About the MSME Data Portal

Understanding Kenya’s MSME Credit Landscape Through Data

The MSME Data Portal is an initiative of the Kenya Bankers Association (KBA) designed to provide clear, accessible insights into Kenya’s Micro, Small, and Medium Enterprises (MSMEs). Drawing on data from the Metropol Credit Reference Bureau, the platform offers a unique view of MSME financing patterns, credit performance, and participation across the country.

A key feature of the portal is its gender-disaggregated data, which highlights how women-owned and men-owned businesses access and use financial services. Through interactive dashboards and visual analytics, users can explore trends in credit, loan performance, and other indicators, supporting policymakers, financial institutions, and development partners to identify gaps, design targeted interventions, and promote inclusive growth.

By making high-quality MSME data openly available, the Kenya Bankers Association aims to strengthen evidence-based decision-making and contribute to a more resilient, equitable, and inclusive financial sector in Kenya.

Methodology at a glance

A compact map of how the dashboard turns CRB records into consistent MSME and gender insights.

1) How we classify MSMEs

Turnover where available; loan size as proxy otherwise. Remove large/corporate exposures.

2) How we classify gender

Female-Owned / Male-Owned / Jointly Owned / Unknown, using 51% ownership rule for businesses.

3) How we classify age

Age is derived from year of birth relative to each reporting month, then grouped into fixed adult age bands.

4) How we compute gender gap

Male share minus Female share (excludes Joint & Unknown), shown in percentage points.

5) How we track NPL metrics

Full portfolio in scope. NPL if 90+ days in arrears or Substandard/Doubtful/Loss.

Methodology

1. MSME Classification (Micro, Small, Medium)

Kenyan law defines MSMEs based on annual turnover. However, turnover data is not consistent. To ensure consistency and accuracy, the dashboard applies a structured classification process that uses turnover where available and loan size as a proxy where it is not.

1.1 MSME Thresholds

Based on the Micro and Small Enterprises Act (2012) and national MSME surveys, the turnover bands are:

Micro

Less than KES 500,000

Small

KES 500,000 to 5 million

Medium

KES 5 million to 100 million

Large / Corporate

Above KES 100 million

Large Enterprises /Corporates exposures are removed using conservative criteria:

Loans with annual turnover above KES 100 million, or
Loans issued with a value above KES 100 million

2. Gender Classification

All dashboards classify borrowers using four gender categories:

Female-Owned
Male-Owned
Jointly Owned
Unknown

Gender is assigned as follows:

Individual borrowers: The loan is attributed to the registered borrower’s gender.
Businesses: A business is classified as:
- Female-Owned if women own at least 51%
- Male-Owned if men own at least 51%
- Jointly Owned if neither reaches 51%
- Unknown if ownership or gender data is unavailable

Female Owned

Male Owned

Jointly Owned

Unknown

This classification is applied consistently across all gender-disaggregated dashboards.

3. Age Classification and Youth Lens

The dashboard also supports an age lens built from each borrower's year of birth. Age is not treated as a fixed current-age attribute. Instead, it is recalculated against the reporting period of each loan record so that historical views remain reproducible over time.

3.1 Age calculation approach

The source data provides a borrower year of birth.
The dashboard calculates age as: reporting year minus year of birth.
The reporting period is the record's own reporting month, which means a loan reported in an earlier year is bucketed using that earlier reporting year, not today's date.
This preserves historical consistency, so the same record stays in the same age context when analysing a past month.

3.2 Adult age bands used in the dashboard

Once age is calculated, records are grouped into fixed adult bands used consistently across charts, tooltips, exports, and story insights:

18-24

25-34

35-44

45-54

55-64

65+

Unknown

3.3 Interpretation of the youth lens

For youth-focused storytelling, the dashboard combines the 18-24 and 25-34 bands into a single youth segment. All older known age bands are grouped as the rest of the portfolio.

4. Gender Gap Indicator

The dashboard highlights the gender gap, shown as Male minus Female.

For each selected metric (such as loan value, number of loans, or number of borrowers):

Only Male-Owned and Female-Owned loans are included
Joint and Unknown categories are excluded
Shares are calculated for male-owned and female-owned portfolios
The gender gap is the difference between these two shares and is presented in percentage points

Male

−

Female

Gap

Percentage points

5. Non-Performing Loan (NPL) Metrics

NPL indicators on the Loan Performance pages reflect the full loan portfolio in scope, not only MSME-specific products.

A loan is classified as non-performing if:

It is 90 days or more in arrears, or
It falls under prudential risk categories: Substandard, Doubtful, or Loss

Loan performance is grouped into five categories: Normal, Watch, Substandard, Doubtful, and Loss. These classifications form the basis of all NPL ratios and trends shown in the dashboard.

Prudential Risk Classification (NPL Status)

Normal: 0–30 days past due

Watch: More than 30 up to 90 days past due

Substandard: More than 90 up to 180 days past due

Doubtful: More than 180 up to 360 days past due

Loss: More than 360 days past due

Definition of Key Terms

Loan StatusExpand

These terms describe the current state of a loan or credit facility:

Closed – No more admin processes running such as instalment demands or interest charges to account, and no further facilities can be offered on this account.
Dormant - no activity for 2 years. This applies for Overdraft/Current Accounts.
Write-Off – For facilities that don’t form part of the outstanding portfolio in the Balance Sheet but are still outstanding in the books of accounts.
Legal -with legal officer in court
Collection- with collection bureau
Active - For facilities that form part of the outstanding portfolio and are reported in the Balance Sheet.
Facility Rescheduled – For Rescheduled/Restructured Facilities
Settled – The facility has been cleared. This status can only be used for revolving facilities
Called Up - The facility has been called up. Once the client has paid up, the status should be updated to Closed, Settled, or otherwise Client Deceased.
Suspended – The facility has been put on hold for an indefinite period
Client Deceased
Deferred – This refers to facilities whose payments have been put on hold for a definite period or in moratorium (Grace Period)
Not Updated – This status is reserved for CRBs (if last record status is not CLOSED)
Disputed – Refers to a Record that the Client has disputed at the CRB. The account cannot be updated until the dispute is resolved

Prudential Risk Classification (NPL Status)Expand

Loan performance is assessed using standard prudential risk categories based on days past due:

Normal: 0–30 days past due
Watch: More than 30 up to 90 days past due
Substandard: More than 90 up to 180 days past due
Doubtful: More than 180 up to 360 days past due
Loss: More than 360 days past due

Loans classified as Substandard, Doubtful, or Loss are treated as Non-Performing Loans (NPLs) in the dashboard.

Client TypeExpand

Borrowers are grouped into two main categories:

Legal Clients: Fully registered businesses under the Business Registration Service (BRS).
Individuals: Individuals who have accessed loans for business purposes.

Product TypeExpand

Business lending in the dashboard is grouped into three main product categories:

Business Expansion Loans
Trade Finance
Business Working Capital

Our Partners

As of Dec 2025 • Outstanding value

The current selection focuses on MSMEs and selected product types.

The total outstanding value across all reporting financial institutions as of Dec 2025 is KES 596.8B. This page focuses on how Outstanding value is distributed across gender categories.

When comparing male-owned and female-owned businesses only, male-owned businesses hold 75% of the outstanding value, while female-owned businesses hold 25%. For every KES 1K going to men-led businesses, women-led businesses receive about KES 341.

Note: Jointly-Owned and Unknown gender categories are shown in the charts but excluded from the comparison above for clarity.

Gender gap

49.09 PP (MEN > WOMEN)

Absolute difference in outstanding value share (percentage points).

By Product Type

Male-Owned

Female-Owned

Jointly-Owned

Unknown

Category	Male-Owned	Female-Owned	Jointly-Owned	Unknown	Total
Business Working Capital	52.3%	19.4%	9.2%	19.1%	100.0%
Trade Finance Facility	50.6%	13.1%	7.6%	28.7%	100.0%
Business Expansion Loan	48.5%	17.7%	9.3%	24.5%	100.0%

Distribution by Gender

Female-Owned

Male-Owned

Jointly-Owned

Unknown

Shows the distribution of the selected metric across gender categories (Female-Owned, Male-Owned, Jointly-Owned, Unknown).

By Institution Type

Male-Owned

Female-Owned

Jointly-Owned

Unknown

Category	Male-Owned	Female-Owned	Jointly-Owned	Unknown	Total
Trade	67.3%	23.1%	4.4%	5.2%	100.0%
Digital Credit Provider	84.0%	11.8%	0.9%	3.3%	100.0%
Non Deposit Taking Saccos	49.1%	45.0%	NaN%	5.8%	100.0%
Deposit Taking Saccos	57.1%	33.2%	1.8%	7.8%	100.0%
MFB	54.2%	29.4%	4.1%	12.3%	100.0%
Banks	49.1%	16.1%	9.7%	25.1%	100.0%
DFI	38.4%	14.6%	11.7%	35.3%	100.0%

Each bar sums to 100%. Segments show the gender composition within each institution type.

By Sector

Male-Owned

Female-Owned

Jointly-Owned

Unknown

Category	Male-Owned	Female-Owned	Jointly-Owned	Unknown	Total
Agriculture	43.4%	9.3%	19.3%	28.0%	100.0%
Energy/Water	42.6%	7.4%	18.6%	31.4%	100.0%
Finance	40.4%	17.5%	14.9%	27.3%	100.0%
Government	55.4%	8.2%	13.6%	22.7%	100.0%
Mining/Quarrying	42.6%	3.5%	12.8%	41.1%	100.0%
Other	70.1%	4.9%	18.8%	6.2%	100.0%
Tourism/ Restaurant/Hotels	38.9%	3.0%	14.5%	43.6%	100.0%
Trade	41.4%	9.2%	9.9%	39.6%	100.0%
Transport/ Communications	33.6%	9.7%	20.6%	36.1%	100.0%
Building/Construction	43.9%	10.6%	15.8%	29.7%	100.0%

Each bar sums to 100%. Segments show the gender composition within each sector.

By Client Type

Male-Owned

Female-Owned

Jointly-Owned

Unknown

Category	Male-Owned	Female-Owned	Jointly-Owned	Unknown	Total
Individual	67.5%	30.7%	NaN%	1.8%	100.0%
Legal Entity	42.3%	10.6%	13.3%	33.9%	100.0%

By Collateral Status

Male-Owned

Female-Owned

Jointly-Owned

Unknown

Category	Male-Owned	Female-Owned	Jointly-Owned	Unknown	Total
Collateralised	48.4%	22.8%	9.7%	19.1%	100.0%
Uncollateralised	51.6%	15.4%	8.5%	24.5%	100.0%

By Loan Status

Male-Owned

Female-Owned

Jointly-Owned

Unknown

Category	Male-Owned	Female-Owned	Jointly-Owned	Unknown	Total
Active	50.1%	16.5%	9.1%	24.3%	100.0%
Called Up	88.8%	1.0%	9.6%	0.6%	100.0%
Client Deceased	75.9%	24.1%	NaN%	NaN%	100.0%
Collection	56.1%	17.4%	8.1%	18.3%	100.0%
Deferred	27.0%	73.0%	NaN%	NaN%	100.0%
Dormant	64.0%	21.9%	2.7%	11.4%	100.0%
Facility Rescheduled	42.9%	22.9%	13.8%	20.5%	100.0%
Not Updated	52.4%	47.6%	NaN%	NaN%	100.0%

Trend Analysis

Female-Owned

Male-Owned

Jointly-Owned

Unknown

Period	Female-Owned	Male-Owned	Jointly-Owned	Unknown
2025-07	17.1%	51.4%	8.5%	22.9%
2025-08	17.8%	49.9%	9.2%	23.2%
2025-09	17.5%	48.9%	9.4%	24.1%
2025-10	17.4%	50.7%	9.0%	22.9%
2025-11	17.3%	50.7%	8.8%	23.2%
2025-12	17.3%	50.8%	8.8%	23.0%

Time trend of gender shares. Recent view shows Jul 2025+; use Historical to view 2020–2024.

Gender Gap Trend (Female − Male)

Shows how the gender gap changes over time, computed as Female share − Male share (Male + Female only). We display the absolute size in percentage points (pp) and label whether Women > Men or Men > Women.

Counties with Lowest Gender Gap

Counties:

▾

Ranks counties by the smallest absolute gender gap (Female share − Male share). Direction is shown next to the county label.

County gap data is not available for the current filter scope.

County Map — Gender Gap vs Overall (percentage points)

For each county, this shows the county-level gender gap (Female share − Male share) and compares it to the overall gap for the current filter scope. Positive deltas mean the county is better for women than the overall (a larger female advantage / smaller male advantage).

Map data is not available for the current filter scope.

Gender Focus Analysis for MSMEsin Kenya

Methodology at a glance

Methodology

1. MSME Classification (Micro, Small, Medium)

1.1 MSME Thresholds

2. Gender Classification

3. Age Classification and Youth Lens

3.1 Age calculation approach

3.2 Adult age bands used in the dashboard

3.3 Interpretation of the youth lens

4. Gender Gap Indicator

5. Non-Performing Loan (NPL) Metrics

Prudential Risk Classification (NPL Status)

Definition of Key Terms

Our Partners

By Product Type

Distribution by Gender

By Institution Type

By Sector

By Client Type

By Collateral Status

By Loan Status

Trend Analysis

Gender Gap Trend (Female − Male)

Counties with Lowest Gender Gap

County Map — Gender Gap vs Overall (percentage points)