HOW TO FROMALIZE THE INFORMALS - banky-foibe.mg · Preamble • Increasing pace of technology...

How to “formalize

theinformals”:

Case Study: Timiza – Barclays Kenya Mobile Lending Proposition

Presenter: Chrispus Muhia: Head of Consumer Credit

Barclays Bank of Kenya

| Presentation to CCI Ivato Antananarivo MADAGASCAR26 November 2019

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Preamble

• Increasing pace of technology adoption for financial services calls for banks and other financial institutions to

adopt new delivery channels to reach key target markets.

• Africa’s population is characterised by low banking and credit penetration rates, with access tocredit being a key concern

• Mobile telephony has been well adopted and embraced by Africans, sidestepping key physical

infrastructural challenges like transport networks

• Banks can thus leverage m-banking solutions to reach to African populations to deliver access to


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• Banks can thus leverage m-banking solutions to reach to African populations to deliver access to

banking and credit.

• Use of alternative data sources is critical where most of the population do not have historical banking and

credit histories. Mobile Network Operator (MNO) data, both airtime and wallet, is critical in bridging these

gaps.

• Credit bureaus supplement these alternative data sources by enabling credit information to be shared regularly,

enabling customers to build credit profiles that enhance access to higher credit limits, and better risk based pricing decisions that benefit good customers with lower interest rates.

Drivers to Mobile Banking/Virtual Banking

Ease of access:• Mobile Banking offers easier accessibility to banking services anytime and anywhere - consumers are able to

access their account at a click of a button with or without need of internet connectivity (USSD access is critical)

User friendly interface:• Mobile banking offers a friendly and easier way to access one’s account.

Convenience:• Not time consuming


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• Mobile Banking offers instant banking services that initially could require hours queuing in the banking hall

• one can withdraw/save money in the account at the comfort of their home/office

• Cuts unnecessary human interface. Offers dignity and avoids humiliation

Secure way to access and use your money• One can pay bills (schools fees, utility bills, buy good and services) without physical withdrawal of money

• Offers minimal risk of theft –password protected

Background: The Kenyan market is characterised byhigh demand for digital loans with approximately 6.3million unique digital borrowers

48.5Millions

KenyanPopulation

23.5Millions

KenyanAdults

18.2MillionsMillions

Phone Owners

6.3Millions

Digital Borrowers 27% of

Kenyan Adults

35% of Phone

OwnersSource: Financial Sector Deepening Kenya, March 2018, The digital credit revolution in Kenya: An assessment of market demand, 5 years on.


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Competitivedigital financing operating environment


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Customer Value Proposition

Ease of Access & Convenience =

Transactional, simple and

secure way to manage your

finances

+

Quick and easy

loans

+ +

Efficient 24/7 Contact

Centre for assistance

Enable me to grow

=

Bank - Anywhere

anytime

#Mobile first

+

Fund your Timiza account & pay

loans from your Mpesa wallet

with ease

No credit record

required to borrow

++Access group loans and

group savings facilities

Access to in-store financing

to access goods on credit

Flexibility = + +

More than just aBank: Protection

and Value adds

+

Last expense Cover to your

next of Kin - Funeral Cover

No Minimum balance

requiredNo limit on transactions to

access funds in your account

Personalaccident Cover

Pay your bills conveniently

from your mobile phone.

Ability to book a tax ride on

Timiza App (Little cabs)

+

Open a second/additional

account on mobile with

ease

Personal Finance

Management

+


6

=

Key relationships during on-boarding

Customer dials short code /

downloadsTimiza App and enters

National ID

Retrieve data from MNO, IPRS andBureau with ID

Number

Provides KYC as per Government

Database using ID Number

Through a Data Sharing API

service, it verifies mobile number and ID Number match

Providescustomer credit history from

credit bureau

Matching Algorithm

KYC Lite matches ID number to to

KYCprocess• Automated, integrated customer on

boarding where customer enters only ID and eco system relies on Govt Population registry (IPRS – Integrated Population Registry System) as the golden source for

Customer Technology

Partner

MNO

Partner

Credit Reference

Bureau

ID number to to IPRS

Customerreceives SMS message–„Sorry, unable to

verify your details, please contactCall

Centre

Match es ?

Timiza Account is opened, OTPis generated

Credit Scoring and Limit Assignment initiated

Welcome to Timiza SMS Message

Credit approval process• Transition from rule based decisioning to

data driven decisioning supported by scorecards

• 2nd Generation application scorecard in place, with 3rd generation scorecard under development.

• 1st Gen Behavioural scorecard in place

• Collection process• Call centre based• On roll forward, no punitive / predatory

compounding interest rates or accumulated collectionfees

• No new loans extended before existing loan paid off

Registry System) as the golden source for KYC. Customer and Account record created with IPRS details

• MNO service enables verification ofmobile number ownership to the IDnumber

Customers with Limits can apply for loan


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Typical Scoring Process


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Activity Model is used to

assess customers with NO or

Immature CRB footprint

Performance Model is used

to assess customers with

mature CRB footprint. This

thus uses both CRB and MNO

data at assessment.

• Bad Rate was defined as 30 days past due

Bureau Information Critical in enhancing Credit Decisioning

ActivityModel PerformanceModel

y = -0.0038x + 2.922

R² = 0.2877

20.00%

30.00%

40.00%

50.00%

60.00%

Ba

dra

te

No Bureau Info

y = -0.0024x + 1.894

R² = 0.9254

25.00%

20.00%

15.00%

10.00%

30.00%

Ba

dra

te

Bureau Info supplementing MNO

Data

0.00%

10.00%

640 650 660 680 690 700670

Mean score

Rule Based ModelBad Rate33.34%

Score cut-off 695

Rejection Rate 92.51%

Expected Echo Bad Rate 20.37%

Gini 23.8%

5.00%

0.00%660 680 700 720 740 760

Mean score

Rule Based ModelBad Rate15.63%

Score cut-off 685

Rejection Rate 19.61%

Expected Echo Bad Rate 14.49%

Gini 41.8%


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5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

CRB data critical in managing default

0.0%A B C D E X (No CRB)

default rate. Additional CRB data helpsdistinguish performance of existing

borrowersCustomers are not allocated a credit limit

Customers with low probability of

defaults will be allocated limitsbased on their proven track record.

customers

Customers with no previous

borrowing history will be scored purely based on their

Full limits allocated to these Cut out CRB grades D&E based on high telco data.The default rates are high

hence full limits not allocated


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Portfolio Distribution Outcome


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Impact of Bureau Data in Credit Decisioning

Phase 1:

Post Launch

Testing launchassumptions

Phase 2:

Decision made to cut out Bureau

score Grades D and E, and to limit initial ticket size for

‘No Score’ customers

Phase 3:

Bureau score built into application

and Behavioural scorecards.


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Key Lessons Learnt

1.

Customer

2. Technology

Partnership

4.

Business Mix

Key

Lessons

Learnt• Availability of alternative datain

absence of traditional financial and credit data. MNO, Social

• Customers concerned with ease of access to finance

• Customers do not typically have previous financial or credithistories, (providingbanking and credit to the informals)

• Customers require basic financial and credit solutions to run their lives (personal and business)

• Fit for purpose scorecards, both application and behavioral (to assess customer profiles)

• Criticality of building and retainingrepeat customers to drive revenueand profitability

• Focus on vulnerable agegroups; educating and offering them payment plans

• Major difference in loan performance acrossdemographics

• Use of credit Bureau input incredit decisioning

• Lack of skill & resources on agile product development and DevOps

5.

Agility/Speed &

Marketing

Intensity

3.

Data

• Third party may provide hosted solution for scorecard and customer data if this is not available internally however, there needs to be constant monitoring of the scorecardand data changes

• Availability of in-house dataanalytics vital for unlockinggrowth

media• Leveraging Bureau data to

enhance creditdecisioning• Real time monitoring and

oversight on system performance key fordigital products

• Misalignment of culture between Banks and technology providers on strategicroadmap may posechallenges

• Scalability challenges with increased customer demand& transaction volume necessitating frequent optimization and system investments

• Response time of partnersa consistentchallenge

development and DevOps with technologyproviders may be a potential challenge

• Clearly articulated operating model forDigital Business is important across the different business units to drive ecosystemopportunities

• Adequate resourcing is key to fully monitor the day to day monitoring of the product andevolve the proposition

• The new ecosystem requires unconventional marketing tactics & intensity


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Customer Feedback – Timiza Making a difference in customers lives – Bringing their possibilities to life


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HOW TO FROMALIZE THE INFORMALS - banky-foibe.mg · Preamble • Increasing pace of technology...

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