LIME LOANSMULTI-MARKET ONLINE CONSUMER LENDING

JUNE 2020

LIME ©2020

Russia

LIME ©2020

CORPORATE MILESTONES

Joined MintosP2P Platform

7/2018

Machine Learning lab founded with local technical

university6/2017

Russia Launch4/2014

Poland Launch12/2015

South Africa Launch7/2016

Installment loans introduced

4/2017

100k Loans

6/2016

200k Loans

4/2017

300k Loans10/2017

400k Loans

4/2018

ORIGINATIONS, Q

Corporate partners

Mexico Launch12/2018

500k Loans

5/2019

Insurance introduced

5/2019

MobileScoring

LIME ©2020

Established in 2013

132 employees

51M EUR issued in 2019

Top-10 rated by RAEX on

Russian market

8 years on market

Internal fintech tools

automating business

processes

Ye

ar

2019 Effective interest rate

PDL 363%Installments 235%

Vintage total recovery 6Month

127%

LIME ©2020

Partnered cross products

PDL INSTALLMENTS

To get money for shortterm goals Loans with annuity schedule up

to 6 month

Virtual cards Payment cardsInsurance

Easy and fast from any point for

RU citizens

2 MAIN PRODUCTS: PDL AND INSTALLMENT LOANS

LIME ©2020

BENEFITS OF INSTALLMENT LOANS

Annuity predictable payment schedule:

Approval of higher quality clients

A set term: six or twenty four weeks:

A set sum of loan: from ~ 320 to ~ 900euros:

Due to predictable annuity paymentschedule clients definitely know whenand in what amount to make a payment

Only clients with a high credit score and meetingthe pool of additional criteria are approved forinstallment loans

Significantly lower level of fraud

LIME ©2020

2019 HIGHLIGHTS

YonYOriginations up

By 50%

Introducing a new type of product: insurance

Originated 51m EUR in 2019, versus 34m in 2018.

In May 2019, insurance policies were integrated into PDL andinstallment. Thus, share of policies in total issuance amounted to 5%.The total revenue from insurance reached 2.5m EUR in 2019.

A cascade of scoring models has been introduced for early rejection of theriskiest customers and savings on the cost of paid data sources

Introducing a cascade of scoring models

LIME ©2020

LENDING KPIS - RUSSIA

Qty of loans

% changesTotal

originations (EUR)

% changes

2014 7 769 450 219

2015 50 782 554% 2 465 398 448%

2016 88 614 74% 5 010 293 103%

2017 163 493 85% 14 172 854 183%

2018 295 776 81% 34 377 067 43%

2019 340 066 15% 51 584 176 50%

Annual Quantities and Values of Loans

Blended PD Instalment

2014 60 60

2015 49 49

2016 57 57

2017 87 78 304

2018 122 82 332

2019 151 121 333

Average Loan Size (EUR) Loans share

69%

31%

Instalment PDL

LIME ©2020

LENDING KPIS - RUSSIA

Cumulative Recovery (as % of Originations), *figures as of 31 December 2019 at historical FX, *money-weighted average

2019Originations

(EUR)M1 M2 M3 M4 M5 M6 M12

Q1 16 357 528 105 112 116 118 119 120 121

Q2 13 011 102 103 112 116 119 121 122 122

Q3 10 902 362 101 111 116 119 121 123

Q4 9 664 119 107 116 121 124 126

Borrower Demographics

Women Men

% age % age

2014 47.6 29 52.4 33

2015 49.9 33 50.1 36

2016 52.8 33 47.2 36

2017 52.7 32 47.3 35

2018 55.7 32 44.4 35

2019 44,59 30 55,41 33

LIME ©2020

PAYMENT FLOWTrendlineOutgoing (weekly total) Incoming (weekly total)

During pandemic Out of the crisisBefore

NPL increased

Originations decreased

Funding deficit on P2Pplatforms

Full repayment of debt to investors

Reduced fixed costs

Increasing originations

Restructuring of overdue loansPlanned increase of originations

Planned introduction of newproducts

LIME CREDIT GROUP DEMONSTRATES STABLE GROWTH ON THE RUSSIAN MARKET

2016 2017 2018

2016 2017 2018

3 885

12 568

LCG is growing in the volume of loans to customers without reducing the

quality of the loan portfolio: 2019 vs. 2018 – increase 50%

The LCG demonstrates a stable growth rate: the revenue 2019 increased by

nearly 2X compared with 2018

Despite a tightened regulatory environment in 2019, the company maintains

it’s Total Recovery and Profitability

Despite the significant growth in originations, LCG managed to reduce

unrecovered principal across the year

* Revenue (interest income etc)., thous. Euro

* Originations, thous. Euro Unrecovered principal 180 days

24 577

48 315

10* Implies vintage analysis by loan generations: the financial results of clients (took a loan, for example, in Feb’19) in 180 days

*

4 168

13 135

34 377

51 584

2019 0,00

5,00

10,00

15,00

20,00

25,00

30,00

35,000.30

0.25

0.20

0.15

0.10

0.35

Jan’20 Mar’20 May’20 Jul’20 Aug’20 Sep’20Jun’20Apr’20Feb’20 Oct’20 Nov’20 Dec’20

Forecast

114,00

116,00

118,00

120,00

122,00

124,00

126,00

128,00

1.30

1.25

1.20

1.15

1.10

1.05

Forecast

2019

FORECAST

Total Recovery 180 days by generation of loans*

Jan’20 Mar’20 May’20 Jul’20 Aug’20 Sep’20Jun’20Apr’20Feb’20 Oct’20 Nov’20 Dec’20

Trend

Trend

MARKET OPPORTUNITY

Consumers around the world lack access toreliable credit that they want and deserve. This isespecially true in emerging markets, for historicaland other reasons.

Huge Total Addressable MarketsAnnual lending in these markets is significant – hundreds of millionsand up to billions annually. With their automated processes and lowcost-base, Lime operations don't have to capture much of thesemarkets to be profitable.

Traditional lenders are reluctant to address thisdemand because default rates are high, loan amountsare low, but processing costs are similar to otherproducts.

5 Years of Experience Since early 2014, Lime haveproven that their on-line lending platform can be set upquickly and cheaply, tuned to the local specifics, and beconfigured to address any target market and productmix.

224 227 228 280 340 365 461 1300

Unsecured Short-Term (< 6 month) Consumer Lending annual volume (in USD Millions):

1400 1500 1510 2000

Sources: South Africa National Credit Regulator, “Payday lending market investigation” 2015 UK CMA, “The State of Online Short Term Lending” 2015 Bretton Woods, World Bank WDI, Lime Capital Research

Peru VietnamGreece Chile PhilippinesSouth Africa

Mexico RussiaThailand

KoreaBrazil

Spain

LIME ©2020

AUTOMATED SCORING

Credit scoringCredit application

Auto approve

Manual verification

Auto refuse

Tariff assignment

Blocking

Credit robot & Antifraud model

Repayment

OutstandingCollection scoring

Court Collectionscoring

Short & Long term review

1

2

3

4

5

5

7

3

3

5

Scoring for Leads

LIME ©2020

RISK MATRIX

ProbabilityLow Middle High

1 2

3

4

5

6

1

2

3

4

5

Fraud risk

Credit risk

Model mistake risk

Loyalty risk

Collection risk

6 Lost profit risk

Solvency assessment risks

Economy risks

Lime Credit group manage risks on all of the operation stages. Main risks mitigation strategy connected to solvency assessment. It is important to mitigate economy risks too

LIME ©2020

CREDIT ROBOT & ANTIFRAUD MODEL

• terrorists

• risk PANs / bill numbers

• loosed document

• match table

• age limits

• data from credit bureaus

Credit robot performs static checks oncustomer data and loan applications fortheir authenticity based on credit bureausdata

Antifraud system is based on MLalgorithms model that performs dynamicverification based on open sources data(blacklist and etc.)

Checkpoints examples

What we do for improvement

• Develop potential checkpoints

• Retro-testing checkpoints for target results to confirm efficiency

• Periodically re-analysis the checkpoints.

Firstly, applications are processed by the Credit Robot’s checkpoints to block the criminal and clients with false data

• name from filled bank account

owner differs from name filled in

application form

LIME ©2020

CREDIT SCORING ML SERVICE

Automatic estimation for the probability of loan repayment by this client based on ML algorithms

Less mistakes due to fraudblocking by Credit Robot andAnti Fraud Model

Services performs step-by-step estimation result ofthis model is the clients score that normalized from 0to 1. Client’s score is a probability of repayment

1 2

• Use only free data sources• Rating clients on these data• Passes on the next stage only

clients with good rating

On the first step model: The second consists few iterations

At each iteration model use new NON FREEdata source and:• Rating clients on these data• Passes on the next stage only clients with

good rating… then goes to the next one

Step-by-step estimation advantages

Less money spend on paydata

Scoring model could beretrained on the better qualitytrain

LIME ©2020

Combination of different data sources increases model’s quality rapidly

- Black lists • Terrorists • Extremists

- The Federal Tax Service reports

- Clients application

- Intersection of personal data of the application with the current database

- Bank transactions’ sources

- Credit history bureaus

- Mobile operators • complex to get sensitive data

- Social networks data

- UI-telemetry

- Anti-fraud sources

CREDIT SCORING ML SERVICE

LIME ©2020

ADDITIONAL DATA SOURCES AND MANUAL VERIFICATION

Auto approve

Manual verification

Auto refuse

Model makes this decisionwhen It is hard to tell isthis client reliable or not:chances are the same

Manual verification process

1 Verifiers review client’s applicationsand look through needed data

2 Personal verification that could not be automated.Mostly calls to clients or to client’s employer/ confidant

3 Fulfilling the or correction the application data

4So model could makeunequivocal decision to lend or not on fullest data set

Verifiers complement application with manually found data so model could make a concrete decision

LIME ©2020

PERSONAL TARIFF ASSIGNMENT PROCESS AND LOYALTY PROGRAM

Lime credit group mitigate loyalty risk by offering individual terms and discounts to clients

Important to give clients what they need so they’ll stay loyal and regular. In other case clients will prefer competitors services

Loyalty program

Individual tariff assignment

1

1

Dynamic calculation sums-percentrates in according to score

2With every successful paid loan clientrise available sums and decline rate fornext loan

2Most attractive terms for the first loanallows to get acquainted with our services

With every successful 15 days loan clientrises discount rate for the next loan

General idea: regularly make optimization: retro-test for previous credits by using all the combinations sum-percent-score match table tomaximize received profit \ complex metricHypothesis: independence of repayment statistics from sum in limits of product type

Behavioralmarketing

We use RFM behavioral analysis to drive desirable behavioral

LIME ©2020

COLLECTION SCORING AS OUTSTANDING’S MANAGEMENT

Collection scoring provides most efficient strategies for non-performing clients

Collection scoring gives probability of repayment the outstanding bythis concrete client. Collection score provides the communicationstrategy that is the most efficient for this deb loan.

Collection scoring advantages

1 First collection score:

Provides insides for taking case to court• Trial requests additional costs• How old should be debt to take this additional costs

Second collection score – court score:

Provides insides for debtor’s communication strategy• On which day to start the automatic newsletter• On which day to start personal calls• How much unfulfilled promises could we take before taking

case to the court

Cost optimization:

Automation:

Collection scoring allows toreduce excessive funds spendon personal communication

Machine learning services isway more efficient thenpersonal estimates

In progress

LIME ©2020

REHABILITATION PROCESS FOR REJECTED CLIENTS

Review client’s relatability allows to satisfy the maximum requests and get maximum profit

Client could be blocked on different stages. It is important to monitor client’s reliability over time. If, for example, a client will fill an application with correct data, relatability will grow and the model will be able to give more positive decisions. Not tracking such changes could lead to losses.

30days

90days

1year

Review

Credit application

Review

Review

LIME ©2020

COLLECTION PROCESS

-2 0 15 30 45 60 75 90

Decision ZoneDebt sale

Court (semi-automatic procedure)

Outsourced debt collection

Automated Pre-collection Reminders Phone calls Restructuring offer

On improvement

SMSE-mails

Will be automated

LettersOn

improvement

Collections Success In Numbers

Early On time <30 days <60 days

Principal Recovery

<90 days

Total Recovery(as % of original principal)

47% 62% 76% 82% 84%

130%123%106%

LIME ©2020

PRODUCT DEVELOPMENT ROADMAP

Lime's expertise is in the consumer credit silo, which is highly profitable and funds our geographic expansion and product development:

Partner products:

- Insurance – Auto, Health, Property, Travel

- Vacations / Travel Layaway programs

- Auto Title Loan

Virtual Credit Cards

Revolving Credit

Installment Loans

Instant Loans

Consumer Credit

Leverage Client Base

The client base can be leveraged through partnership deals with providers of other financial services.

>12M

12M

4M

1M

Typical Loan Terms

(months)

The platform can be leveraged by adapting it's decision making power and distribution flexibility to similar businesses.

Employee Background

Tenant Credit Check

Government / NGO Sponsored Specific Purpose MicroFinance

Leverage Platform

Branded Credit Cards• Collection scoring• ML scoring• RFM model

• Voice-bot

• Chat-bot

• Loyalty program

Expected

>12M

Information Services Development Credit

LIME ©2020

Stanislav SergushkinCOO / Co-founder

Before co-founding Lime, Alexey spent a few years working as an implementation consultant for an enterprise applications developer and as a credit card underwriter at a major Russian bank. In his day-to-day role as CEO of Lime, Alexey sets strategic priorities, is the chief system architect, makes most HR decisions, and oversees legal and regulatory compliance. In the three years since its establishment, Alexey has spent time in pretty much every role in the company from collector to underwriter to programmer.

Alexey NefedovCEO / Co-founder

Stanislav and Alexey met at university in the UK, and since both of them had prior experience as underwriters, their ideas about a better on-line lending platform quickly came together. As Lime's COO, Stanislav handles day-to-day operations in addition to leading product development and territorial expansion. The business process flows, risk management procedures, and scoring models in use at Lime are all products of Stanislav's guidance.

FOUNDERS

LIME ©2020

LIME LOANSMULTI-MARKET ONLINE CONSUMER LENDING

Contact:

Kevin Hurley

CFOk.hurley@limecreditgroup.com

LIME ©2020

LEGAL DISCLAIMER

IFRS Financial Information

In addition to the unaudited financial information prepared in conformity withInternational Financial Reporting Standards (“IFRS”) included in thispresentation and its accompanying supporting documentation, the Companyprovides details of its operations, calculations, and financial statements that arenot considered measures of financial performance under IFRS. Managementuses these non-IFRS financial measures for internal managerial purposes andbelieves that their presentation is meaningful and useful in conveying anunderstanding of the activities and business metrics of the Company’soperations. Management believes that these non-IFRS financial measuresreflect an additional way of viewing aspects of the Company’s business that,when viewed together with with the Company’s IFRS results, may provide amore complete understanding of the factors and the trends affecting theCompany’s business. Management provides such non-IFRS financialinformation only to enhance readers' understanding of the Company’s IFRSconsolidated financial statements. Readers should consider the information inaddition to, but not instead of, the Company’s financial statements prepared inaccordance with IFRS. This non-IFRS financial information may be determinedor calculated differently by other companies, limiting the usefulness of thosemeasures for comparative purposes.

Risk and Uncertainties that May Affect Performance

This presentation contains forward-looking statements about the business,financial condition, strategy, and prospects of Lime Capital Partners Inc (further“Lime” or “the Company”). These forward-looking statements represent thecurrent expectations or, in some cases, forecasts of future events and reflect theviews and assumptions of Lime's management with respect to the business,financial condition and prospects of the Company as of the date of this releaseand are not guarantees of future performance. Lime's actual results could differmaterially from those indicated by such forward-looking statements because ofvarious risks and uncertainties applicable to its business. These risks anduncertainties are beyond the control of Lime, and, in many cases, the Companycannot predict all of the risks and uncertainties that could cause its actual resultsto differ materially from those indicated by the forward-looking statements.When used in this presentation, the words "believes," "estimates," "plans,""expects," and "anticipates", as well as similar expressions or variations as theyrelate to Lime or its management are intended to identify forward-lookingstatements. Lime cautions you not to put undue reliance on these statements.Lime disclaims any intention or obligation to update or revise any forward-lookingstatements after the date of this release. A more in-depth, though not complete,discussion of some of the risks and uncertainties that may affect Lime's futureperformance is included as an appendix to this presentation