mono Connect for linked bank accounts, bank statement parsing for manual uploads, and digital footprint data for credit-invisible customers.

The scoring pipeline,
step by step.

Customer joins

Identity verification triggered

On sign-up, BVN and NIN lookups are queued via Mono Lookup API. Consent is collected before any external call is made. Identity status is stored immediately.

Mono BVN lookupConsent frameworkIdentity stored
24–48 hours

Bureau data pulled

Once identity is confirmed, credit history is retrieved from CRC and FirstCentral. Loan stacking is detected if a customer has 3+ active facilities across both bureaus.

CRC BureauFirstCentralLoan stacking check
Weekly

Transaction features extracted

12 scoring signals are computed from HitchPay's transaction database: average monthly inflow, income regularity, savings rate, balance volatility, recurring debit success rate, and more.

12 featuresHitchPay DBWeekly refresh
On-demand / event

Score computed

All pillar data is normalised, weighted, and mapped to a 300–850 score. A confidence level and top 5 contributing factors are generated alongside the score. Score is stored and the risk dashboard is updated.

300–850 outputConfidence scoreTop 5 factors< 3s compute
30 days

Score automatically refreshed

Bureau data and open banking data refresh every 30 days. Transaction features refresh weekly. Significant events — like a large balance drop — trigger an immediate recompute.

30-day bureau refreshEvent-triggered recompute
Scoring model

Five weighted pillars.
Transparent by design.

Every score is built from five clearly defined data pillars with published weights. Internal teams can see exactly what's driving each customer's score — no black boxes, no guesswork.

Pillar weights

What goes into
every score.

Weights reflect the predictive power and data availability of each pillar. Configurable by product managers via the model configuration UI — no engineering deployment needed.

Transaction behaviour35%
Income regularitySavings rateBalance consistencyRecurring debits
Bureau credit history25%
Loan historyDelinquenciesActive facilities
Open banking / cashflow20%
Verified incomeNet cashflowDTI ratio
Platform loyalty12%
Account ageFeature breadth
Digital signals8%
Digital footprintDevice signals
AO
Adaeze Okafor
HP-20291 · Lagos
BVN verified
742out of 850
Band B · Good
Transaction behaviour
84
Bureau history
67
Open banking
74
Platform loyalty
92
Digital signals
54
Score bands

Five bands.
Clear actions for each.

Every customer lands in one of five bands. Each band maps to a defined internal action — from maximum credit access to monitoring mode.

A
750 – 850
Excellent
Maximum BNPL limits. Instant pre-approval for lending partner referrals.
B
700 – 749
Good
Standard BNPL eligibility. Eligible for partner lending referrals at normal rates.
C
620 – 699
Fair
Limited BNPL. Loan referrals require manual review by a risk analyst.
D
500 – 619
Poor
No credit products. Monitoring mode. Score improvement nudges triggered.
E
300 – 499
Insufficient
Data collection prompt. Restrict high-value transactions pending full score.
Platform features

Everything your team
needs to act on credit.

Six core modules for five internal personas — risk analysts, operations managers, compliance officers, product managers, and engineers.

Core modules

Six modules.
One internal tool.

📊

Customer credit profiles

Full credit picture per customer — score, 12-month history trend, identity status, bureau data, open banking summary, risk flags, and a complete audit log. Loads in under 2 seconds.

Score trendRisk flagsAudit logManual refresh
🏢

Portfolio overview dashboard

Score distribution across all customers, bureau coverage rate, risk concentration heatmap, and week-on-week trends. Cached with a 6-hour TTL — fast and always available.

Distribution chartCoverage rateWoW trends
⚙️

Model configuration UI

Adjust pillar weights, edit score band thresholds, and trigger a full portfolio recompute — no engineering deployment needed. Every config change is versioned and rollback-ready.

No-code configVersion historyRollbackRecompute trigger
🔍

Risk flag queue

Automatically surfaced alerts for loan stacking, identity mismatches, balance volatility spikes, and unusual transaction velocity. Prioritised by severity, assignable to analysts.

Auto-detectionSeverity tiersAssignable
📋

Compliance audit module

Full consent log, data lineage records, and third-party API call history. Two-tab interface: Consent Log and API Call Log. Exportable CSV for CBN or NDPC regulatory submissions.

Consent logAPI call logCSV exportImmutable records
🔒

Data deletion workflows

NDPR-compliant right-to-erasure processing. One workflow removes credit data across all tables, revokes Mono Connect tokens, and records completion in the deletion audit log.

NDPR compliantToken revocation48h SLA
Who uses it

Built for five
internal personas.

👤

Risk Analyst

Primary user

Reviews customer credit profiles, investigates risk flags, approves manual loan referrals, and adds internal notes. Lives in the customer list and flag queue.

📊

Operations Manager

Dashboard user

Monitors portfolio health via the overview dashboard, manages escalations, sets credit policy, and exports filtered customer lists for offline review.

⚙️

Product Manager

Model owner

Owns the scoring model weights. Configures pillar weights and band thresholds, triggers recomputes, monitors score distributions, and runs A/B experiments.

📋

Compliance Officer

Audit & governance

Manages the compliance audit module. Processes data deletion requests, exports regulatory reports, tracks consent records, and manages the NDPC data register.

🔧

Backend Engineer

Integration owner

Manages API integrations, monitors pipeline health, handles webhooks and scoring events, and maintains the data ingestion crons.

🏛️

Leadership / Board

Read-only overview

Views portfolio metrics, credit product eligibility rates, risk trend summaries, and bureau coverage growth — without operational access.

Compliance

Built for
Nigerian regulation.

HitchPay Credit was designed around CBN, NDPC, NDPR, and FCCPC requirements from day one — not retrofitted after launch. Every data touchpoint is documented, auditable, and consent-gated.

Regulatory frameworks

Six regulations.
All covered.

📋

CBN Credit Reporting Guidelines

Explicit consent required before every bureau query. Timestamped, immutable, and auditable at any time via the compliance module.

🔒

Nigeria Data Protection Act 2023

BVN and identity data encrypted at rest (AES-256). Lawful basis documented. Right-to-erasure workflows built in. Data retention schedules enforced automatically.

🏛️

NDPC Registration

HitchPay registered as a data controller with the Nigeria Data Protection Commission. Annual data protection audit. NDPR compliance tracked internally.

⚖️

FCCPC Consumer Protection

Score explainability surfaced for every score — top 5 contributing factors visible to analysts. Internal dispute resolution workflow for score challenges.

🛡️

AML / CFT Monitoring

Transaction velocity signals are baked into the scoring model. High-risk flags auto-route to a compliance review queue for human assessment.

📁

Data retention & deletion

Score history retained 7 years. Raw bureau responses auto-purged after 2 years. Customer deletion requests completed within 48 hours, with full audit confirmation.

Security architecture

Enterprise-grade
security by default.

Data protection

  • AES-256 encryption for all BVN, NIN, and identity data at rest
  • TLS 1.2+ for all API calls to third-party providers
  • No PII written to application logs at any layer
  • Bureau raw responses stored encrypted, auto-purged after 2 years
  • Role-based access control — least privilege across all internal roles

Audit & compliance ops

  • Immutable append-only logs for all consent events and API calls
  • Full data lineage — every score traceable to its source data
  • CSV export of consent and API logs for regulatory submission
  • Automated deletion cascade across all tables on erasure request
  • Score history maintained for 7 years per regulatory retention rules
Pre-launch checklist

Before going live.
Seven must-haves.

1

Sign CRC and FirstCentral data subscriber agreements

Required before bureau API calls can go to production. Coordinate with legal and compliance. Both bureaus require CBN-approved data subscriber status.

2

Register as NDPC data controller

HitchPay must be registered with the Nigeria Data Protection Commission before processing personal credit data at scale. File before platform launch.

3

Confirm CBN money lending or MFB licence status

If HitchPay will extend credit directly, a CBN licence is required. Without it, the platform is informational only — scoring and referrals are permissible, direct lending is not.

4

Implement consent framework before first API call

No bureau or identity API call can be made without a valid consent record. The consent service must be live before any integration goes to production.

5

Complete privacy policy update

Privacy policy must disclose all data sources used in scoring: bureau data, open banking, alternative signals, and digital footprint. Legal review required.

6

Enable AML/CFT monitoring pipeline

Any credit disbursement triggers CBN AML obligations. The transaction velocity monitoring must be active and connected to the compliance review queue before go-live.

7

Test data deletion workflow end-to-end

Confirm that a deletion request removes data from all tables, revokes third-party tokens, and generates a completion audit record. NDPR mandates 48-hour completion.

Ready to get started?

Contact the HitchPay product team to request access to the Credit Intelligence platform.