January – June 2026 · A scroll-led walkthrough of the funnel, credit decisioning, customer
lifestyle, repayment performance, tech impact and the moves that turn H1 into H2 results.
01 Executive Summary
02 User & Customer Funnel
03 Credit Decisioning & Risk/LTV
04 Customer Profiles & Lifestyle
05 Repayment Performance
06 Tech Impact & H2 Strategy
07 Recommendations
+ Appendix: Data Gaps
Sign-ups
5,122
Applications
2,613
Approval rate
8.73%
Repayment rate
18.12%
SeedFi (The DataSeed Company) · Prepared for internal distribution only Scroll to begin
↓
01 / Executive Summary
H1 2026 at a glance.
What we set out to do, what happened, and what it means for H2.
What was intended
The primary focus for
H1 was to begin diversifying the business beyond lending — strengthening lending infrastructure, expanding into
remittance, improving customer repayment mechanisms, and laying the regulatory and operational foundation for
future growth.
Key H1 wins
Diversification journey initiated
New financial products beyond
traditional lending began development.
Remittance product development
Built and soft-launched remittance,
a new revenue stream and ecosystem expansion.
SeedScore expansion
Extended to merchant use cases,
broadening utility beyond internal lending.
Loan Engine launch
Scalable infrastructure improving
efficiency, innovation speed and decisioning consistency.
Direct
debit improvements
Enhanced payment reliability and
reduced repayment friction.
Watch areas / misses
Remittance not yet public
Soft-launched only — general
availability still pending.
Diversification revenue still early
Most initiatives haven't reached
full commercial potential.
SeedScore
needs partnerships
Additional partnerships and
integrations required to maximize adoption.
Top insight for H2 → Bank linkage predicts repayment. Retention is the cheapest growth lever. Affordability
should be automated from lifestyle data.
The headline numbers
Six months of lending, told end to end.
5,013
people signed up. 69 disbursements made it all the way through. Everything in between is what this report walks
through.
Sign-ups
0
Activation
0
Approval rate
0
Repayment rate
0
02 / Acquisition & Sign-Ups
Where users come from. Where they drop.
5,013 sign-ups in H1, overwhelmingly organic, overwhelmingly mobile, overwhelmingly from Lagos.
Total sign-ups
0
H1 2026 cumulative
Activation rate
0
submitted a loan application
Top sign-up month
April
1,214 users onboarded
Days to first application
0
avg from sign-up
Onboarding platform
Mobile App
4,060
Web
242
Mobile app accounts for 79.26% of
onboarding (Android 91%). Web is browser-based and a minor channel. App downloads Jan–Jun: Android 4,022, iOS
2,486. Note: 820 NULL device values; 3,906 mobile records have no OS info.
Sign-up channel mix
Organic
97%
Executive channel
3%
4,975 sign-ups came organically — app
stores, word of mouth, search. 147 came through founder/co-founder referrals: manual, high approval rate.
Geographic spread
Lagos
33%
Ogun
9%
Abuja
9%
Oyo
8%
Delta
4%
36 others
36.5%
The remaining 32 states account for 36.48%
combined — Rivers, Osun and Edo are the next three emerging markets worth watching.
02 / The Onboarding Journey
Where users progress, and where they don't.
Every stage is a filter. Watch where the funnel actually breaks.
Sign-Up
5,122
Profile Completed
3,920
Selfie Upload
3,801
BVN Verified
1,829
NIN Verified
1,730
Account Activated
1,501
Loan Application
500
Bank Statement Upload
493
Affordability
77
Disbursement
70
Biggest drop → BVN verification. 1,974 users — 52% of everyone who uploaded a selfie — never completed it.
The single largest leak in the funnel.
Account deletion analysis
0
of all onboarded users — H1
88%
11%
1%
Never applied — 88%
Post-rejection — 11%
Post-repayment — 1%
Top 3 deletion reasons
Inability to use services
43%
Loan / eligibility issue
19%
Poor customer service
8%
User tier distribution
Tier 0 — Not eligible 3,705 users
72.34%
Tier 1 — Limited eligibility 1,416
users
27.65%
Tier 2 — Fully loan-eligible 1
user
0.02%
3,629 users sit in Tier 0, 1,383 in
Tier 1, and just 2 users in Tier 2 — full eligibility is exceptionally rare in the current pool.
03 / Credit Decisioning & Risk/LTV
Who applied. Who got approved. What it's worth.
2,545 applications, 8.73% approved. Here's how SeedScore band maps to who actually got through —
and who actually repaid.
Applications received
0
H1 total
Approval rate
0
approved / received
Avg loan amount
₦0
approved loans
Avg tenor
0
months, approved
SeedScore band → approval rate
750–800 Exceptional
0%
690–749 Very Good
57%
620–689 Good
60%
530–619 Fair
32%
280–529 Poor
4%
Affordability
PASS
15%
FAIL
85%
Of loan applicants in H1.
Borrower retention & lifetime value
2nd loan take-up
9.09%
2+ loan take-ups
14.29%
Repeat rate
16%
H1 Gross Revenue per Borrower: ₦7,341—
conservative estimate, will grow as cohorts mature.
Key eligibility metrics
1
SeedScore
2
Debt-to-capital index
3
Gambling record
03 / SeedScore Deep Dive
Is our credit score getting smarter?
Score bands aren't linearly predictive of repayment — and that's the most important finding in
this section.
Avg SeedScore (Approved)
0
Avg SeedScore (Declined)
0
H1 vs prior period
-2.38
avg score change MoM
App completion rate
0
started vs fully submitted
Counter-intuitive finding → Good-band borrowers (620–689) repay better than Very Good (690–749): 91% vs
73%. Affordability signals may be a stronger driver than score band alone.
SeedScore → repayment correlation
690–749 Very Good
73%
620–689 Good
91%
530–619 Fair
80%
280–529 Poor
64%
% fully repaid or actively on track.
750–800 band has no record in H1.
SeedScore pillars & weights
Payment History
35%
Depth of Credit
25%
Open Credit
25%
Demographics
15%
Repayment behaviour and closed loan
records remain the strongest determinants of a borrower's score.
Score
distribution is consistent with H2 2025 — the Poor band still dominates at 85% of scored applicants. Higher
bands remain thin, signaling limited credit depth in the current borrower pool.
03 / Affordability & Loan Sizing
Can they afford it? Are we approving the right amounts?
15.23% of all applicants pass affordability — and larger loan requests clear the check more
easily than small ones.
Affordability pass rate
0
of all applicants
Affordability fail rate
0
failed the check
Avg requested
₦0
across all applicants
Top pass-rate month
March
40.63% pass rate
Avg loan amount by income band
< ₦50k/mo
₦106,240
₦51k–100k/mo
₦48,969
₦101k–200k/mo
₦145,977
₦201k–500k/mo
₦223,532
> ₦500k/mo
₦1,641,208
Approval rate by amount requested
<₦50k
4.09%
₦51k–₦100k
1.18%
₦101k–₦200k
90%
₦201k–₦500k
82.93%
>₦500k
99.19%
Larger loan requests approve at higher
rates — borrowers above ₦200k clear affordability checks more easily than low-ticket applicants.
Monthly affordability pass rate
13.4%
Jan
20.24%
Feb
39.39%
Mar
13.01%
Apr
18.33%
May
18.42%
Jun
Pass rate
Fail rate
March is a clear outlier at 39.39%
pass rate against a consistent 13–20% range across all other months. What drove March's spike warrants
investigation.
04 / Customer Profiles & Lifestyle
Who is our customer. How do they live.
213 linked bank statements, deduped to one per customer. Internet service is the closest thing
to a universal signal.
Top spend categories — % of borrowers
Internet Service
71.8%
Miscellaneous
25.8%
Food & Dining
24.9%
Supermarket
24.4%
Financial Services
23.5%
Savings & Investment
22.5%
Betting & Gaming
10.3%
Faith
9.4%
Aviation
4.7%
213 linked statements analyzed. Highest
typical spend is Aviation at ₦8,916/mo — rare (4.7%) but the single highest-ticket category. Transfers &
Cash excluded as a catch-all for money movement, not discretionary spend.
Top spendFood, TransportBetting spendLow /
NoneAvg tenor6.5 monthsAvg SeedScore572
Overdue (7–60+ days)
Top spendBetting, AirtimeBetting spendHigh /
ElevatedAvg tenor7.9 monthsAvg SeedScore516
Note: SeedScore averages include
borrowers approved through executive channels, which can bypass the standard 530 minimum threshold.
04 / Lifestyle Audit
How borrowers spend, and what it signals.
213 linked statements — legacy + Loan Engine, deduped to one per customer.
Statements analyzed
0
linked bank statements
Most universal category
Internet Service
71.8% of borrowers (n=153)
Highest typical spend
₦8,916
Aviation — rare (4.7%, n=10) but the single highest-ticket category
Spend by category — full linked-account population (n=213)
Category
% Borrowers
Typical/mo
n
Internet Service
71.8%
₦957
153
Miscellaneous
25.8%
₦5,000
55
Food and Dining
24.9%
₦1,165
53
Supermarket
24.4%
₦2,196
52
Financial Services
23.5%
₦5,742
50
Utilities
23.5%
₦5,848
50
Savings & Investment
22.5%
₦3,227
48
Subscription
17.4%
₦667
37
Healthcare
16.0%
₦1,548
34
Education
12.2%
₦2,798
26
Category
% Borrowers
Typical/mo
n
Betting and Gaming
10.3%
₦1,670
22
Wellness
9.9%
₦5,096
21
Faith
9.4%
₦1,391
20
Skin, Body & Beauty
9.4%
₦2,107
20
Road Transportation
8.9%
₦785
19
Fashion
8.5%
₦3,288
18
Party
6.6%
₦5,751
14
Gadgets
6.6%
₦2,024
14
Entertainment
5.6%
₦2,504
12
Aviation
4.7%
₦8,916
10
Transfers & Cash excluded —
catch-all for Opay / Kuda / PalmPay money movement, not discretionary spend.
Key finding → Internet service is the closest thing to a universal borrower signal (72% prevalence) — most
other categories are niche (under 25%).
05 / Repayment Performance
The money went out. Did it come back?
18.12% overall repayment rate across all H1 cohorts — but the breakdown by bucket tells the real
story.
Loan status breakdown
54%
26%
15%
5%
Performing — 54%
7-day overdue — 26%
30-day overdue — 15%
60-day+ overdue — 5%
Early / on-time
0
of total repayments
Auto-recovered (late→paid)
0
paid without chasing
60-day+ overdue
0
requires intervention
Who defaults? Who repays?
Common default profile
280–529 SeedScore
₦300k+ income · ₦500k+ loan amount
· 180+ day tenure
Most reliable bank
WEMA
Bank whose customers repaid best in
H1 (FCMB 100% but excluded — low sample n=2)
Repayment rate by loan tenor
30-day
82.95%
60-day
93.72%
90-day
39.11%
180-day
19.25%
Employment type vs repayment behaviour
Early
Employed 47%
Self-Emp 53%
On-Time
Employed 43%
Self-Emp 45%
Auto-Recovered
Employed 49%
Self-Emp 47%
Reach Out Needed
Employed 48%
Self-Emp 41%
60-Day+ Default
Employed 16%
Self-Emp 83%
The self-employed make up a
disproportionate 83% of 60-day+ defaults — the clearest behavioural split in the repayment data. FCMB achieved
100% repayment but is excluded from top rank due to low sample size (n=2).
05 / Bank Linkage Deep Dive
Does your bank account predict how you repay?
100% of H1 borrowers had retrievable bank statement data — powering everything below.
Most linked bank
GTBank
22% of borrowers
3+ banks linked repayment
0
Banks linked vs repayment rate
1 bank linked
16.9%
2 banks linked
14.6%
3+ banks linked
19%
Repayment rate by linked bank
FCMB
100%
WEMA
64%
First Bank
50%
Sterling Bank
32.8%
Access
30.55%
UBA
28%
Others (14 banks)
22.34%
Insight → More banks linked = better repayment. 3+ bank linkers repay at
19% vs 16.9% for single-bank linkers. FCMB and Wema linkers are the most reliable at 100% and 64% respectively —
Opay and fintech-only linkers carry meaningfully higher default risk.
05 / Staff Loan Analysis
How much of our repayment rate is staff-driven?
Staff loans are a small slice of the book by value — and don't meaningfully inflate H1's
headline repayment rate.
Staff loans as % of total
0
of H1 approved loans
Staff repayment rate
0
salary-deducted cohort
Non-staff repayment rate
0
external borrowers
Repayment rate difference
+3.33
pts, staff vs non-staff
Staff vs non-staff portfolio split
No. of loans
Staff: 12
Non-staff: 201
Avg loan amount
₦925,833
₦4,494,852
Avg SeedScore
594
554
Repayment impact analysis
Blended (staff + non-staff)
18.12%
Excluding staff
18.08%
Difference: +0.04 pts without
staff
Key finding → Excluding staff from the repayment cohort increases the overall rate by just 0.04 pts.
Despite a 3.33pt individual gap, staff loans aren't materially inflating H1 performance — they're only ~1.2% of
total loan value.
05 / Competitive Positioning
How does SeedFi stack up against the market?
Before we plan H2, here's where SeedFi sits today — so we know exactly what we're closing the
gap on.
Approval rate
8.73%
SeedFi
55%
Industry avg
70%
Best-in-class
Avg turnaround time
<3hrs
SeedFi
18hrs
Industry avg
2hrs
Best-in-class
Lower is better — SeedFi already
leads the industry here.
Collection efficiency
79%
SeedFi
82%
Industry avg
93%
Best-in-class
App store rating
3.5
SeedFi
4.4
FairMoney/Renmoney
4.6
OPay/Moniepoint
Advertised APR range
SeedFi
42–70% APR
Source: SeedFi official FAQ /
website, 2026
Other major lenders
30–260% APR
FairMoney, Carbon — via TechCabal
lender comparison, Nov 2025
Positioning gaps
App rating is our clearest gap — UX and support need investment
Collection efficiency trails industry average by ~3 points
Approval rate trails industry average by 46 points — but tighter underwriting is a deliberate choice, not
just a gap
Market context → Moniepoint and OPay compete on raw transaction scale. FairMoney has paired aggressive
underwriting (loans up to ₦3M, minutes to decision) with a deposit-funded model. Renmoney plays a slower,
document-heavy game for higher-ticket salaried borrowers (up to ₦6M, 24-month tenors). SeedFi's approval rate sits
well below these peers — but that's also why the risk profile looks different, and H2's job is to selectively
expand access using the Loan Engine and SeedScore 2.0.
06 / Tech Impact & H2 Strategy
Loan Engine is the anchor everything else builds on.
Four new initiatives launch in H2: Investment, SeedFi V2, SeedScore 2.0, and a revolving Credit
Line.
New initiatives for H2
Investment — SeedFi Investment Product
Enable users
to build wealth through investment opportunities directly within the SeedFi ecosystem.
Expected outcomes
Increased customer retention
Diversified revenue streams
Higher customer lifetime value
Platform — SeedFi V2
Deliver a
significantly improved customer experience with enhanced performance, usability and scalability.
Focus areas
Improved onboarding
Better loan management experience
Enhanced repayment flows
Improved customer self-service
Optimization — SeedScore 2.0
Credit scoring
optimization across the board.
Focus areas
Launch external bureau-only scoring for partners
Build behavioural scoring from bank statement data
Deploy new scoring weights, run shadow validation before migration
Upgrade ML capabilities
Credit — Credit Line Product
Launch a
revolving credit facility giving customers ongoing access to credit instead of one-time loans.
Expected outcomes
Increased customer engagement
Higher repeat borrowing rates
Improved revenue per customer
What moved the needle in H1
Impact: High
Loan Engine
Scalable infrastructure, faster
launches, more consistent decisioning.
Impact: Medium-High
SeedScore Expansion
Strengthened proprietary edge,
opened merchant/third-party integrations.
Increase disbursement volume, improve
repayment, optimize underwriting via SeedScore.
02 — Drive adoption
Scale remittance, increase SeedScore
use across internal and external channels.
03 — Strengthen efficiency
Automate lending and collections,
enhance monitoring and compliance.
H2 success metrics
✓
Growth in active customers & loan disbursement volume
✓
Improved repayment and collection rates
✓
SeedScore adoption across internal and external channels
✓
Successful launch of SeedFi V2
✓
Launch and adoption of the Credit Line product
H2 North Star → H2 is about depth and breadth together — strengthening
the core lending engine while launching Investment, V2, and Credit Line in parallel.
07 / Recommendations
Six moves that turn findings into H2 results.
Five of six point at the same root pattern: retain, re-engage, and extract more value from the
users already inside the system.
01
Quick win
Win back high-intent lapsed users
11% of account deletions happen right after a loan decline, 1% after full repayment — both groups already
proved real intent.
→ Re-apply flow for post-rejection users; fast-tracked pre-approved offer for repayers.
02
Quick win
Build a structured repeat-loan program
Only 16% of first-time borrowers return for a 2nd loan, just 4.89% reach one at all.
→ Trigger pre-approved, discounted 2nd-loan offers immediately on repayment completion.
03
High priority
Fix the BVN verification bottleneck
1,935 users — 39% of selfie-uploaders — drop at BVN verification, the single biggest leak in the funnel.
→ Streamline or automate verification to cut friction at this exact step.
04
Medium-high
Formalize bank-linkage into underwriting
3+ bank linkers repay at 19% vs. 16.9% for single-bank linkers.
→ Fold into SeedScore 2.0's behavioural-scoring workstream.
05
High priority
Define internal metrics for impact reviews
After Loan Engine launched, most internal teams couldn't quantify how it changed their workflows.
→ Track Loan Engine performance metrics from H2 onward.
06
Medium
Launch cross-sell pathways
The active borrower base is already-verified and engaged, but has no structured path to adjacent products.
→ Build cross-sell triggers starting with Credit Line and Investment.
+ / Appendix
Data gaps & H2 tracking backlog.
What we couldn't measure this cycle — and what to start tracking from H2.
Account Deletion
No lifecycle stage tag — can't split post-rejection vs post-repayment deletions.
→ Tag deletion events with lifecycle stage from H2
Staff Applicant Skew
No flag on staff applications — may be distorting approval rate and SeedScore averages.
→ Add staff flag to application table; exclude from metrics
SeedScore Predictiveness
No formal correlation run between SeedScore at approval and eventual repayment outcome.
