A
ACL Analytics (ACL Services Ltd), 6, 33–55
ad hoc, 52
fraud detection in financial crimes and banking, 52–53
inventory fraud of operation supply and demand (case study), 35–50
specifics, 36
techniques for fraud detection, 35
Actionable Intelligence Technology Financial Investigative Software (AITFIS), 8, 125, 130–134
federal agencies using, 134
hierarchy/analytical chart, 133
AML Manager (Fiserv), 148
Anti-counterfeiting, challenge with, 93
Association of Certified Fraud Examiners (ACFE), 3–6, 50, 85, 141
Report to the Nations on Occupational Fraud and Abuse (2010), 51, 141
Report to the Nations on Occupational Fraud and Abuse (2012), 3–6
key findings and highlights of, 3–6
B
Bank fraud, challenge with, 95
case study, 97
Benford's Law analysis, 41,43–45, 83
graph, 84
C
CaseWare. See IDEA Data Analysis Software
Centrifuge Analytics, 91–105. See also Visual Network Analytics
anti-counterfeiting, challenge with, 93
bank fraud, challenge with, 95
case study, 97
advanced link analysis and identity visualization, 99–100
data preparation and connectivity, 95, 98
fraud management process, 100–105
investigative analysis using data visualization, 102
investigative analytics, 103–105
collaborative analysis, 95
unified data views, 94
visualization, 94
link analysis, 92
vs. fraud data analysis, 66–68
D
Data Clarity, 145
Data mining, cross-industry standard process for (CRISP-DM 1.0), 59
Dee Consulting and Fraud Solutions Limited, 43, 46
F
FICO Insurance Manager 3.3, 145–146
Financial crimes and banking, fraud detection in, 52–53
cash transactions, 53
check tampering, 52
financial skimming, 53
financial statement fraud, 53
Fraud, schematics of, 1–14
fraud analytics
Fraud analytics
fraud prevention and detection in, 19–20
incentives, pressures, and opportunities, 21
strategies, 20
using fraud analytics, reasons for, 17–19
new trends in and tools, 137–149
FICO Insurance Manager 3.3, 145–146
Fiserv's AML Manager, 148
Raytheon's VisuaLinks Analytics, 143–145
vs. predictive analytics, 57–76
comparing and contrasting predictive modeling and data analysis, 72–75
conflicts within methodologies, 69–70
CRISP-DM vs. fraud data analysis, 66–68
methodologies, comparing and contrasting, 60–64
SAS/SEMMA vs. fraud data analysis, 68–69
13 Step Score Development vs. fraud analytics, 64–66
G
I
i2 Analyst's Notebook (IBM), 7, 107–125
fraud and fraudsters, rapid investigation of, 108–109
money-laundering scenario, using in, 121–125
general process used in, 121
IDEA Data Analysis Software (CaseWare), 6, 77–89
correlation, trend analysis, and time series analysis, 83
detecting fraud with, 79
fraud analysis points with, 82–83
purchase fraud of employee as vendor, 86–87
collaborative analysis, 95
unified data views, 94
visualization, 94
Inventory fraud of operation supply and demand (case study), 35–50
duplicate invoices, 37, 38, 39
invalid product codes, 37–39, 40, 41
invoices billed on weekends, 39, 41, 43–44
quantity-on-hand vs. quantity-on-order analysis, 46–49
unavailable, backordered, or deleted product codes, 39
specifics, 36
L
Link analysis, 92
M
Millar, Peter, 331
N
Nigrini, Mark, 83
O
Occupational fraud, impact of, 3
P
Peterson, Marilyn, 16
Predictive analytics, vs. fraud analytics, 57–76
comparing and contrasting predictive modeling and data analysis, 72–75
CRISP-DM vs. fraud data analysis, 66–68
methodologies
comparing and contrasting, 60–64
SAS/SEMMA vs. fraud data analysis, 68–69
13 Step Score Development vs. fraud analytics, 64–66
R
Ratley, James D., 3
Raytheon. See VisuaLinks
Relationship graphs, 100
Report to the Nations on Occupational Fraud and Abuse
impact of occupational fraud, 3
S
model development life cycle (with SEMMA), 60–64
SAS/SEMMA vs. fraud data analysis, 68–69
“South Sea Bubble” scandal, 2
T
13 Step Score Development vs. fraud analytics, 64–66
V
18.226.52.124