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Transcript of Paretix Mobile Lending Marketplaceinsightnow.xyz/wp-content/uploads/2019/03/paretix-intro.pdf ·...
Paretix Mobile Lending Marketplace
February 2019
Pare ’s novel solu on allows lenders to serve untapped credit segments, while maintaining low-opera onal costs and reducing credit losses
THE OPPORTUNITY
FI’s find it difficult to profitably lend to some customers segments such as
LOW INCOME
Unbanked & SMB’s
NEW TO CREDIT
Millennials, new
businesses and
immigrants
EXOTIC
Transact in new platforms such
as mobile wallets or crypto
PARETIX SMART LENDING
DATA GATHERINGFrom user mobile phone, data partners and banking
data
PROFILINGLearning credit scoring algorithms based on alternative data and historical behavior
PERSONALIZED OFFERDefines lending policy and
tailors the loan terms to customer needs
COLLECTIONSPersonalized collection process with reminders and explanations
Paretix’ Smart Lending relies on historical data from:
DATA GATHERING
Lending AppBorrowers allow access to logs in
their mobile devices including:
AppInstalled
and usage
List of contacts q
device setting
History of calls,
SMS/MMS.
Geolocatiohistory
Data PartnersProviding historical transaction data
for their customers
Banking DataSavings and credit history for
customers
Public DataPublicly available data for credit
scoring assessment
MNO
Retailers
Mobile Wallets
Credit Bureau
National Registry
Credit history
Savings history
Paretix profiling methodology enable lenders to assess credit and fraud risk and decide whether they fit the lenders risk policy
FinancialAbilities
PsychologicalProfile
Map of SocialRelations
Area of PhysicalPresence
PROFILING
Monitoring unique characteristics
12 years expertise analyzing alternative data for lending
20+ banks as customers Provided analysis supporting 10M+ credit decisions
1.5% defaults on average 40+ data scientists AI lending algorithms proven in various segments and
geographies
TRACTION
Enable your organization to reach different segments
Paretix Smart Origination proven use cases:
MICRO LOANSEnable the underbanked, short-
term, unsecured loans
WORKING CAPITAL LOANSMSMEs can access credit based
on their transactions
POINT OF SALE LOANSRetailers can offer buyers loans to purchase white goods, phone
device or vehicles.
USE CASES
The Paretix platform can be applied to different business environments.
AVAILABLE CHANNELS
Direct Customer FacingThe Paretix platform can be set up to face the end customer through USSD
or Mobile APP.
Credit Officers SupportingThe Paretix platform can be set up to support credit officers in the field to
complete loan applications and collect additional data.
Go live within 12 weeks
Easy to integrate with your current systems
CUSTOMER EXPERIENCEEasily add Pareti’s SDK to existing lending app or
implement Paretix’s white label lending app
BANK INTEGRATIONIntegrates via API to your core system to enable
real time disbursement
INTEGRATION
SUMMARY
Proven algorithms that limit your risk Go live fast in 12
weeksAI solutions for
the entire customer life cycle
Highly satisfied customers
Experienced local partners
1 2 3
4 5
WHERE DATA EMPOWERS PEOPLE
• About Paretix
• Paretix Algorithms
• Demo
• Portfolio Risk Management
Agenda
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About Paretix
• Established in 2007
• Customers - 20+ Banks and Fintech lenders, mainly from Asia and Africa
• Solution - Credit Origination technology based on mobile and payment data. Integrates with MNOs/payments banks and with lenders’ core systems
• Team - 40+ data scientists experts in credit analytics
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Paretix Accelerates your Mobile Lending Growth
Advanced risk management capabilities
Links between Payment Banks and lenders in a streamlined process
A Machine learning algorithm based on mobile and payment data is used for underwriting
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Machine Learning Algorithm for Underwriting
• Based on data such as payments and saving history, CDR’s and social recommendations
• Enables usage of additional data sources like banking transactions, credit bureau and questionnaires
• Algorithms can be explained to lenders and users
• Performance improves over time as more data is collected
Transparent, learning algorithm based on payments and mobile data
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Customizing Credit Scoring for your Organization
There are three main stages in credit scoring customization:
• Define the exact need such as credit products and segments to focus on
• Define the relevant data sources
• Calibrate the algorithm to your organization
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Credit Products that we Support
• Micro-loans – unsecured, small ticket, short term loans.
• SME loans – unsecured loans to small and medium enterprises mostly to fund working capital
• Hire Purchase – “Semi-collateralized”, limited loans. Generally given in partnership with a retailer, with specific repayment guidelines
• Salary-advance – loans for employees of established companies
• Mobile Wallet Agent loans – funding mobile wallet agents float
WHERE DATA EMPOWERS PEOPLE
Banking Data • Traditional data sources such as core system, credit bureau and questionnaires
Partners Data• Service providers with large customer base such as MNOs, utilities companies and retailers• Big employees with thousands of long term employees such as the army, schools, airlines and
more • Industry aggregators for SME loans such as large tea and milk manufacturers
Social Data• Social network connections and activity usually not predictive enough for credit scoring• Recommendations can be relevant because unpaid loan could damage recommender's reputation• Co-borrowers or guarantors are the strongest predictors
Types of Data Sources
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• A robust scoring algorithm is usually composed of 2-3 different data source types (e.g. mobile money transaction data + personal data)
• From each source type the best available parameters are chosen for modeling
• A list of available parameters will enable us to provide an initial GINI estimation.
Data source – more details
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• The Paretix algorithms have been developed over the last 10 years and been used for scoring in more than 200 credit portfolios.
• Responsible for more than 10 million credit decisions for a total of 1.8million unique customers (private and MSME).
• In total more than 1,000 data points from different data were processed through our scoring systems.
• The algorithm is based on more than 150,000 actual defaults in various credit products (e.g. payday loans, credit cards, short term unsecured loans, secured loans etc.) with an average PD of 1.5%.
What are the Paretix algorithms?
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Why GINI?
o Looking at the PD levels might not always be helpful – the observed PD is largely dependent on the applied credit policy
o In addition – in many markets dynamic pricing can be applied such that high PD rates do not necessarily have a large impact on profitability.
o The above shortcomings are mostly overcome when using a model performance measure – e.g. GINI which measures the ability to successfully classify customers into good / bad.
Customer risk level PD Avg. Interesthigh 10% 20%low 1% 1.5%
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Advanced Risk Management capabilities
Daily portfolio monitoring, easy to change credit policies
• Constant monitoring of loan portfolio, all credit decisions are trackable• Enables bank staff to understand credit decisions and challenge them• Customizable tools to change credit policy such as loan amounts, interest rates and more
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• Evaluate the past and current credit performance
• Set risk-based targets to allow for credit growth based on risk appetite
• Identify warning signals - detect credit deterioration
Goals
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Simplicity• Easy to use
Principals
Visualization• Graphical data display
Self Service• Suitable for business users
Flexibility• Customizable (slicing and dicing)
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Risk Portfolio Management Components
• Credit Portfolio Monitoring
• Application Monitoring
• Arrears & Provisions Monitoring
• Scoring Models Performances
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Credit Portfolio Monitoring
Explore your credit portfolio to gain business insights • Understand the impact of various factors on the portfolio risk profile• Credit concentration analysis• Trends analysis of portfolio growth and risk over time• Analysis of expected repayments• Tracking exceptions – watch list of customers at risk
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The challenge:
o A postal bank with a large network of branches operates as a Payment Bank only and cannot offer credit to its customers.
o The postal bank is not allowed to share customer data with other financial institutions to enable its customers access to credit.
“Paretix Mobile Lending Marketplace”:
o Enables both sides to collaborate without sharing sensitive data but only the customer’s scoring based on his transactions.
o The commercial bank can fully control credit decisions through an advanced UX for credit policy updates as well as through analytical marketing tools.
Case study 1 – Micro loans for Payment Bank customers
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The challenge:
o A mobile wallet operator growth was limited by the liquidity of its agents o Most of its 20,000+ agents have difficulties receiving credit from a bank , but the MNO lacks the
necessary infrastructure and experience to extend loans to its agents
The “Paretix Mobile Lending Marketplace”:
o Enables partnerships of the MNO with a commercial bank that was interested in extending credit to the agents.
o Calculates agents’ credit scoring based on MNO’s data about the agents’ transaction and commissions o Credit decisions are controlled by the bank through a dedicated UX enabling full control of the business
process and allowing to perform changes in real-time
Case study 2 – Bank loans for MNO agents
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The challenge:
o A white goods retailer lost deals because many customers did not have credit cards.o Most of those customers could get loans, but the application process is too slow. Once customers leave
the point of sale, they will most likely not return.
The “Paretix Mobile Lending Marketplace”:
o Retailer and lender collaborate to offer instant loans based on purchase and mobile data.o Loans are approved at the point of sale therefore the retailer closes more deals.o Loans are disbursed directly to the retailer, minimizing the lender’s fraud risk.o Retailers and lenders receive the reporting they need without exposing sensitive data to each other.
Case study 3 – Hire Purchase Loans for Retailer Customers
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The challenge:
o A commercial bank with USD 4 billion in secured assets wants to build an unsecured loan portfolio.o The bank was used to a manual underwriting process that was slow and did not serve well the
underbanked.
The “Paretix Digital Lending Solution”:
o An machine learning algorithm was introduced to score applicants based on application data and historical credit behavior.
o Due to the almost instant credit decision and a lean underwriting process, the bank was perceived as a great innovator in the market.
o In addition, Paretix provided a platform for dynamic credit strategies which enabled the bank to tailor its offers to each customer and react quickly to changes in the market.
Case study 4 – Commercial bank builds loan portfolio
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• The bank basically started lending without any analytics in place based on business rules (2011)
• The introduction of the Paretix Expert Scorecard helped to reduce initial losses (2012 – 2013)
• The application of statistical models enabled the bank to grow the portfolio without adding risk (2014 – today)
Case study 4 – cont.
100
200
300
400
500
0%
2%
4%
6%
8%
2011 2012 2013 2014 2015 2016
Portfolio size (m
illion USD)Defa
ult r
ate
Portfolio development 2011 - 2016
Default rate (PD) Portfolio size
Product descrip on: Medium term unsecured loan (up to 6 years)USD 5,000 – 25,000 (average USD 10,000)
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Machine learning – how it works (1/2)
Credit outcome of customers: default / non-default
OUTPUT
core banking data / application data prior credit decision
DATA
Computer CLASSIFICATIONALGORITHM
Has ability to classify new data into non-defaults and defaults
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Machine learning – how it works (2/2)
• The classification algorithm applies the optimal weight for all available parameters
• Depending on the size of the data, the number of defaults and the complexity of the input data, algorithms can be based on either parametric / nonparametric solutions and have a linear or nonlinear kernel.(Example: parametric + linear - logistic regression, parametric + nonlinear - SVM; nonparametric – decision tree)
Para mayor información:[email protected]