It should be noted that the whole digital identity model on mobile phones and digital financial services models in Kenya is built from the ground up based on one SIM one account per one human user
The limits of this model are exposed once you move up to the app layer 1/
The limits of this model are exposed once you move up to the app layer 1/
On the ground people can be
1. Individuals yes
2. Or also part of a group with other humans
The app layer with current models is not flexible enough (at all) for group dynamics
Eg guarantees from members of a savings group 2/
1. Individuals yes
2. Or also part of a group with other humans
The app layer with current models is not flexible enough (at all) for group dynamics
Eg guarantees from members of a savings group 2/
In an ideal digital financial services world, digital lenders would score how much to lend to an individual based on
individual digital footprint + group guarantees/savings group footprints 👣 3/
individual digital footprint + group guarantees/savings group footprints 👣 3/
They would even lend to either
An individual
Or the group itself 4/
An individual
Or the group itself 4/
They would have a better picture of who they lend to
Are they in biashara?
Are they in boda boda biz?
Are they in a special biashara group ?
Group dynamics help refine the score
Right now, digital footprint is only part of true picture
5/
Are they in biashara?
Are they in boda boda biz?
Are they in a special biashara group ?
Group dynamics help refine the score
Right now, digital footprint is only part of true picture
5/
If you read this article, from an expert, you realize the limits of their model
Im more concerned that he, the author, is trapped in his bias
He cant see any other way out besides attempting to refine what we've established is sub par 6/
Im more concerned that he, the author, is trapped in his bias
He cant see any other way out besides attempting to refine what we've established is sub par 6/
How Can Providers Make Digital Credit More Profitable? | Graham Wright | LinkedIn 7/
This one too by CGAP cgap.org/blog/introduct… /8
Here is one of the problems identified by CGAP 9/ 
"Offering credit without a strong REMOTE IDENTIFICATION SYSTEM. When you can’t verify CUSTOMER IDENTITY, offering remote services is difficult, especially at scale."
Now see point /1
/10
Now see point /1
/10
In summary, i never pay much attention to digital lenders who inteoduce 0 new data on the table
Eg lender who tout the same digital data with an AI algorithm spin /11
🙄
Eg lender who tout the same digital data with an AI algorithm spin /11
🙄
Start ups like
Uber
And
Twiga
Have unearthed data that was hidden and why they are the new avenue for lenders /12
Uber
And
Twiga
Have unearthed data that was hidden and why they are the new avenue for lenders /12
Jumia Pay too
Who are lending to Jumia online merchants 13/
Who are lending to Jumia online merchants 13/
How do we unearth data found in Chamas and social savings group?
Data on the savings, credit, investment and insurance patterns of people within a group setting?
Where is it? 14/
Data on the savings, credit, investment and insurance patterns of people within a group setting?
Where is it? 14/
