Big- Data and Risk Management - Ido Lustig, PayPal

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© 2014 PayPal Inc. All rights reserved. Confidential and proprietary. Big Data and Financial Risk Management December 1, 2014

Transcript of Big- Data and Risk Management - Ido Lustig, PayPal

© 2014 PayPal Inc. All rights reserved. Confidential and proprietary.

Big Data and Financial

Risk Management

December 1, 2014

© 2014 PayPal Inc. All rights reserved. Confidential and proprietary.

at a glance

143M active accounts in nearly 200 countries

180B payment volume in 2013; ↑24% YoY

2B+ events/day

12 TB new data added per day

7.5M payments per day, 5,000 every minute

500K+ real time queries per second

Less than 100ms average response time

We are talking a lot of data …Big Data!

© 2014 PayPal Inc. All rights reserved. Confidential and proprietary.

• Key dangers stated by experts (partial list):

Buyer Fraud

• Good account is taken over by fraudster (e.g., using phishing)

• Identity theft (using a stolen credit-card in a new account)

• There are not sufficient funds in the Bank

Seller Fraud

• Order never arrives/ merchants don’t send it

• Product is significantly not what you ordered (e.g., picture of iPhone and not an iPhone…)

There is also AUP, AML, terror-funding, etc.,…

Risk/Fraud is regarded many times as a big threat to

online/mobile commerce

© 2014 PayPal Inc. All rights reserved. Confidential and proprietary.

• % of Transactions with fraudulent activity is very close to ZERO!

• Well ahead of our competitors (traditional and start-up competitors)

PayPal gives protection:

• Full buyer/purchase protection (if the seller was fraudulent)

• Full Seller protection for tangible goods

• Global expansion to digital-goods/non-tangible Seller protection as well

However, reality (at least with PayPal) is so much better…

© 2014 PayPal Inc. All rights reserved. Confidential and proprietary.

© 2014 PayPal Inc. All rights reserved. Confidential and proprietary.

© 2014 PayPal Inc. All rights reserved. Confidential and proprietary.

© 2014 PayPal Inc. All rights reserved. Confidential and proprietary.

Car…….. Pat….. Created: Aug 30, 2013

P… Tho… Legit PayPal user

t0 $686.55

Al… Wo…. Created: Sep 3, 2013

t+1:03 min. $673.54

D… Mar… Legit PayPal user

J… Smi… Legit PayPal user

I… Le… Legit PayPal user

Jam…….. Lo….. Created: Aug 30, 2013

Tom…….. Men….. Created: Aug 30, 2013

Alb…….. Rich….. Created: Aug 30, 2013

Fio… Jec…. Created: Sep 3, 2013

Don…….. Li….. Created: Aug 30, 2013

Anj…….. Por….. Created: Aug 30, 2013

t+0:23 min. $686.55

t+1:21 min. $686.55

t+1:47 min. $686.55

t+0:58 min. $686.55

t+0:35 min. $686.55

t+2:00 min. $686.55

© 2014 PayPal Inc. All rights reserved. Confidential and proprietary.

© 2014 PayPal Inc. All rights reserved. Confidential and proprietary.

Identity

IP

Phone num.

Device

Location

Phone

Name

Email

Connection

Change IP

Change num.

© 2014 PayPal Inc. All rights reserved. Confidential and proprietary.

Identity

IP

Phone num.

Change IP

Change num. Name

Email

Location

Phone

Connection

Device IP b-class

IP whois

IP geo

Phone geo

Phone type

© 2014 PayPal Inc. All rights reserved. Confidential and proprietary.

© 2014 PayPal Inc. All rights reserved. Confidential and proprietary.

Fraudsters are people like you and me, they also have habits

Scaling attacks requires them to generate a lot of information

© 2014 PayPal Inc. All rights reserved. Confidential and proprietary.

ana… para. pro… Premier - Mexican Verified [email protected] Time Created: Jun 20, 2013 10:24:26 Contact Information julion 8… huniero culiacan, Sinaloa 895932 Mexico Home: +52 5278…….9700808 Date of Birth: 1981

gab.. noc… sino… Premier - Mexican Verified e…[email protected] Time Created: Jun 20, 2013 10:43:33 Contact Information culubire 8… amada culiacan, Sinaloa 49693 Mexico Home: +52 5374……9978989 Date of Birth: 1981

mor… ind… ca… Premier - Mexican Verified d…[email protected] Time Created: Jun 20, 2013 09:46:35 Contact Information poier 9… esdirre culiacan, Sinaloa 59879 Mexico Home: +52 52…..697998 Date of Birth: 1981

sir…. bon… pas…. Premier - Mexican Verified ju.…[email protected] Time Created: Jun 20, 2013 10:33:41 Contact Information camjutuli 4… indio culiacan, Sinaloa 43869 Mexico Home: +52 52……9798 Date of Birth: 1981

mar… sin… pit… Premier - Mexican Verified ci……[email protected] Time Created: Jun 20, 2013 10:16:19 Contact Information esburgos 9…. pancho culiacan, Sinaloa 38692 Mexico Home: +52 527……9969798 Date of Birth: 1981

Behavioral patterns: • Jun 20, 2013 – Signup • Aug 10, 2013 – Added CC • Jan 11, 2014 – Confirmed CC

Same actions, same dates!

• Name pattern • Account type • Country • Email pattern • City • Date of birth

© 2014 PayPal Inc. All rights reserved. Confidential and proprietary.

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Input

Generation

Algorithm

Execution

Output

Generation

~3 hour run-time

Output:

26 week x 1M accounts

5 min per week

Memory based (not MR)

~150k account a week

~15M account from the last

two years

© 2014 PayPal Inc. All rights reserved. Confidential and proprietary.

© 2014 PayPal Inc. All rights reserved. Confidential and proprietary.

Merchant Industry in PayPal data is partial and incorrect

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• Required only for business accounts

• Incorrect user inputs (lying? / negligent?)

Example 3 – Ambiguous categories:

• Retail (not elsewhere classified)/Chemicals and allied products

• Business/General

• Hardware and Software

Sells:

’Tickets’

Example 1:

Declared:

‘Sports &

Recreation/

General’

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Sells:

’Fashion’

Example 2:

Declared:

‘Business to

Business/Acc

ounting’

© 2014 PayPal Inc. All rights reserved. Confidential and proprietary.

True Industry

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• An automated system for merchant website

categorization:

• Identifies seller websites using PayPal known URL’s

• Crawls seller websites for terms and additional attributes

• Categorizes sites to industry categories by applying statistical

modeling

• Training process is done offline based on examples to

produce a predictive model

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Category probability estimation example – “Travel” category

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Random site scores from training set

“Travel” site scores from training set For each site, we estimate the probability of it belonging to each category given its weight

© 2014 PayPal Inc. All rights reserved. Confidential and proprietary.

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73%

91%

39%

84%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

PayPal and Agent True Industry

True Industry Performance

Accuracy

Coverage

© 2014 PayPal Inc. All rights reserved. Confidential and proprietary.

cost, speed

data volume, accuracy

Effective decision = func (accuracy, speed, cost)

data age

secon

ds

ho

urs

years

Data in-motion

Data in-use

Tiered Big-Data strategy

© 2014 PayPal Inc. All rights reserved. Confidential and proprietary.

Working HL Flow

Crawler Crawler

Manager URL list

Crawled data

Sellers’ Potential Details

External Data source

Queries

API data Attributes

output

Common Infra

THE

WEB

WEB

SERVICES DB

/FILES

© 2014 PayPal Inc. All rights reserved. Confidential and proprietary.

Thank You!

Ido Lustig – [email protected]