DEMO ENVIRONMENT
Predictive Analytics
System-wide risk trends, score distributions, and model performance
Avg Customer Risk
58
Avg Account Risk
13
Critical Accounts
2
Total Feedback
1
Alert Volume & Avg Risk Score Over Time
Last 30 days
- Alert Count
- High Risk (≥70)
- Avg Risk Score
Customer Risk Distribution
119 customers
Account Risk Distribution
103 accounts
Top Riskiest Customers
1
Victor CentralNode
C015
98
2
Fatima Recruiter
C010
97
3
Sanctioned Individual
CUST_mr6sj2ufm6ml3u
96
4
Sanctioned Individual
CUST_mr6sjewdl22bz5
96
5
Sanctioned Individual
CUST_mr6srs6s2j84n7
96
6
Robert Taylor
CUST-MULE-005
95
7
Ahmad Smurfington
C001
95
8
Dave Sanctions
MG-DEMO-CUST-004
95
9
Test Sanctions
SBX-CUST-003
95
10
Jennifer Martinez
CUST-MULE-006
93
Top Riskiest Accounts
1
MG-DEMO-ACCT-003
MG-DEMO-ACCT-003
100
2
9999000005
ACC-MULE-005
100
3
SG002000001
A003
62
4
ACC_VPN_001
ACC_VPN_001
62
5
SG011000001
A012
62
6
SG013000001
A014
62
7
SG015000001
A016
62
8
SG015000002
A017
62
9
SG012000001
A013
62
10
SG010000001
A011
62
Model Performance
Based on 1 analyst feedback submissions — submit feedback on alerts to improve these metrics
100%
True Positive Rate
Correctly identified risky alerts
0%
False Positive Rate
Alerts flagged as false positives
100%
Precision
Accuracy of positive predictions
0%
Missed Risk Rate
Genuine risks that scored too low