Fraud scoring

Qualification borrower integrity in retail banking (fraud-scoring)

on the basis of psychosemanticmethods

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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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.


Scoring in microfinance organizations

• According to the passport

• According to the work book

• according to relatives

• Visual evaluation


Idea

At bona fide and unscrupulous borrowers a different attitude to the loan / loan

This difference can be revealed by psychosemanticmethods according to individual systems of meanings

we conducted an experiment

and confirmed our assumption


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 "-"


Filling out a questionnaire

Assessment of the borrower

Decision to grant / refuse a loan

Interface example


Questions Example


Description of the application


• 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"

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

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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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

EXPERIMENT

Checking the accuracy of psychosemantic scoring in the retail lending system

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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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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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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%.


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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%


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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%.


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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%.

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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START UP

Possibility of commercial realization of the idea of psychosemanticonline scoring in the system of retail lending and microfinance

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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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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% *

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/

http://www.rg.ru/2013/01/29/mikrozaymi.html

http://www.eg-online.ru/news/164555/

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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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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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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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

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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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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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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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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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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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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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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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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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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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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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 [email protected]

Semenova Irina

PhD (candidate of technical sciences), expert in the field of system analysis and databases

semenova.pro mob. + 7-919-000-11-74 [email protected]

Andieva Elena

PhD (candidate of technical sciences), expert-analyst

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mailto:[email protected]

mailto:[email protected]

Fraud scoring

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