Russia - MAY 2021 LIME LOANS - Mintos
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Russia
LIME LOANS
MULTI-MARKET ONLINE CONSUMER LENDING
MAY 2021
LIME RUSSIA ©2021
CORPORATE MILESTONES
Machine Learning lab Insurance Over 0,5 bln RUB
Russia South Africa Joined Mintos Mexico
Installment loans founded with local technical introduced Originated
Launch Launch P2P Platform Launch
introduced university 5/2019 4/2021
4/2014 7/2016 7/2018 12/2018
4/2017 6/2017
100K 200K 300K 400K 500K 1,4M
Loans Loans Loans Loans Loans Loans
6/2016 4/2018 5/2019 04/2021
4/2017 10/2017
Corporate partners
MobileScoring
LIME RUSSIA ©2021
Effective interest rate
Year 2020
PDL 363%
Installments 325%
Russia
Vintage total recovery
6M
131% Established in 2013
187 employees
46,2 M EUR issued
since January 2020 to
March 2021
9 years on market
Internal fintech tools
automating business processes
LIME RUSSIA ©20212 MAIN PRODUCTS:
PDL AND INSTALLMENT LOANS
PDL INSTALLMENTS
Loans with annuity schedule up to
To get money for shortterm goals 6 month
Partnered cross products
Insurance Tele-health
Easy and fast from any point for
RU citizens
LIME RUSSIA ©2021BENEFITS OF INSTALLMENT LOANS
Annuity predictable Approval of higher quality clients
payment schedule:
A set term: six or twenty four weeks:
Only clients with a high credit score and meeting
the pool of additional criteria are approved for
installment loans
A set sum of loan: from ~ 320 to ~ 1 120 euros:
Significantly lower level of fraud
Due to predictable annuity payment
schedule clients definitely know when
and in what amount to make a payment
LIME RUSSIA ©20212020-2021 HIGHLIGHTS
YonY
Originations up Originated 13,4 M EUR in Q1 2021 vs 8,0 M EUR in Q1 2020
By 60%
Increased Principal
Principal Recovery 60 dpd +8 p.p. in Q1 2021 vs Q1 2020
Recovery
Average loan amount +7% in Q1 2021 by changing the approach to limit
Increased Loan amount
policy
LTV +15% in Q1 2021 vs Q1 2020 due to an overall improvement in product
Increased LTV
quality
LIME RUSSIA ©2021LENDING KPIS - RUSSIA
Current Working
Annual Quantities and Values of Loans Average Loan Size (EUR)
Capital structure
Total
Qty of %
originations % changes Blended PDL Instalment
loans changes
(EUR)
2014 7 769 450 219 2014 60 60
2015 50 782 554% 2 465 398 448% 2015 49 49 38%
2016 88 614 74% 5 010 293 103% 2016 57 57
2017 163 493 85% 14 172 854 183% 2017 87 78 304 62%
2018 295 776 81% 34 377 067 43% 2018 122 82 332
2019 340 066 15% 51 584 176 50% 2019 151 121 333
2020 306 366 -10% 33 271 418 -35% 2020 107 85 186
PDL Instalment
Q1 2021 114 756 60% 13 429 060 60% Q1 2021 113 80 195
LIME RUSSIA ©2021LENDING KPIS - RUSSIA
Cumulative Recovery (as % of Originations),
*figures as of 31.12.2020 and 31.03.2021 at historical FX,
*money-weighted average
PDL IL
Originations Originations
2020 M1 M2 M3 M4 M5 M6 M12 2020 M1 M2 M3 M4 M5 M6 M12
(EUR) (EUR)
Q1 118 122 123 124 124 125 125 Q1 2 506 683 127 129 130 130 131 131 132
5 078 755
Q2 131 135 136 137 138 138 Q2 2 069 649 141 143 145 145 145 146
4 132 212
Q3 125 128 129 130 130 Q3 3 878 743 132 134 134 135 135
4 418 397
Q4 117 119 120 121 Q4 5 384 886 128 129 130 130
5 802 093
Originations Originations
2021 M1 M2 M3 M4 M5 M6 M12 2021 M1 M2 M3 M4 M5 M6 M12
(EUR) (EUR)
FY FY
34 625 000 123 126 127 128 129 130 136 32 727 000 131 134 135 135 136 137 141
expectations expectations
* Implies vintage analysis by loan generations: the financial results of clients (took a loan, for example, in Feb’20 then M3 is May’20 ). Thus Q1 2021 will appear at the end of Q2 2021.
LIME RUSSIA ©2021PAYMENT FLOW
16000
Outgoing (quartely, in K Euro) Incoming (quartely, in K Euro)
15000
14000
13000
12000
11000
10000
9000
8000
7000
6000
Q1 2020 Q2 2020 Q3 2020 Q4 2020 Q1 2021
Before During pandemic Out of the crisis
Planned increase of originations NPL increased Full repayment of debt to investors
Originations decreased
Planned introduction of new Restructuring of overdue loans
because of Stricter Scoring
products
Funding deficit on P2P platforms Increasing originations
without increasing cost
Reduced fixed costs
LIME RUSSIA ©2021LIME CREDIT GROUP DEMONSTRATES STABLE
GROWTH ON THE RUSSIAN MARKET
LCG is growing in the volume of loans to customers without reducing the quality Despite the significant growth in originations, LCG managed to reduce NPL
of the loan portfolio: Q1 2021 vs. Q1 2020 – increase 60% across the year
NPL 6M * *
Originations, M Euro 67,4 *
51,6 25%
34,4 36,8 20%
15% 17%
13,1 10%
4,2 10%
13,3
5%
2016 2017 2018 2019 2020 Q1 2021
Revenue dynamics despite Regulatory restrictions
Despite a tightened regulatory restrictions in 2020-2021, the company maintains
Max Revenue 250% in 2019 => 150% in 2020
its Total Profitability
Max interest rate 2% in 2018 => 1% in 2019
Revenue (interest income etc.), M Euro Profitability 6M * *
48,3
34,1 * 150%
142%
140%
24,6 24,8
130% 135%
12,6
3,9 120% 128%
6,2
110%
2016 2017 2018 2019 2020 Q1 2021 Oct'19 Nov'19 Dec'19 Jan'20 Feb'20 May'20 Jun'20 Jul'20 Aug'20 Sep'20 Oct'20 Q2 Q4
2021 2021
* 2021 Budget Forecast
** Implies vintage analysis by loan generations: NPL and Profit in 6 months 10MFI MARKET DYNAMICS IN RUSSIA
• MFI market has been booming in Russia in recent years;
• For the first time, fall of volumes occurred in H1 2020,
but already in H2 2020 all indicators recovered, and in
2021 there is already a widespread growth;
• Next year, market growth will be around 15%. Volume
of loans will come close to 0.5 trillion, that is, more
than 1.3 billion rubles in loans per day. 85% of this
volume - individuals;
• Despite the slowdown in growth rates, the MFI market
in Russia will only grow
Source: CBR industry reviews; Expert RA MFI-industry analytics
LIME RUSSIA ©2021MFI MARKET DYNAMICS IN RUSSIA, ONLINE DOMINATION
• First online MFOs began to appear in Russia in 2015,
then no one believed in this segment - risks were too
high. But over time, share of such companies only
grew
• Now 44% of microloans are issued online. If we
estimate in pieces, more than 70% of loans are
originated in online
• Growth stimulus of online segment is high
operational efficiency, customer friendliness, easy
scaling and of course COVID
• Current challenge for online is to learn how to
originate not only small PDLs, but also to cope with
risks in IL segment
Source: CBR industry reviews; Expert RA MFI-industry analytics
LIME RUSSIA ©2021AUTOMATED SCORING
5 5
3
Auto approve
Scoring for Leads 2 Outstanding
4 Collection
scoring
Tariff assignment
Credit application Credit scoring 3
1 Manual
verification 5
Credit robot & 3
Repayment Court
Antifraud model Auto refuse
Collection
scoring
7
Blocking Short & Long term review
LIME RUSSIA ©2021Impact RISK MATRIX
Lime Credit group manage risks on all of the
operation stages. Main risks mitigation strategy
3 connected to solvency assessment. It is important
1 2 to mitigate economy risks too
High
1 Fraud risk
Solvency
5 7 assessment risks 2 Credit risk
Middle
4 Economy risks 3 Model mistake risk
6
4 Loyalty risk
5 Collection risk
Low
6 Lost profit risk
Low Middle High Likelihood 7 Monetary risk
LIME RUSSIA ©2021CREDIT ROBOT &
ANTIFRAUD MODEL
Firstly, applications are processed by the Credit Robot’s checkpoints to block the criminal and clients with false data
Credit robot performs static checks on What we do for improvement
customer data and loan applications for
their authenticity based on credit bureaus • Develop potential checkpoints
data
• Retro-testing checkpoints for
target results to confirm efficiency
Antifraud system is based on ML
algorithms model that performs dynamic
verification based on open sources data • Periodically re-analysis the
and borrower's device data (blacklist and checkpoints.
etc.)
Checkpoints examples
• terrorists • match table • name from filled bank account
• risk PANs / bill numbers • age limits owner differs from name filled in
• loosed document • data from credit bureaus application form
LIME RUSSIA ©2021CREDIT SCORING ML SERVICE
Automatic estimation for the probability of loan repayment by this client based on ML algorithms
Step-by-step estimation advantages
Services performs step-by-step estimation result of
these model is the clients score that normalized Less mistakes due to fraud
from 0 to 1. Client’s score is a probability of blocking by Credit Robot and
repayment Anti Fraud Model
1 On the first step model: 2 The second consists few iterations Less money spend on pay
data
• Use only free data sources At each iteration model use new NON FREE
• Rating clients on these data data source and:
• Passes on the next stage only • Rating clients on these data
clients with good rating • Passes on the next stage only clients with
Scoring model could be
good rating
… then additional, more expensive data are
retrained on the better quality
requested, and the check is carried out again train
LIME RUSSIA ©2021CREDIT SCORING ML SERVICE
Combination of different data sources increases model’s quality rapidly
- Bank transactions’ sources
- Black lists
- Credit history bureaus
• Terrorists
• Extremists - Anti-fraud sources
• Clients at risk of money laundering
- Mobile operators
- The Federal Tax Service reports • complex to get sensitive data
- Clients application - Borrower's device data
• no personal data, only technical
- Intersection of personal data of the variables
application with the current database
- UI-telemetry
LIME RUSSIA ©2021ADDITIONAL DATA SOURCES AND
MANUAL VERIFICATION
Verifiers complement application with manually found data so model could make a concrete decision
Manual verification process
1 Verifiers review client’s applications and look through
Auto approve needed data, check photos of documents
Model makes this decision
when It is hard to tell is 2 Personal verification that could not be automated.
this client reliable or not: Mostly calls to clients or to client’s employer/ confidant
chances are the same
Manual
verification 3 Fulfilling the or correction the application data
Checks on a sources that has not yet been automated -
4
Quick Payments System, Kronos
Auto refuse
LIME RUSSIA ©2021PERSONAL 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
Dynamic calculation Sums/Periods/ With every successful paid loan client
Individual tariff
Rates in according to score and rise available sums and decline rate for
assignment
customer needs next loan
Most attractive terms for the first loan With every successful loan client rises
Loyalty program allows to get acquainted with our discount rate for the next loan
services
Behavioral We use RFM behavioral analysis to
marketing drive desirable behavior
General idea: regularly make optimization: retro-test for previous credits by using all the combinations sum-percent-score match table to
maximize received profit \ complex metric
LIME RUSSIA ©2021COLLECTION SCORING AS
OUTSTANDING’S MANAGEMENT
Collection scoring provides most efficient strategies for non-performing clients
Collection scoring gives probability of repayment the outstanding by Collection scoring advantages
this concrete client. Collection score provides the communication
Automation:
strategy that is the most efficient for this deb loan.
Machine learning services is
way more efficient then
1 First collection score: personal estimates
Provides insides for debtor’s communication strategy
• On which day to start the automatic newsletter
Cost optimization:
• On which day to start personal calls
• How much unfulfilled promises could we take before taking Collection scoring allows to
case to the court reduce excessive funds spend
In progress Second collection score – court score: on personal communication
Provides insides for taking case to court
• Trial requests additional costs
• How old should be debt to take this additional costs
LIME RUSSIA ©2021REHABILITATION 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.
30
days
Credit application
Review
90
days
Review
1
year
Review
LIME RUSSIA ©2021COLLECTION PROCESS
PRE-COLLECTION 1-89 DPD 90+ DPD
STRATEG
STRATEGY PRIORITIZING OUTSORCING A CA
STRATEGY
Y1 1
1
High risk
Possible after 90th day of delay
SCORING SOFT Refused to pay, no HCG
communication,
SCORING unkept promise
HARD COLLECTION
STRATEGY PDL/IL, Standartized
START STRATEGY 2 Score point (Hard
2 collection Calls are made using dialer, offering
Based on Collection
process, phone discounts on loan repayment
credit history Group)
Medium risk calls and auto
Individual
communications
approach to
clients
LEGAL
STRATEGY
STRATEGY
3 3 Deadline for staying in the (possible after 120th day of delay)
HCG
Low risk
AUTOCOMS (SMS, IVR, E-MAIL), MAILING LIST
LIME RUSSIA ©2021PRODUCT DEVELOPMENT ROADMAP
Lime focuses on the development of consumer credit
products. Next 2 years, we do not plan to deviate from Typical The client base can be leveraged through increasing market
this and will increase our market share Loan Terms awareness and launching new products
(months)
The platform will be leveraged by increasing flexibility and speed
Additional products: Core products:
of development
- Health Insurance Installment Loans 9M
- Financial literacy tools • SMEV Development of the
«Mini» Installment Data Sources
3M • Bank IDs category of loans with
Loans integrations
- Products to improve a • QPS (SBP) annuity payments
borrower's credit history
Instant Loans 1M
Client • Collection scoring Development of more
Auto Title Loans 12M
Credit • ML scoring models variability in loan
Expected
Check • Limit-policy model terms
Consumer Credit Information Services Development Credit
Leverage Client Base Leverage Platform
LIME RUSSIA ©2021FOUNDERS
Alexey Nefedov Stanislav Sergushkin
CEO / Co-founder COO / Co-founder
Before co-founding Lime, Alexey spent a few Stanislav and Alexey met at university in the
years working as an implementation UK, and since both of them had prior
consultant for an enterprise applications experience as underwriters, their ideas
developer and as a credit card underwriter at about a better on-line lending platform
a major Russian bank. In his day-to-day role quickly came together. As Lime's COO,
as CEO of Lime, Alexey sets strategic Stanislav handles day-to-day operations in
priorities, is the chief system architect, addition to leading product development
makes most HR decisions, and oversees legal and territorial expansion. The business
and regulatory compliance. In the three years process flows, risk management procedures,
since its establishment, Alexey has spent and scoring models in use at Lime are all
time in pretty much every role in the products of Stanislav's guidance.
company from collector to underwriter to
programmer.
LIME RUSSIA ©2021LIME LOANS
MULTI-MARKET ONLINE CONSUMER LENDING
Contact:
Kevin Hurley
CFO
k.hurley@limecreditgroup.com
LIME RUSSIA ©2021LEGAL DISCLAIMER
IFRS Financial Information Risk and Uncertainties that May Affect Performance
In addition to the unaudited financial information prepared in conformity with This presentation contains forward-looking statements about the business,
International Financial Reporting Standards (“IFRS”) included in this financial condition, strategy, and prospects of Lime Capital Partners Inc (further
presentation and its accompanying supporting documentation, the Company “Lime” or “the Company”). These forward-looking statements represent the
provides details of its operations, calculations, and financial statements that are current expectations or, in some cases, forecasts of future events and reflect the
not considered measures of financial performance under IFRS. Management views and assumptions of Lime's management with respect to the business,
uses these non-IFRS financial measures for internal managerial purposes and financial condition and prospects of the Company as of the date of this release
believes that their presentation is meaningful and useful in conveying an and are not guarantees of future performance. Lime's actual results could differ
understanding of the activities and business metrics of the Company’s materially from those indicated by such forward-looking statements because of
operations. Management believes that these non-IFRS financial measures various risks and uncertainties applicable to its business. These risks and
reflect an additional way of viewing aspects of the Company’s business that, uncertainties are beyond the control of Lime, and, in many cases, the Company
when viewed together with with the Company’s IFRS results, may provide a cannot predict all of the risks and uncertainties that could cause its actual results
more complete understanding of the factors and the trends affecting the to differ materially from those indicated by the forward-looking statements.
Company’s business. Management provides such non-IFRS financial When used in this presentation, the words "believes," "estimates," "plans,"
information only to enhance readers' understanding of the Company’s IFRS "expects," and "anticipates", as well as similar expressions or variations as they
consolidated financial statements. Readers should consider the information in relate to Lime or its management are intended to identify forward-looking
addition to, but not instead of, the Company’s financial statements prepared in statements. Lime cautions you not to put undue reliance on these statements.
accordance with IFRS. This non-IFRS financial information may be determined Lime disclaims any intention or obligation to update or revise any forward-looking
or calculated differently by other companies, limiting the usefulness of those statements after the date of this release. A more in-depth, though not complete,
measures for comparative purposes. discussion of some of the risks and uncertainties that may affect Lime's future
performance is included as an appendix to this presentation
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