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Rapid Model Refresh (RMR) in Online Fraud Detection Engine
1.
2.
3.
Best Practice in
PayPal Fraud Detection
4.
Rapid Model Refresh
(RMR)
5.
6.
In-Branch
7.
8.
Cyber Spaces
9.
10.
Old-Fashion
11.
Isolated Individual
12.
Limited-Scope Damage
13.
14.
Organized Gang
15.
Multi-Billion Loss
16.
17.
18.
19.
Best Practice in
PayPal Fraud Detection
20.
Rapid Model Refresh
(RMR)
21.
22.
Identify Patterns
23.
Set Review Criterion
24.
Model-Based Score
25.
Rely on Statistical
Models (Logit Models / Neural Nets)
26.
Generate Suspicion Score
27.
Rank Order Transactions
28.
Rule-Based System
29.
Employ Machine Learning
Algorithms
30.
Generate Rule Sets
for Segmentation
31.
32.
Local Solutions without
Global View
33.
Integrate Domain Knowledge
34.
Easy to Implement
35.
Scoring
36.
Long Time-to-Market
37.
Static perspective of
Fraud Trends
38.
Successful Industrial Applications
39.
Ideal for Large-Scale
Domains
40.
Rule-Induction
41.
Require Frequent Refreshes
42.
Burden of High-Volume
Rules
43.
Fits Dynamic Online
Nature
44.
45.
46.
Best Practices in
PayPal Fraud Detection
47.
Rapid Model Refresh
(RMR)
48.
49.
50.
Most risky segments
are further identified by balancing between bad and pass-through rate.
51.
Modelers developed scoring
models with logistic regression / neural network
52.
Risk score is
assigned to each transaction through the system.
53.
Low-risk transactions will
be passed through.
54.
Most risky transactions
identified by rule sets are sent into review queues.
55.
Queued transactions are
prioritized and routed to agents in specific domains.
56.
57.
58.
59.
Best Practice in
PayPal Fraud Detection
60.
Rapid Model Refresh
(RMR)
61.
62.
Optimized Queries
63.
Repeatable Stream
64.
Arbitrary Models
65.
Standard Evaluation
66.
Version Controlled
67.
Model Specs. to
XML
68.
Deploy in Real-Time
69.
70.
71.
72.
73.
74.
75.
76.
77.
78.
Simple Average of
Massive Number of Trees
79.
Take Advantages of
RMR Deployment Layer and Parallel Computing
80.
Use as A
Challenger to Traditional Logistic Regression
81.
Bumping
82.
Stochastic Search from
Massive Number of Trees
83.
Improve Estimation while
Retain Simple Tree Structure
84.
Use to Enhance
Vallina-Version Tree Development
85.
Stump
86.
Exhaustive Search on
1-Dimension Space, e.g. Score
87.
Induce 1-Level Binary
Tree by Minimizing Gini Impurity
88.
89.
90.
91.
92.
93.
94.
Best Practice in
PayPal Fraud Detection
95.
Rapid Model Refresh
(RMR)
96.
97.
Statisticians Build Predictive
Model
98.
Engineers Hard-Code Specification
into On-Line Environment
99.
100.
A Suite of
Challenger Models Built Automatically
101.
Model Specifications Published
in Live Scoring Platform
102.
103.
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