Paretix Mobile Lending Marketplaceinsightnow.xyz/wp-content/uploads/2019/03/paretix-intro.pdf ·...

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Paretix Mobile Lending Marketplace February 2019

Transcript of Paretix Mobile Lending Marketplaceinsightnow.xyz/wp-content/uploads/2019/03/paretix-intro.pdf ·...

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Paretix Mobile Lending Marketplace

February 2019

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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

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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

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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

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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

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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

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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

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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.

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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

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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

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WHERE DATA EMPOWERS PEOPLE

• About Paretix

• Paretix Algorithms

• Demo

• Portfolio Risk Management

Agenda

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WHERE DATA EMPOWERS PEOPLE

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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WHERE DATA EMPOWERS PEOPLE

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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WHERE DATA EMPOWERS PEOPLE

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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WHERE DATA EMPOWERS PEOPLE

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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WHERE DATA EMPOWERS PEOPLE

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

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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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WHERE DATA EMPOWERS PEOPLE

• 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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WHERE DATA EMPOWERS PEOPLE

• 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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WHERE DATA EMPOWERS PEOPLE

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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WHERE DATA EMPOWERS PEOPLE

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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WHERE DATA EMPOWERS PEOPLE

• 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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WHERE DATA EMPOWERS PEOPLE

Simplicity• Easy to use

Principals

Visualization• Graphical data display

Self Service• Suitable for business users

Flexibility• Customizable (slicing and dicing)

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WHERE DATA EMPOWERS PEOPLE

Risk Portfolio Management Components

• Credit Portfolio Monitoring

• Application Monitoring

• Arrears & Provisions Monitoring

• Scoring Models Performances

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WHERE DATA EMPOWERS PEOPLE

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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WHERE DATA EMPOWERS PEOPLE

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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WHERE DATA EMPOWERS PEOPLE

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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WHERE DATA EMPOWERS PEOPLE

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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WHERE DATA EMPOWERS PEOPLE

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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WHERE DATA EMPOWERS PEOPLE

• 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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WHERE DATA EMPOWERS PEOPLE

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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WHERE DATA EMPOWERS PEOPLE

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)

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Para mayor información:[email protected]