Dominik Matula presented Instalment Detector, a tool that reveals clients' payment behaviors from transactional banking data through advanced machine learning techniques. It aims to improve risk scoring, maximize profit, and help clients save through detecting hidden loan payments. The tool engineers complex features from relationships between transactions and uses Bayesian networks to achieve a 100% boost in detecting instalment payments compared to conventional methods. The tool provides interpretable results while adapting to market changes and can be applied to other financial fields.

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

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