Understanding the Past, Present, and Future Economic ... · KNOWLEDGE FOR ACTION Equifax (NYSE:EFX)...
Transcript of Understanding the Past, Present, and Future Economic ... · KNOWLEDGE FOR ACTION Equifax (NYSE:EFX)...
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Understanding the Past, Present, and Future Economic Behaviors of Customers for Financial Products Over TimeData at a Glance:Data are collected from a major Information Service Company and capture the financial activity of over 2 million consumers:
• Consumer Credit Histories• Collections/Bankruptcy Filings• Consumer Attributes• Tax Liens• File Inquiries
WEBINAR WILL BEGIN SHORTLY
Submit your proposal by Tuesday, May 31st, 2016. Learn more at http://wcai.wharton.upenn.edu/research
Presentation recording and slide deck will be made available via wcai.wharton.upenn.edu by Monday, May 16th, 2016
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A WCAI Research Opportunity
May 13th, 2016
Understanding the Past, Present, and Future Economic Behaviors
of Customers for Financial Products Over Time
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Introductions
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Debbie ShapiroAssociate Research Director, WCAI
Brandon KrakowskyResearch Director, WCAI
Colleen O’NeillExecutive Director, WCAI
Elea FeitSenior Fellow, WCAIAssistant Professor of Marketing, Drexel University
Pinaki GhoshSr. Director Enterprise Data & AnalyticsData Sponsor
Peter MaynardSVP, Enterprise AnalyticsData Sponsor
Trevis LitherlandSenior Data ScientistData Sponsor
Piyush PatelData AnalystData Sponsor
Karen DealAnalytics Program ManagerData Sponsor
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Agenda
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1. Equifax Introduction2. Credit data story/consumer life cycle3. Data walk through4. Use case/research areas walk through5. Demo of the environment6. On-boarding, environment/insights and publishing
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Equifax(NYSE:EFX)isa$2.6B/year($13.2Bmarketcap) globalinformation solutionscompany&memberoftheS&P500withaheritagedatingback116years.
CANADAConsumer #1Commercial#2
UNITEDKINGDOMConsumer #2Commercial#3
CHILE #1ARGENTINA #1
BRAZILCommercial#2Consumer #2
IBERIASpainConsumer#1PortugalConsumer#1
LATAMREGIONPeru #1Uruguay #1ElSalvador #1Honduras #1Ecuador #1
INDIAConsumer#2
RUSSIAConsumer#1
UNITEDSTATESConsumer #1Commercial#3
AustraliaConsumer#1
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EquifaxisaGlobalInformationCompany
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Tobethegloballeaderininformationsolutionsthatcreatesunparalleledinsights tosolvecustomerchallenges.
Equifaxmaintainsdataonmorethan600millionconsumers,81millionbusinessesworldwide,&220millionemployeefiles.
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UniquelyEFX
EFXVision&Data
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Top
2MortgageGSE’s
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EFXMarketPosition
Top
4Wireless,broadband,andpayTVproviders
12ofthetop15banksandFI’s
#1AutoLender
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EFXData&Analytics
Customized High Value
DecisioningSolutions+ + =
Data Analytics Technology CustomerSolutions
Broad-Based and Unique Data
Assets
Value-added Analytical Insights
Software that Integrates Data &
Insights
Sourceandintegratestructuredandunstructureddatafromanyindustry,anywhereintheworldandproactively
deliveractionableinsightvaluedbycustomers
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Credit Data Story and Consumer Life Cycle
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Update • Segmentation • Scores and models • Insights• Estimated
performance • Based on
frequently updated data feedback.
Dynamic insight
• Segment population according to all relevant insights.
• Provide estimated performance to determine actions.
Decision strategies
• Down payment and terms insights impacting pricing,
• Vehicle value insights over loan period,
• When will delinquency occur?
• Projected loss given the vehicle
• Next vehicle purchase
Scores & Triggers: multi-
source data
• Credit Risk score,• Bankruptcy risk
score• Lead score
Scores & Triggers: single
source data
• Delinquency status and history,
• Balances,• Number of
accounts
Attributes
• Credit accounts, • Marketing Data• Utility Data• Payment Data
Data
Marketing Intelligence
Customer Prospecting
Customer Acquisition
Customer Management
Debt Management
Equifax has solutions across the Consumer Lending Life-cycle
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A Few Operational Notes
This webinar is intended for researchers & scholars. It should not be published or presented without permission from WCAI.
Questions about our research program or for press inquiries:
To get access to the data, research teams must submit a proposal to WCAI and the sponsor for approval. More details at the end of this presentation.
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Data Sneak Peek…
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• Credit File: 1- 3% randomly selected consumers full credit file
• Attributes: Advanced Decisioning Attributes • Monthly snapshots from 2010/01 to 2015/12
2010/01
2010/02
2015/12
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Credit File Example
Credit Accounts(aka Trade lines)
Credit Inquiries
Public RecordsCollections
John Doe 123-45-6789
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Advanced Decisioning Attributes | Equifax
Text
Advanced Decisioning Attributes
Attribute Processing
John Doe 123-45-6789
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Example of Advanced Decisioning Attributes
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John Doe123-45-6789
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Trades
Past Due
Balance
• Number Trades• Number of Auto Trades• Number Bankcard Trades• Number Department Store Trades• Number Installment Trades• Number Revolving Trades
• Total Past Due Amount• Total Past Due Amount Bankcard Trades w/Update w/in 3 Months• Total Past Due Amount Installment Trades w/Update w/in 3 Months• Total Past Due Amount Retail Trades w/Update w/in 3 Months• Total Past Due Amount Revolving Trades w/Update w/in 3 Months• Total Past Due Amount Trades w/Update w/in 3 Months
• Percent Balance to High Credit Open Bankcard Trades w/Update w/in 3 Months• Percent Balance to High Credit Open Department Store Trades w/Update w/in 3 Months• Percent Balance to High Credit Open Revolving Trades w/Update w/in 3 Months• Percent Balance to High Credit Open Retail Trades w/Update w/in 3 Months• Percent Balance to Total Loan Amount Open Auto Trades w/Update w/in 3 Months• Percent Balance to Total Loan Amount Open Installment Trades w/Update w/in 3 Months
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Conceptual Entity-Relationship Diagram
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Example Trades Table
Table Name Column Description
Trades
C_ENTITYID A unique consumer id assigned to the consumer record by the source system.Archive The date the archive was created in the source system.Date Reported The date the account was reported to Equifax from the data provider.Date Opened The date the account was opened or first established with the credit grantor.High Credit The highest amount of credit ever extended to this consumer on this account.Credit Limit The maximum amount of credit that can be extended to the consumer on this account.Balance The amount still owed on the account.
Past Due Amount The amount that has not been paid within the agreed upon time frame per the specified terms or payment period of the contract.
Portfolio Type The credit or loan type of the instrument used to grant credit to the consumer.
Rate/Status Code This field contains either a numeric rate or a non-numeric status code for the most current month the account was reported.
Account Designator (ECOA)A code that indicates the consumer's relationship and payment responsibility for the account.
30 Day Counters The number of times the account has been reported as 30 days late60 Day Counters The number of times the account has been reported as 60 days late90+ Day Counters The number of times the account has been reported as 90 days latePrevious High Rate1 The most recent previous high rate.Previous High Date 1 The account reported date for the most recent Previous High Rate being reported.
Previous High Rate 2 The 2nd most recent previous high rate.
The trade table contains consumer trades information.
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QUESTIONS
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Possible Research Areas
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• Continuous updating approaches for risk score analytics
• Are there general-purpose approaches to detecting anomalous consumer behavior that could be used to detect fraud and reporting errors?
• Is it possible to completely automate the process of translating information from raw credit files into attributes/scores without human intervention?
• How can we precisely denote the level of certainty of a consumer’s credit score?
• Something not on this list!
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Research Area 1
Continuous Updating Approaches for Risk Score Analytics
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Data Snapshots
Month 1 Month 2 Month 3 Month 4 Month 5
• Massive changes in the macro-economy cause scoring models to become outdated quickly
• We are interested in methods for automatically updating credit scoring models
• Preference towards Bayesian approaches
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Research Area 2
General-Purpose Approaches for Detecting Anomalies
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• Anomalies in consumer behavior can be fraud cases, but other possibilities abound, e.g., unusual payment patterns in credit file, etc.
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Research Area 3
Automating the Translation of Raw Credit Information into Attributes/Scores
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• We are interested in machine-learning approaches to determining the right variables from consumer event data for calculating credit scores
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Research Area 4
Confidence Measures for Risk Assessment
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• How can we precisely denote the level of certainty/uncertainty of a consumer’s credit score?
• We are particularly interested in consumers with high income but thinner credit files, recent immigrants vs. long-term residents, etc.
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QUESTIONS
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Data
Attributes
Scores
Triggers
DecisionStrategies
DynamicInsight
INSIGHTSUNIVERSITYDATAPANELENVIRONMENT
AttributeNavigator
VisualAnalytics
ModelDevelopment
(R,Python)
ModelIntegration
Tool
Metadata D ataand Model Governance Secu ri ty
ConnexusKeying
AttributeNavigatorExecution Engine
ZementisModelExecution Engine–PMML
Data I ngestion,S tandardizationandHy giene(ETL+ Data Pump+ Hadoop-basedTechnologies )
BigDataStore(Offline)
Governance
InsightsWorkbench
EquifaxData Customer Data Third-party Data
RulesEngine–DecisionPolicy
CAPABILITIES§ StorepetabytesofdatainHadoopclusters
§ Processbillionsofrecordsinminutes
§ Bringtogetherdifferentdatasourceswithinasingleenvironment
§ HighPerformanceAnalyticaltoolstorunmultiplemodels
§ Visualizationtoolstohelpunderstanddata fasterandpivot,asnecessary
HighPerformanceComputing– UniversityDataPanel
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Environment Demo…
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VDI Login Screen VDI Storefront Apps
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Software Tools
• R and RStudio (Open Source version)
• SQL – HIVE and Impala as part of Cloudera Eco system
• HUE – To access Job browser, File Browser and Meta data
• MS Excel
• MS Word
• Google Chrome Browser
• Firefox Browser
• Python
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User On-Boarding Process
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• Unique opportunity to access credit risk data
• Three-tier process for on-boarding academic researchers1. Proposal Submission (and acceptance)2. Background Check3. Access to the data granted by Equifax
• Data cannot be downloaded from Equifax’s secure environment.
• Equifax will review requests to extract results in order to ensure that only results (and not the data itself) leaves the secure environment.
• The process protects all consumers, including you!
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QUESTIONS
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Proposal Format
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Proposals should be in PDF format, no more than 2,000 words, and include the following information:• Title• Author(s) name, title, affiliation and e-mail address: Please designate a corresponding author.• Summary information: a single “slide” that visually summarizes team & project.• Abstract• Introduction: Describe expected contribution(s), covering both the academic and practical aspects. Please
keep it concise, and cite relevant work as necessary to explain your academic contribution. There is no need to include a lengthy literature review.
• Detailed project proposal: Please include enough detail that we can assess the feasibility & merit of the proposed approach. For example, modeling projects should include at least a sketch of the model. In addition, include a rough estimate of how long the project will take. Also include the business relevance of your research and the impact for the sponsor as well.
• Data Needs: Bulleted list of data required or requested for analysis not explicitly mentioned during this webinar. While we can’t guarantee the inclusion of these items, we are happy to investigate the availability.
• Biographies: Include up to a paragraph-long biography highlighting what each team member will contribute to the project.
• Tools: List the software tools you plan to use for your research
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Proposal Submission Process
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• Read more about the proposal format and Submit Proposals here:http://wcai.wharton.upenn.edu/for-researchers/get-the-data/
No later than Tuesday, May 31st, 2016, @ Midnight US Eastern
– Proposals will only be accepted online
– A single PDF combining the written portion and the single slide
• Proposals will be evaluated both on academic contribution and potential to significantly improve the research sponsor’s marketing practice by the proposal review committee:
Eric Bradlow (WCAI), Pete Fader (WCAI), Elea Feit (Drexel University), Brian Dalessandro (NYU), Jane Dokko (U.S. Treasury) and
representatives from the Corporate Sponsor of this dataset
• Contact [email protected], if you have questions prior to submitting your proposal.
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Other WCAI Opportunities for Researchers
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If you registered for this webinar, you will receive regular announcements regarding:
• Upcoming Research Opportunities:
- Identifying & Maintaining Great Financial Advisors
Also find us at:
• SSRN Research Paper series: http://www.ssrn.com/link/Wharton-Cust-Analytics-Initiative-RES.html
• Sign up for updates: http://wcai.wharton.upenn.edu/newsletter/
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