Call Network / Call detail records as new Big Data Source to predict Credit Scoring - Statswork

Research paper

CALL NETWORK / CALLDETAIL RECORDS AS NEWBIG DATA SOURCE TOPREDICT CREDIT SCORING

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TAGS-Credit scoring, Social network analysis, Profit measure, Mobile phone data, Big Data Scoring, Call Networks, Metadata

SERVICES-Research Planning | Data Collection | Semantic Annotation | Business Analytics | Bio Statistics | Econometrics

In Brief

Research Planning | Data Collection | Semantic Annotation | Business Analytics | Bio Statistics | Econometrics


Big Data Scoring is a cloud-based creditdecision engine that helps banks, telecoms andconsumer lenders improve credit quality andacceptance rates through the use of big data.

The study demonstrates how including callnetworks, in the context of positive creditinformation, as a new Big Data source has addedvalue in terms of profit by applying a profitmeasure and profit-based feature selection.



Credit scoring is one of the ancient applications of analytics whereinvestors and financial institutions execute statistical analysis toevaluate the affluence of potential borrowers to support themdecide whether or not to grant credit.

In 1956, Fair Isaac was found as one of the first analyticalcompanies contributing retail credit scoring facilities in the US.

It’s a well-known FICO score that has been used as an analyticaldecision instrument by financial institutions, insurers, utilitycompanies and even employers.

Introduction

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Edward Altman developed a z-score model for bankruptcy prediction, whichis still used to this day in Bloomberg reports as a default risk benchmark.

Initially, these models were built using limited data and were based on simpleclassification techniques such as linear programming, discriminant analysisand logistic regression.

The significance of these retail and corporate credit scoring models furtherincreased due to numerous regulatory compliance guidelines such as theBasel Accords and IFRS 9 which specify the inputs and outputs of a creditscoring model together with how these models can be used to computeprovisions and capital buffers.

The most elementary handset passively engenders a vast amount of metadataleaving behind a digital hint of the activity of its user.

Contd.



Credit Scoring Models

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These metadata deliver information on when, how, from whereand with whom we connect. In the beginning, researchersrealized the possible of such data by uploading the followingsoftware into submissive subjects’ phones through the RealityMining project of the MIT4.

They later expanded admittance to actual metadata directly frommobile network providers, leading to larger-scale research andhigher analytical power. Several creativities have occurred, suchas the Data For Development (D4D) challenge prepared byOrange, that delivered datasets to the research community forprojects associated with development.

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In a current survey carried out by the World Bank, mobile phone data seemed at the uppermost position in theBig Data used in SDG-related projects.

However, new sources of data present the chance to profile potential borrowers using a more comprehensiverepresentation of behaviour; they also offer an ethical challenge.

Mobile phone data, e.g., in the form of call detail records (CDR), allows constructing an extensive social network,and using this information to profile repayment behavior can be seen as unfair to borrowers that could bepunished for their mobile cell phone behavior.

Contd.

Call Detail Records as New Big DataSource to Predict Credit Scoring

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https://heinonline.org/HOL/LandingPage?handle=hein.journals/yjolt18&div=6&id=&page=

https://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs169948

More newly, the curiosity in using call networks as a new BigData source for credit scoring has increased power, e.g., withWei et al. expressing the potential value of credit scores gainedwith networks and how planned tie-formation might affect thesescores.

Though especially fascinating concerning the Chinesegovernment’s idea for a social credit system, the study is onlyhypothetical and is missing a significant experiential evaluationof the planned models.

Additionally, recent press coverage on specialized smartphoneapplications that assess people’s creditworthiness using thevast amount of data created by their handsets designates thepotential of call networks as a substitute data source for creditscoring.



COMPARISON BETWEEN THETRADITIONAL CDR ANALYSIS SOLUTIONS

HIGHLIGHTING THE ADVANTAGES AND THE

LIMITATIONS OF EACH




CDRs are an excellent illustration of Big Data source that can be abstracted from their key persistence toapproximate socio-economic variables and populace mobility.

As they are not intended for this purpose, this means that an inevitable prejudice will always influence anyapplication based on these data.

If not correctly understood, this could lead to a severe misunderstanding of the results and eventually,have damaging influences in misleading policy-makers.

Technical issuesSelection biasSpatial bias

1.2.3.


Statistical Limitations

https://digitalcommons.law.yale.edu/cgi/viewcontent.cgi?article=1122&context=yjolt


To defend people’s confidentiality, phone data are anonymised continuously, i.e., all personal data suchas name, address, etc., are either removed from the database or substituted by a randomly producednumber to avoid documentation.

Data are then provided to a third party after a non-disclosure agreement was signed with the MNO.

The persistence of the deal is to prevent CDRs to be shared with another party and to define thepossibility of research questions that will be discovered with the data.

Both the anonymization technique and the NDA are hypothetical to reserve the security of usersprivacy.


Data Privacy

https://www.sciencedirect.com/science/article/abs/pii/S156849461830560X



ConclusionCompared to traditional data composed to calculate official statistics, theyare cost-effective and can deliver earlier or even near real-time insights.

They might also be used to test ideas and define future researchquestions. Credit-scoring agencies and creditors continually check andsize new credit-scoring models.

The accessibility of “big data” could generate opportunities for creditorswho want to prospect, consumers, support new accounts, managecustomers and grow profits.

It is already vibrant that the mobile phone data used in this study isprominent in the sense of ‘Volume’, ‘Velocity’, ‘Veracity’ and ‘Variety’.

Analysis of the data and the resultant well-performing models show that italso has a positive effect for financial inclusion and on model profit, andas such is also essential for ‘Value’: the fifth V of Big Data!

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