big data scoring
TRANSCRIPT
Big Data Scoring Leveraging Non-traditional Data Sources for Improved Risk Differentiation
13 October 2016
Speaker Bio
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Erik Stephen Kragas
EVP and Head, Credit Risk Analytics Division
Risk Management Group
Siam Commercial Bank PCL
Erik Stephen Kragas is a business leader with over 20 years of experience at some of the world’s most innovative and successful banking, financial services, and technology organizations. He specializes in building and maximizing returns from advanced analytics infrastructure, teams, and capabilities, and has a passion for creating high-impact and needle-moving business solutions through advanced analytics.
In his current role as EVP and Head, Credit Risk Analytics Division, Siam Commercial Bank, Erik sets the strategic direction and leads the development of analytical risk management solutions for retail, SME, and corporate business lines. In addition to coaching a large team of analytics professionals, he collaborates closely with senior stakeholders within Risk and across the business units, Group Strategy, Operations, Finance, IT, Audit, and the Bank of Thailand.
Prior to SCB, Erik was the Global Solutions Leader and Head of Advanced Analytics for MasterCard Advisors in the Asia, Pacific, Middle-East and Africa region, based in Singapore. He also held regional leadership roles in CRM and Marketing Analytics at Citibank and DBS Bank in Singapore. Erik began his career at American Express in New York. He also held leadership positions with J.P. Morgan Chase, and IBM Consulting in the United States.
Erik graduated with a MA in Economics from New York University, and a BS in Statistics from University of South Carolina.
Read more or contact Erik at www.linkedin.com/in/erikkragas.
The problem (and opportunity) of Financial Inclusion.
“Globally, about half of all adults are
„financially excluded‟ — they do not have
access to banking-type services, including
the ability to access credit.” MasterCard Advisors
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Inadequate credit bureau coverage is a problem in most countries.
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A number of non-bank fintech start-ups are exploiting non-traditional data sources to underwrite unbanked (and banked) consumers.
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Non-traditional data sources …
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Social Media
• Google+
• Vimeo
• YouTube
• Skyrock
• Renren
• Sina Weibo
• …
E-mail & Messaging
• Gmail
• Outlook
• AOL
• Yahoo
• Line
• Tencent
• …
Mobile Device
• Phones
• Tablets
E-commerce
• Amazon
• eBay
• PayPal
• Alibaba
• …
… provide a rich demographic and behavioral data …
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Social Media • Name • eMail address • Age • Gender • City/Country • Photo • Bio/Profile • Marital Status • Languages • Profession • Employment • Employer • Education • Certification • Interests • Activities • Connections • Messages • Updates • …
E-mail & Messaging • Name • eMail address • Age • Gender • Country • Photo • Languages • Interests • Activities • Contacts • Messages • eMails • …
Mobile Device • Geolocation • Call History • SMS History • Top-up History • Contacts • App Usage • Browser History • …
E-commerce • Name • eMail address • Age • City/Country • Purchases • Sales • …
… yielding powerful customer insights …
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Social Media • Name • Identity • Address • Location • Language • Life-stage • Life-events • Personality Traits • Travel • Employment • Income (est.) • Date of Pay • Financial Hardship • Time of usage • Social Relationships • Relationship Length
and Depth • Interests/Hobbies • …
E-mail & Messaging • Name • Identity • Address • Language • Life-events • Personality Traits • Travel • Financial Hardship • Number of received/sent • Time of usage • Number of Contacts • Length and Depth of
Contacts • …
Mobile Device • Location • Location History • Number of calls/sms • Recipient Location • Time/length of usage • Data activity • Number of Contacts • Length and Depth of
Contacts • Frequency/Recency
of Top-up • Amount of Top-up • …
E-commerce • Name • Identity • Address • Spending Volume • Spending History • Purchase Profile • Interests/Hobbies • Preferences • Time of usage • Sales Volume • Sales History • …
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Social Media
• Authentication
• Credit Scoring
• Fraud Scoring
• Collections
• Lead Generation
• Acquisition
• MGM
• Cross-selling
• Personalization
• Right Offer / Channel / Time
• App Pre-fill
• Event-triggers
• Sentiment
• Retention
• …
E-mail & Messaging
• Authentication
• Credit Scoring
• Fraud Scoring
• Collections
• Lead Generation
• Acquisition
• MGM
• Cross-selling
• Personalization
• Right Offer / Channel / Time
• App Pre-fill
• Event-triggers
• Sentiment
• Retention
• …
Mobile Device
• Credit Scoring
• Fraud Scoring
• Collections
• Acquisition
• MGM
• Cross-selling
• Right Offer / Channel / Time
• Event-triggers
• Retention
• …
E-commerce
• Authentication
• Credit Scoring
• Fraud Scoring
• Collections
• Lead Generation
• Acquisition
• Cross-selling
• Personalization
• Right Offer / Channel / Time
• App Pre-fill
• Event-triggers
• Retention
• …
… and enabling the development of high-impact business applications.
Informed consumer consent / opt-in is key.
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‘Please connect via your social media/e-mail/e-commerce account…
… to authenticate your identity.‘ -or-
… to speed the application process.' -or-
… to complete your application.' ‘
Step 1: Consumer request sent via e-mail. Customer clicks through to …
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Step 2: Complete and submit short application.
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Step 3: Connect via social login to authenticate / speed application / complete application.
Step 4: Sign in.
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Step 5: Accept the terms and formally opt-in.
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Step 6: Data acquisition completed.
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Consent requests for social login are generally meet with acceptance.
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• Over 50% of consumers choose social login to speed up the loan application process.
• Over 60% of consumers on mobile devices opt-in for social login to speed up the loan
application process.
• About 80% of applicants opt-in to share social media when requested as part of the
application process.
• Financial services companies offering social login see up to 50% higher application
completion rates.
Consent login token is valid for one year, enabling ongoing data refresh.
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• Change of eMail address
• Change of Location
• Change of Residence
• Occurrence of Life-event
• Change of Marital Status
• Change in Wallet-Share
• Travel
• Financial Hardship
• Change of Profession
• Change of Employment
• Change of Employer
• Additional Education
• New Certification
• New Interests
• New Activities
• New Connections
• Change in Usage Patterns
• Change in Relationships
• Change of Sentiment
• Change in Spending
• Change in Purchase Profile
• Change in Sales
• Change in Call/SMS Patterns
• Change in Top-up Patterns
Significant changes in a customer’s demographic or behavioral profile are
correlated with their financial risk and opportunity.
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Traditional Data Sources Traditional + Social Data Sources
Credit Bureau
Credit App.
Existing Behavior
Social Data
Credit Bureau
Credit App.
Existing Behavior
For traditional lenders, Social Data supplements traditional data sources.
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Social Data can be combined with existing scorecards to improve ranking.
Overlay Matrix
Master Score
Tra
dit
ion
al
Cre
dit
Sco
re
Social Data Score
Master Score= f(Traditional Credit Score, Social Data Score)
Approve
Decline
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For loan underwriting, the benefits of improved risk differentiation are likely to be greatest in new-to-bank bureau no-hit and thin-file segments.
Scorecard Gini Coefficient
Traditional Data
Social
Data Combined % Impact
New-to-bank w/ Bureau
50% 30% 60%+ ? 20%+ ?
New-to-bank w/out Bureau
30% 30% 40%+ ? 33%+ ?
Significant improvements in risk differentiation drive:
Significant reduction in Loss Rates and/or
Significant increase in Approval Rates
Beyond underwriting, there are many other high-potential applications:
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• Authentication
• Application Pre-fill
• Behavior Scoring
• Fraud Scoring
• Collections
• Lead Generation
• Customer Acquisition
• MGM
• Cross-selling
• Personalization
• Right Offer / Channel / Time
• Event-triggers
• Sentiment
• Retention
• …
Possible business partners specializing in non-traditional data:
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Khap Khun Khrap!