Revealing clients’ attitude from transactional data.

1. Dominik Matula 23th June, 2020
Instalment Detector
Revealing clients’ behaviour from transactional data

2. Instalment Detector
Revealing clients’ behaviour from transactional data
sli.do
#PROFINIT
Dominik Matula
Senior Data Scientist
Specialization: Finance, Banking
Research: Pseudosocial networks
VAT Fraud detection

3. Use case

4. 4
Your bank
Use-case
Wild
world
Greta Karl Mark
LOAN LOAN LOAN
LOAN
LOAN
LOAN
LOAN
LOANLOAN
LOAN
LOAN LOAN

5. 5
Key goals of
Instalment detector
ADVANCED
RISK SCORING
CLIENT‘S
LOYALTY
PROFIT
MAXIMIZATION
€€€
CLIENT‘S
SAVINGS
€€€

6. Information mining

7. 7
How to reveal an instalment transaction?
Client Receiver Date € Symbol Note
Karl 123456 2020-04-18 21.45 Loan payment
Anna 513487 2020-05-15 172.99 4783351
Greta 363391 2020-03-21 45.87 76558 Fancy shoes
Thomas 513487 2020-03-14 3000.00 Paying the rest
Receiver
black/white lists
Transaction
note mining
Symbol-based
triggers
› Conventional approaches
OUTDATING
CONFUSING
SPARSE
CONFUSING
SPARSE
Typical amount
filtering
IMPRECISE

8. 8
What if …
› …a transaction doesn‘t contain enough information?
› Use complete RELATIONS
+ Information from all transactions
+ Time series patterns
+ Variability (amounts, symbols)
+ …
Client Receiver Date € Symbol Note
Karl B (new) 2020-06-18 21.45 6612162909 Mobile
Karl B (new) 2020-05-16 21.45 6612162909 Loan payment
Karl B (new) 2020-04-17 21.45 6612162909 Iphone
Advanced feature
engineering

9. Machine learning

10. 10
Results
Battle Plan
Super-duper
features
+ power
- - interpretability
- power
+ interpretability
Black box
Oldies but
goldies
Bayesian
networks

11. 11
Bayesian models intro
Super-duper
features
Id Feature Meaning Adjustment
F1 Amt Time Series Pattern 13 : 2
F2 Specific symbol presence 3 : 2
F3 Note contains loan 12 : 1
F4 Some other cool feature 8 : 1
… … …
PRIOR ODDS: 1:49
(Prior probability: 2 %)
Got those from
TRAINING data
# CASES w F
# nonCASES w F

12. 12
Prior probability
F2 F4
From Naive Bayes to Bayesian network
F1 F3 F5
Posterior probability
⋅
3
1
⋅
3
2
⋅
12
1
⋅
13
2
Id Meaning Adj.
F1 Young sender 3 : 1
F2 Amt Time Series Pattern 13 : 2
F3 Constant Symbol 3 : 2
F4 Note contains 'loan' 12 : 1
F5 Some other cool feature 8 : 1
1
49
⋅
8
1
2 %
86 %

13. Let‘s get advanced

14. 14
› .. is a multilayer Bayesian network model
– RELATION layer
– RECEIVER layer
– Other layers if needed: SENDER, COUNTER-RELATION, …
Instalment detector
RELATION layerRECEIVER layer

15. 15
Other context examples
› RECIEVER context based knowledge
› COUNTER-RELATION context based knowledge
Client Receiver Date € Symbol Note
Karl 123456 2020-04-18 21.45 32154342 Loan payment
Anna 123456 2020-05-15 73.50 99554722
Greta 123456 2020-03-21 45.87 32154688 Loan TV
Sender Receiver Date € Symbol Note
123456 Karl 2020-04-15 5000.00 4783325 cons loan 2020/04
Karl 123456 2020-05-15 43.20 4783325
Karl 123456 2020-06-15 43.20 4783325

16. Achieved results

17. 17
› Implemented in a major European bank (2018)
– Billions of transactions, ca. 1 TB of data
– Hive+Spark, modelled in R
– Further implementations discussed in several other banks
(Germany, Czechia)
› Intended boost: +10%
› Reality: +100%
– Small lending companies
– Changes in banking accounts
– P2P loans (family, friends, …)
– Indirect instalments
– …
Results
1.56M
17k
1.48M
BANK Intersection Extra
INSTALMENT DETECTOR
NumberofInstalmentPayments
271k
Conflict
Feature description DWH Hive Spark *)
No. of unique clients per RECEIVER 2280 s 242 s 71 s
Regex spl[aá]tk **) matching 4260 s 38 s 40 s
*) + initial overhead
**) Real feature contains more complicated expressions. Unfortunatelly, DWH couldn‘t make it…

18. 19
Key advantages of Instalment Detector
Straightforward
INTERPRETABILITY
ADAPTABILITY
to market changes
Client Receiver Date € Symbol Note
Karl B (new) 2020-06-18 21.45 6612162909 Mobile
Karl B (new) 2020-05-16 21.45 6612162909 Loan payment
Karl B (new) 2020-04-17 21.45 6612162909 Iphone
ADVANCED
feature engineering
Directly aplicable in
OTHER FIELDS
Salary
Alimony Leasing
InsuranceGaming
Mortgages

19. 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
Thank you
for your attention!