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Transfer Wise Data Talk 2
1.
FIGHTING FRAUD WITH
GOOD CUSTOMERS IN MIND
2.
LAUR +
3.
4.
MAKING THE FINANCIAL
SYSTEM FAIR AND TRANSPARENT
5.
€700 MILLION IN
TRANSFERS PER MONTH
6.
€30 MILLION OF
CUSTOMERS MONEY SAVED EVERY MONTH
7.
TRUSTED ALL OVER
THE WORLD CAN SEND MONEY FROM
8.
TRUSTED ALL OVER
THE WORLD CAN SEND MONEY TO
9.
FRAUD 101
10.
11.
12.
13.
CHARGEBACK =
14.
PRICE
15.
WHAT TO DO
ABOUT IT?
16.
FRAUD AGENTS >>> MANUAL REVIEW
17.
TEAM
18.
FRAUD ENGINE STATIC RULES SCORING
19.
RULES 80+ YEARS OLD +10“FUN”
20.
FRAUD ENGINE STATIC RULES SCORING MANUAL REVIEW
21.
22.
23.
RULE BASED SYSTEM -
Become outdated - New rule for each pattern
24.
PRICE
25.
PRICE SPEED
26.
PRICE SPEED CONVENIENCE
27.
PRICE SPEED CONVENIENCE COVERAGE
28.
TEAM
29.
FRAUD ENGINE STATIC RULES SCORING DYNAMIC RULES
30.
FEATURE 1 FEATURE
2 FEATURE 3DYNAMIC RULE MODEL
31.
SOURCE CURRENCY AMOUNT
TIER £ NUMBER IN EMAIL?DYNAMIC RULE MODEL FEATURE 1 FEATURE 2 FEATURE 3
32.
SOURCE CURRENCY AMOUNT
TIER £ NUMBER IN EMAIL? USD 100-200 Yes EUR 1000-1100 Yes GBP 500-600 No … DYNAMIC RULE MODEL DYNAMIC RULE 1 DYNAMIC RULE 2 DYNAMIC RULE N FEATURE 1 FEATURE 2 FEATURE 3
33.
SOURCE CURRENCY AMOUNT
TIER £ NUMBER IN EMAIL? USD 100-200 Yes EUR 1000-1100 Yes GBP 500-600 No … DYNAMIC RULE MODEL DYNAMIC RULE 1 DYNAMIC RULE 2 DYNAMIC RULE N FEATURE 1 FEATURE 2 FEATURE 3 30% 70% 76%
34.
SOURCE CURRENCY AMOUNT
TIER £ NUMBER IN EMAIL? USD 100-200 Yes EUR 1000-1100 Yes GBP 500-600 No … THRESHOLD: 65% DYNAMIC RULE 1 DYNAMIC RULE 2 DYNAMIC RULE N FEATURE 1 FEATURE 2 FEATURE 3 30% 70% 76% DYNAMIC RULE MODEL
35.
SOURCE CURRENCY AMOUNT
TIER £ NUMBER IN EMAIL? USD 100-200 Yes EUR 1000-1100 Yes GBP 500-600 No … THRESHOLD: 65% DYNAMIC RULE 1 DYNAMIC RULE 2 DYNAMIC RULE N FEATURE 1 FEATURE 2 FEATURE 3 30% 70% ADD +10 76% ADD +10 DYNAMIC RULE MODEL
36.
PRICE SPEED CONVENIENCE COVERAGE
37.
38.
FRAUD ENGINE STATIC RULES SCORING DYNAMIC RULES MANUALLY DEFINED: -
SCORES - THRESHOLDS
39.
FRAUD ENGINE STATIC RULES SCORING DYNAMIC RULES MANUALLY DEFINED: -
SCORES - THRESHOLDS NEW RULE MODEL FOR NEW PATTERN
40.
TEAM
41.
MACHINE LEARNING
42.
MACHINE LEARNING BLACK BOX Machine
learning algorithm RULESOUTPUT FRAUD SCORE 0..100 PARAMETERS Θ . ..
43.
Act IteratePredict
44.
Act IteratePredict TRAINING ENGINE AGENT
45.
Act IteratePredict GOOD / BADPARAMETERS Θ FRAUD SCORE TRAINING ENGINE
AGENT
46.
STATIC RULES MACHINELEARNING DYNAMIC RULES FRAUD SCORE 0..100
47.
FRAUD SCORE 0..100
48.
0% 25% 50% 75% 100% 0% 25% 50%
75% 100% * ILLUSTRATIVE
49.
0% 25% 50% 75% 100% 0% 25% 50%
75% 100% * ILLUSTRATIVE
50.
RISK MANAGEMENT * ILLUSTRATIVE
51.
PRICE SPEED CONVENIENCE COVERAGE
52.
JASON TAN “NOT A
RACE BETWEEN ALGORITHMS”
53.
BLACK BOX Machine learning
algorithm TRANSACTIONDATA FRAUD SCORE 0..100 PARAMETERS
54.
SCALE DATA FEATURE ENGINEERING •
Number in email • Name in email • Device fingerprinting • Geo-location mismatch • …
55.
OUR CHALLENGE
56.
80 - 20
57.
58.
THANKS! Q?
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