Knowing your clients well and knowing when they need financial support is a key part of a bank’s success in lending. But it is challenging to gather and process information about your customers to know them all entirely. Our senior consultant Lukáš Dvořák will show you how to use data to drive your lending business and improve the conversion rate of loan offers.

1. Lukáš Dvořák March 2021
Data-driven lending
Propensity modelling for banking
Online Webinar

2. 2
Lukas Dvorak
Business Development
Manager, PROFINIT
HELPING CLIENTS
TO BUILD BIG DATA
AND DATA SCIENCE
COMPETENCY

3. 3
Improving client targeting with a propensity model
TRADITIONAL MODEL
SAVERS & LESS ACTIVE
CUSTOMERS
MAINSTREAM CLIENTS
+ NEWCOMERS
REGULAR
BORROWERS
IMPROVED MODEL

4. 4
What else will you learn?
1. What bank 4.0 is?
2. What data is the most useful
3. How to approach data-driven lending
4. How to monetize your data

5. WHY
DATA-DRIVEN?

6. 6
„Banking everywhere,
never at a Bank.“
Brett King

7. 7
Ten years ago,
people went to the bank
to get cash.
Nowadays,
we don’t go to banks at all.
We just send our data.
Banks are not as before. They are IT companies now.

8. 8
Isn’t the data in a bank’s
warehouse more valuable
than the deposits in its
accounts these days?

9. 9
Relationship between banks and clients is changing
DATA DATA
SCIENCE
BIG
DATA
Data competency is key to clients satisfaction
BANK

10. 10
Mindset
People
Company
data
strategy
Goals
Business
cases
Technology
What enables data-driven
banking?

11. 11
„In our case we cannot do it, because“
„We need to focus on core
business now.“
„We are a special case.
AI doesn’t work for us;
we trust in our intuition.“
„Data use isn’t allowed here.“
YES
WE
CAN

12. THE DATA

13. 13
Know Your Customer …like you know your partner

14. 14
Next-level data modelling
BEHAVIORAL MODEL
TRAINED USING
TRANSACTIONAL HISTORY
CLIENTS METRICS
STATISTICAL
FEATURES
TRADITIONAL MODEL
TRAINED USING
CLIENTS METRICS
SCORE

15. 15
Transactional data – ATMs
› ATMs withdrawals
– The most boring data in the bank?
› Let’s try to explore the time series
– Average number of withdrawal per hour in a week
Avg number of ATM withdrawal per hour
City Subway Males
Other Other
Station
Subway

16. 16
Avg number of ATM withdrawal per hour
Transactional data – ATMs: Factory areas

17. 17
Avg number of ATM withdrawal per hour
Transactional data – ATMs: Business centers

18. 18
Avg number of ATM withdrawal per hour
Transactional data – ATMs: Nightlife

19. 19
Pseudo-social network
Connecting people with same behaviour
ATMs
Salary
Cards
Online
Shopping
Money Transactions
Payment
Abroad

20. 20
Well, this is pretty much the same as the real social context…
Pseudo-social network
Connecting people with same behaviour
.73
.21
.98
.54

21. 21
Know your customer …like you know your partner
TRANSACTIONS
& CLIENT DATA
SIMILARITIES MICROSEGMENTS
OF „ONE“
› By getting a holistic customer view

22. 22
P2L
› Client data: age, region, education, risk scores, etc.
› Product history: loans, investments, insurance, etc.
› Account data: monthly aggregates, balances, etc.
› Transactional data: card payments, in/out transactions
› Microsegmentation
– Spending behaviour
– Mobility
– Use of services
– Salary and incomes
– Family, households and friends
Important Data Inputs

23. DATA-DRIVEN
LENDING

24. 24
Data-driven
lending
INSTALMENTS
DETECTION
SOCIAL
GROUPS
SALARY
SPENDING
HOUSEHOLD
BEHAVIOURAL
MODEL
INPUT
TRANSACTIONAL
DATA
PROPENSITY-TO-BUY
SCORING
LIFE-EVENTS
MONITORING
HOUSEHOLD
EMPLOYMENT
SPENDING
OPPORTUNITIES
TO OFFER
EVENT
TRIGGERS
LOAN
REFINANCING
DATA-DRIVEN
CAMPAIGNS
INPUT
CLIENT DATA
TARGETED
LOAN OFFERS

25. 25
P2L
INSTALMENTS
DETECTION
SOCIAL
GROUPS
SALARY
SPENDING
HOUSEHOLD
BEHAVIOURAL
MODEL
INPUT
TRANSACTIONAL
DATA
PROPENSITY-TO-BUY
SCORING
LIFE-EVENTS
MONITORING
HOUSEHOLD
EMPLOYMENT
SPENDING
OPPORTUNITIES
TO OFFER
EVENT
TRIGGERS
LOAN
REFINANCING
DATA-DRIVEN
CAMPAIGNS
INPUT
CLIENT DATA
TARGETED
LOAN OFFERS
Event
monitoring

26. 26
P2L
Instalment
detection
INSTALMENTS
DETECTION
SOCIAL
GROUPS
SALARY
SPENDING
HOUSEHOLD
BEHAVIOURAL
MODEL
INPUT
TRANSACTIONAL
DATA
PROPENSITY-TO-BUY
SCORING
LIFE-EVENTS
MONITORING
HOUSEHOLD
EMPLOYMENT
SPENDING
OPPORTUNITIES
TO OFFER
EVENT
TRIGGERS
LOAN
REFINANCING
DATA-DRIVEN
CAMPAIGNS
INPUT
CLIENT DATA
TARGETED
LOAN OFFERS

27. 27
P2L
INSTALMENTS
DETECTION
SOCIAL
GROUPS
SALARY
SPENDING
HOUSEHOLD
BEHAVIOURAL
MODEL
INPUT
TRANSACTIONAL
DATA
PROPENSITY-TO-BUY
SCORING
LIFE-EVENTS
MONITORING
HOUSEHOLD
EMPLOYMENT
SPENDING
OPPORTUNITIES
TO OFFER
EVENT
TRIGGERS
LOAN
REFINANCING
DATA-DRIVEN
CAMPAIGNS
INPUT
CLIENT DATA
TARGETED
LOAN OFFERS
Propensity-to-buy
modelling

28. 28
P2L
Automated
scoring
Outcomes
Propensity-to-loan
score for each client
individually
Probability that a client
takes a consumer loan
Selection and ranking
of clients for sales
campaigns
› Input data
– Client data, products,
monthly aggregates
– Transactional data,
pseudo-social networks
– Microsegmentation
Shopping behaviour,
mobility, billing for
services, salary and
incomes
Propensity-to-buy modelling

29. 29
Data Science platform in a company environment
DATA
SOURCES
BIG DATA
PLATFORM
DWH
STREAMING
MLOps
WORKFLOW
MNG.
DevOps
ENGINEER
CI/CD VERSIONING
SYSTEM
RETAIL
DATAMART
BI
ANALYST
CAMPAIGN
MNG. TOOLS
BUSINESS
USERS
MODEL
MNG.
TARGET
POOL
RECOMMEDER
TOOL
CALLSCRIPT
GENERATION
MODEL
REPOSITORY
DATA
SCIENTIST
SCORING

30. MONETIZING
DATA

31. 31
Propensity-to-buy modelling
Model as a campaign selection tool
1. Effective use of a thin channel
2. Selection of responsive clients
3. Next-best offer
%
1 : 20
1 : 5 1 : 40

32. 32
TRADITIONAL MODEL
SAVERS & LESS ACTIVE
CUSTOMERS
MAINSTREAM CLIENTS
+ NEW COMERS
REGULAR
BORROWERS
Propensity-to-buy modelling
BASELINE
THE VALUE INCREASE
WITH AN IMPROVED
TARGETING

33. 33
IMPROVED MODEL
TRADITIONAL MODEL
SAVERS & LESS ACTIVE
CUSTOMERS
MAINSTREAM CLIENTS
+ NEW COMERS
REGULAR
BORROWERS
Propensity-to-buy modelling
BASELINE
THE VALUE INCREASE
WITH AN IMPROVED
TARGETING

34. 34
› 𝑿 – Channel capacity
› 𝑹𝟎 – Baseline response rate
› 𝑳 – Lift of response rate
› 𝒊𝒏𝒄 – Unit income
Propensity-to-buy
monetization example

35. 35
1
5
10
0% 10% 20% 30% 40% 50%
Velikost výběru
Lift
Model 1
Model 2
▼
› 𝑿 – Channel capacity
› 𝑹𝟎 – Baseline response rate
› 𝑳 – Lift of response rate
› 𝒊𝒏𝒄 – Unit income
Propensity-to-buy
monetization example
baseline
Sample of the whole client base

36. 36
1
5
10
0% 10% 20% 30% 40% 50%
Velikost výběru
Lift
Model 1
Model 2
▼
Value:
› 𝑿 – Channel capacity
› 𝑹𝟎 – Baseline response rate
› 𝑳 – Lift of response rate
› 𝒊𝒏𝒄 – Unit income
𝑿 ∙ ∆𝑳 ∙ 𝑹𝟎 ∙ 𝒊𝒏𝒄
Example:
𝟏𝟎𝟎. 𝟎𝟎𝟎 ∙ 𝟓 − 𝟑. 𝟓 ∙ 𝟏% ∙ 𝟒𝟎𝟎€
= 600.000 €
Propensity-to-buy
monetization example
Sample of the whole client base
baseline

37. 37
~400 000 clients
Case Study: Predicting consumer loans
Propensity score of the client base
Histogram
X – Predicted probability (next 12 months)
Y – Number of clients
Logistic transformation - SCORE
Histogram
X – SCORE
Y – Number of clients

38. 38
TARGET
Loans taken by the client base
Performance: Overall
PREDICTION ACCURACY
AUC = 0,869
Gini = 0,738
K-S = 0,586

39. 39
Performance: Quantiles
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
Relative
cumulative
frequency
(whole
base)
Loan acceptance period (months)
Clients with the highest SCORE (top 20%)
took out more loans in 60 days
than the rest of the clients combined within 1 year.

40. 40
Clients’ SCORE vs. banking segmentation by activity (CLN_SEG)
Client segments
TARGET rate
CLN_SEG
ACT SAC NEW NAC SAV CLOSED TOTAL
PROP_12M_20TILE
1 0,20% 0,10% 0,00% 0,10% 0,00% 0,10% 0,10%
2 0,40% 0,40% 0,00% 0,30% 0,10% 0,10% 0,30%
3 0,60% 0,30% 0,00% 0,50% 0,40% 0,20% 0,50%
4 0,70% 0,40% 0,80% 0,60% 1,50% 0,30% 0,60%
5 1,10% 1,10% 1,80% 0,80% 0,50% 0,50% 0,90%
6 1,40% 1,10% 0,50% 0,80% 2,50% 0,30% 1,10%
7 1,90% 1,40% 1,00% 1,50% 0,00% 0,30% 1,60%
8 2,10% 2,00% 2,40% 1,40% 0,00% 0,50% 1,80%
9 2,70% 3,30% 1,60% 1,70% 2,10% 0,70% 2,30%
10 3,60% 2,80% 3,00% 1,80% 0,00% 0,80% 2,90%
11 4,30% 3,50% 4,50% 2,20% 0,00% 1,10% 3,50%
12 5,80% 5,40% 4,40% 2,40% 5,00% 1,60% 4,30%
13 6,70% 6,10% 5,60% 3,80% 7,30% 2,30% 5,20%
14 8,00% 9,50% 6,90% 6,70% 4,30% 2,20% 7,10%
15 10,00% 11,60% 9,80% 9,10% 4,30% 2,90% 9,40%
16 14,40% 16,70% 14,10% 12,00% 11,10% 3,80% 13,10%
17 19,50% 21,50% 19,50% 15,30% 0,00% 3,80% 16,90%
18 27,80% 28,30% 25,10% 20,40% 11,10% 4,70% 25,20%
19 38,50% 35,20% 35,50% 26,10% 0,00% 9,20% 35,90%
20 57,60% 48,00% 48,80% 32,70% 0,00% 12,00% 54,60%
TOTAL 13,70% 11,40% 11,20% 4,70% 0,40% 1,50% 9,40%
TOP 10%
SCORE

41. 41
Case Study - SUMMARY
TARGET
rate
CNT_CL_EVER
0 1 [2; 3] [4; 7] [8; 15] >= 16
CLN_SEG
ACT 4,8% 24,8% 39,2% 54,6% 73,9% 92,7%
SAC 2,6% 18,9% 31,0% 44,9% 63,3%
NEW 5,5% 27,8% 57,9%
NAC 1,2% 9,1% 17,7% 27,4% 41,1%
SAV 0,3% 3,2% 9,8%
CLOSED 0,8% 2,7% 4,7% 8,6%
% CLN Target rate AUC
31% 1,2% 0,75
1% 70,9% 0,59
68% 12,4% 0,84
Inactive & Savers
AUC .75
Frequent borrowers
AUC .59
Active & Newcomers
AUC .84
31 % of clients 68 % of clients 1 % of clients

42. Profinit EU, s.r.o.
Tychonova 2, 160 00 Prague 6 | Phone + 420 224 316 016
Web
www.profinit.eu
LinkedIn
linkedin.com/company/profinit
Twitter
twitter.com/Profinit_EU
Facebook
facebook.com/Profinit.EU
Youtube
Profinit EU
Find out more at
bigdataforbanking.com
Thank you for joining!
 contact directly:
Lukas Dvorak
Business Development Manager
lukas.dvorak@profinit.eu
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