Qualification borrower integrity in retail banking (fraud-scoring)

1. Qualification borrower
integrity in retail banking
(fraud-scoring)
on the basis of psychosemantic
methods

2. Content
• Concepts
• Traditional credit scoring
• Idea
• Implementation
• Experiment
• Potential consumers
• Methods of implementation
• Benefits and limitations
• Action plan
• Developers and contacts
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3. Concepts
• Credit scoring is a method for classifying borrowers into
different groups when the necessary characteristic is not known
(will the loan return), however, other characteristics are known
which are in some way related to the interest
• Psychosemantics is an area of psychology that studies an
individual system of values that affects the processes of
thinking, memory, decision making, etc.
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4. Scoring in microfinance organizations
• According to the passport
• According to the work book
• according to relatives
• Visual evaluation
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5. Idea
At bona fide and unscrupulous borrowers a different
attitude to the loan / loan
This difference can be revealed by psychosemantic
methods according to individual systems of meanings
we conducted an experiment
and confirmed our assumption
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6. Implementation
When making a loan, the
borrower fills out a
psychosemantic questionnaire
The data is immediately
processed on-line
Result: an assessment of the
borrower "+" or "-"
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Filling out a
questionnaire
Assessment of
the borrower
Decision to grant
/ refuse a loan

7. Interface example
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8. Questions Example
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9. Description of the application
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• from 30 to 50 points
• filling time - 3-10 minutes
• points vary for different socio-
demographic groups
• in the questionnaire there is a system of
protection against "key selection"

10. Decision-making
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On the basis of personal
data, the borrower's
profile is compared with
the reference model of a
bona fide and
unscrupulous borrower

11. Reasons for the non-repayment of a loan
• Planned non-return
• Unforeseen non-return
The proposed psychosemantic system can
recognize only the first type, i.e. Fraud-
scoring
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12. Solutions
Evaluation Solution
"Good" borrower We give out the credit
"Bad" borrower We do not issue a loan
"Unclear"* We do not issue a loan
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* - the "not clear" estimate arises in cases when there is not enough
data for a certain subgroup of borrowers or when the profile of the
borrower can not be attributed to a positive or negative

13. EXPERIMENT
Checking the accuracy of psychosemantic scoring in the retail
lending system
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14. Data collection for the model
947 borrowers on receipt of commodity loans in two
cities of Russia in the stores of household appliances
passed the traditional procedure of credit scoring and
additionally filled the psychosemantic questionnaire
After 6 months we received information from the bank
about how borrowers pay out loans: 880 paid on time,
and 67 borrowers did not pay on the loan
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15. Experiment
From the data on 947 borrowers we:
• randomly selected from 20 to 200 profiles
• made an assessment of these borrowers on the model
based on the psychosemantic method
• offered a loan decision
• compared our decision with the real behavior of the
borrower
• evaluated the correctness of our decision (guessed /
not guessed)
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16. Verification 1. N = 20
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When checking on a random sample: N = 20 people out of 947
A 70% loan was given to those who applied, 30% refused.
Returned - 65%.
Not returned - 5%.

17. Verification 2. N = 50
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When checking on a random sample N = 50 people out of 947
The loan was given to 84% of those who applied, 16%
Returned - 80%
Not returned - 4%

18. Verification 3. N = 99
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When checking on a random sample N = 99 people out of 947
The loan was granted to 76% of applicants, 24%
refused.
Returned - 75%.
Not returned - 1%.

19. Verification 4. N = 200
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When checking for a random sample, N = 200 people out of 947
A 70% loan was given to those who applied, 30%
refused.
Returned - 69.5%.
Did not return - 0,5%.

20. Conclusions on the experiment
• The addition of psychosemantic scoring increases the
accuracy of conventional credit scoring to 98.7%
• The number of issued loans is reduced by 25%
• With the increase in the number of loans granted and
the accumulation of data for the model, i.e. as self-
training, the accuracy of the forecast increases and
the share of loans extended
• The possibilities of evaluation only in the
psychosemantic way require a separate verification
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21. START UP
Possibility of commercial realization of the idea of psychosemantic
online scoring in the system of retail lending and microfinance
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22. Potential consumers
• Microfinance organizations
• Organizations that provide loans on-line
• Banks engaged in retail lending
• Trading networks selling goods with payment by
installments
• Mutual lending systems
• Online Stores
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23. Traditional retail loan
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planned non-repayment of retail loans -
11%
The volume of retail lending in Russia in 2012 amounted to about 6000
billion rubles
Annual growth from 2010 - about 40% *
* According to Euromonitor International
The share of overdue loans in retail lending in 2011 (% in the loan
portfolio) is 5.7% *

24. Loans in microfinance organizations
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In Russia in 2012, about 2-3 million microloans were issued for the
amount (according to various estimates) from 15 to 50 billion
rubles *
average growth of about 50-100% per year *
1183 MFOs as of January 1, 2012 according to the register of the
FSFM **
as of July 1, 2011, there were only 192, for six months the growth was
5 times
Non-return:
 small cities - up to 20%
 large cities - up to 50%
* Российская газета, http://www.rg.ru/2013/01/29/mikrozaymi.html
** http://www.eg-online.ru/news/164555/

25. Competitive assessment technologies
1) Assessment of online credit history: expensive!
• 200 rubles - check of 1 borrower
• 9000 rubles - installation of 1 interface
2) Assessment according to bailiffs: there are no "newcomers" and are in
the process
3) Evaluation of social activity on the Internet: fraud!
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26. What is the value?
Risk reduction:
 the accuracy of the loan repayment estimate to
98.7%
 the "human factor" is excluded (to give "one's own")
by automating the interaction between the borrower
and the lending institution
 Special protection against "key selection" and "good
questionnaire"
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27. What is the value?
Save time:
1-3 minutes for decision making
Save money:
• does not need an office, the evaluation takes place
on-line
• no special trained personnel required
• Reduces the cost of working with arrears
• Cheaper evaluation on credit history
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28. Interest rates
34% - express loan in the store
180% pawnshop
260-1000% - loans in a microfinance organization
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Reducing the risk of non-return
is the possibility of lowering
the% rate and increasing profit

29. Methods of implementing the system
• As an element of a unique
Internet product - on-line
micro-credit systems
• As a remote service for
assessing the good faith of the
borrower for any credit
institution (SaaS)
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30. What is the investment appeal?
• Term of sale up to 6 months
• Payback period from 1 year
• Scalability
• Ability to use in in other countries
• Initial investment of about $ 50,000
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31. What is the investment appeal?
• A certain range of consumers
– Microfinance institutions
– Credit institutions, supported loans on-line
– Banks engaged in retail lending
– Trading networks selling goods with payment by
installments
– Mutual lending systems
• Clear promotion system
– Personal sales
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32. Complexities of implementation
• It is required to form the
initial database for 1000 loans
issued
• Inertness of credit institutions
to introduce innovations
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33. Security Risks
• "Drain" the database from the provider
• Conscious damage to the database from the provider
• Introduction of a virus program for automatic key selection
• Multiple entry of data from one IP address to select a key
• Leak of the program code for decryption and obtaining
calculation algorithms
• Mass distribution through the Internet of questionnaires that
received a positive decision
• Loss of communication during the filling of the questionnaire
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34. Risks of implementation
• There are not enough associations
• Few data on groups of
unscrupulous borrowers
• It may be necessary to adapt the
semantics for each group of
borrowers
• Resistance from borrowers:
unusual deters
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35. What is to be done?
• Designing and implementing a friendly interface for
the client
• Design and implementation of the database and
security system
• Questioning of real clients (about 1000 people) and
building a model
• Checking the model
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36. 2 test options
1) a check on the old borrower model, which we did in 2008 for
short-term loans (3-6 months) at points of sale.
The MFO / bank launches 100 questionnaires when issuing a loan,
we process them on the old model and give the answer: "good
borrower", "bad borrower", "not clear." We compare our
assessment with the real behavior of the borrower and obtain
an estimate of the accuracy of the forecast
A possible source of error is the old borrower model.
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37. 2 test options
2) check on the new model of the borrower.
The MFO / bank launches 1,200 questionnaires when issuing a
loan. Then we are given 1000 questionnaires and the results of
the borrowers' behavior (returned-not returned). We are making
a new borrower model for the MFI / bank.
Then we process 200 questionnaires on a new model, for which we
have no information, and we give the answer: "a good
borrower," "a bad borrower," "it's not clear." The MFI / bank
compares our assessment with the actual behavior of the last
200 borrowers and obtains an estimate of the accuracy of the
forecast.
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38. Publications
Semenov M.Yu., Semenova I.I. Possibilities of psychological means
for assessing the integrity of the lending borrower in retail trade //
The Omsk scientific bulletin .- 2010. -No 5 (91). - P. 134-136.
Semenova II, Andieva E.Yu. On the construction of the
psychological profile of the borrower for risk assessment in the
sphere of consumer lending // Risk Management.-2008.- №1 (45)
.- P.56-63.
and etc.
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39. Developers and contacts
Semenov Mikhail
PhD (psychology, candidate of sciences) expert in the field of economic
psychology and psychology of money
mymoney.pro mob. + 7-919-009-77-37 musemenov@gmail.com
Semenova Irina
PhD (candidate of technical sciences), expert in the field of system
analysis and databases
semenova.pro mob. + 7-919-000-11-74 pro.semenova@gmail.com
Andieva Elena
PhD (candidate of technical sciences), expert-analyst
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