Application of machine learning algorithms in debt collection, that includes: portfolio valuation, customer segmentation, operations optimisation

1. Data Science
in Business
Example from Debt Collection
Industry
1

2. Sergey Sviridenko Vladislav Sikach
VFG
Head of Process Development
Senior Process Manager
Process Manager
Quantitative Analyst
Junior Quant
2

3. 3

4. Data
Science
4

5. AREAS
Valuation Segmentation Processing
5

6. Portfolio
Valuation
6

7. 1.3M debtors
120+ clients
?
7

8. 8

9. vs
9

10. Debtor
Segmentation
10

11. 11
1. Value based
Customers grouping according
to their value.
2. Loyalty based
Used to separate loyal
customers (obedient payers)
from migrators/switchers.
Who will
bring
only
losses?
Who will
bring more
profit?
Debt becomes miniscule?
Any contact occurs?
Contact with Debtor occurs?
Agreement to pay given?
Promise given?
Started to pay?
Active Cases
Communication Channel Exists
Successful Action
Contact with Debtor
Agreement to Pay
Promise to Pay
Payment
Paid-off
Any address or phone added?
1. Value Chain
2. Loyalty
Convertible
(haven’t paid before)
Ongoing
(paid in last 60 days)
Revertible
(paid >60 days ago)
Optimal Segmentation
Initial + Operational Features

12. 12
Optimal Segmentation
Initial + Operational Features
Known Best & Worst
Cases
3. Propensity based
Binning of customers in groups
according to their propensity scores.
Call list sorting by scoring, Known
Best/Worst Segmentation
5. Behavioral based
Used to develop customized product
offering strategies. It is about willingness
and ability to pay off debt (promise
fulfillment), attitude toward collectors
4. Socio-demographic
Used for promoting specific life-stage-
based products. Gathering people into
groups by Country, Region, Age, Sex for
special offers

13. 13
CRISP-DM

14. 14

15. Additional data structures and
procedures for cases (debtors,
phones, addresses and other
entities) attributes & statuses
changes
Part of segmentation and
flows are parameterized in
decision-making system
integrated in Qualco CRM
Prepare analyses/tests
results for presentation to
Management Board
OR
IN IN
Analyses,
Models,
Significance,
Hypotheses
testing
15

16. Optimal
Processing
16

17. 1.3M debtors
2M actions daily
Multiple tools
How to process different segments
Optimally
17

18. Detailed List of
Areas to Improve
18
What is the best time to call depending on segment and phone type
Intraday recalls after successful (refusal to talk/pay, not identified, agreed but not promised etc.)
Optimal Calling Frequency after unsuccessful results
Optimal predictive dialer configuration (Technical contacts classifier, SCR, LT)
How to choose the best SMS and Letters templates and sequences
How to prioritize addresses for letter sending (~7 different addresses per debtor)
Targeted Skip-tracing (contact data search sources and optimal sequences)
When to deactivate the phone (counters for Gateway Disposition Codes & successful results)
Whom & how much to forgive, what product to offer (simple writeoffs, restructuring, hybrid schemes)
What segments should be visible for Legal & Field Collection Agents

19. Experimental
Framework
Champion/
Challenger
Testing
19

20. (Pi – P0)
additional marginal effect.
Pi and P0 are calculated
historically via regressions
Costi
Cost of ith instrument(s)
usage
(Cashn+1 – Cashn)
additional historical cash
increase gained from
transition
P0 – default probability, w/o
any instrument
Value Chain Stagen
Yields Cash per casen
Value Chain Stagen+1
Yields Cash per casen+1
Pi probability of transition
if ith-instrument is applied
vs.
Expected Profiti = (Pi – P0) ∙ (Cashn+1 – Cashn) – Costi
Maximize the profit at each value chain
step applying the most suitable
instrument
20

21. 21
Profit Instr 1 Instr 2 Instr 3
Case 1 -0.5$ 0.5$ 0.1$
Case 2 0.1$ -0.5$ 0.5$
Case 3 0.5$ 0.1$ -0.5$
Case Instrument
1 2
2 3
3 1
ExpectedProfitpercase
Cases in
queue
Case #1 Case #2 … Casen #k … Case #n
$0
$3
$5
$9
-$2
Case queue for ith instrument
If there is a lack of capacity,
quick win is to use substitute and less profitable instruments
If this situation is repeated day after day,
then you need to increase capacity of particular instrument
If it is unprofitable to process them by any av
develop new/substitute instruments or transfe
Step 1
Step 2
Step 3
Calculate expected profit
for each case and instrument applicable
Assign the instrument,
which yields the maximum profit
Build case queues for each instrument
with expected profit as priority
For given case find the most suitable
instrument & apply it according to priorities

22. Additional data structures
and procedures for different
attributes changes (phones
and cases deactivation /
reactivation rules, dialing
logic, address priority
calculation etc.
Some workflows and rules
can be parameterized in
decision-making system
integrated in Qualco CRM
Prepare analyses/tests
results for presentation
to Management Board
IN IN
Used for analyses,
models,
check for
significance &
hypotheses testing
OR
New business processes,
algorithms, workflows, rules
and solution models are
designed as diagrams for
better process understanding
for business and further
development 22

23. Lessons
Learned
23

24. ● Be curious & go into the field
● Speak domain language
● Deliver fast and frequent
● Show them money, not your Gini
● Do simple models
● Clarify assumptions
● Try something new, but
● Test everything
● Learn different languages
● Know system capabilities
● Know system constraints
● Borrow PM practices
24

25. 25