A

ACL Analytics (ACL Services Ltd), 6, 33–55

anomalies, 50–55

ad hoc, 52

fraud detection in financial crimes and banking, 52–53

six-step plan, 54–55

inventory fraud of operation supply and demand (case study), 35–50

analysis, 36–49

further analysis, 49–50

specifics, 36

techniques for fraud detection, 35

Actionable Intelligence Technology Financial Investigative Software (AITFIS), 8, 125, 130–134

case study, 132, 134–135

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

fraud analysis, 95–100

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

interactive analytics, 93–95

collaborative analysis, 95

unified data views, 94

visualization, 94

link analysis, 92

Coderre, David, 57, 65

CRISP-DM, 59, 60–64

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

corruption, 52–53

financial skimming, 53

financial statement fraud, 53

Fraud, schematics of, 1–14

detection, 11–12

fraud analytics

defining, 2–6, 12

in its new phase, 6–10

refined, 12–13

using, 10–11

Fraud analytics

analytical process and, 23–32

data analytics, 31–32

designing, 29–31

probabilities of fraud, 28–29

steps in, 24–28

strategies, 27–28

defining, 2–6, 12

evolution of, 15–22

fraud prevention and detection in, 19–20

incentives, pressures, and opportunities, 21

strategies, 20

using fraud analytics, reasons for, 17–19

in its new phase, 6–10

new trends in and tools, 137–149

FICO Insurance Manager 3.3, 145–146

Fiserv's AML Manager, 148

IBM i2 iBase, 146–147

Palantir Tech, 147–148

Raytheon's VisuaLinks Analytics, 143–145

vs. predictive analytics, 57–76

comparing and contrasting predictive modeling and data analysis, 72–75

composite methodology, 70–72

conflicts within methodologies, 69–70

CRISP-DM vs. fraud data analysis, 66–68

methodologies, comparing and contrasting, 60–64

overview, 58–60

SAS/SEMMA vs. fraud data analysis, 68–69

13 Step Score Development vs. fraud analytics, 64–66

refined, 12–13

using, 10–11

G

Garrett, Mollie, 140–141

I

i2 Analyst's Notebook (IBM), 7, 107–125

and fraud analytics, 113–116

fraud and fraudsters, rapid investigation of, 108–109

highlights, 111–113

money-laundering scenario, using in, 121–125

case study 122–124

general process used in, 121

steps to perform, 121–122

using, 116–121

attribute types, 117–118

directional data, 118–119

list items, 119–121

i2 iBase (IBM), 146–147

IDEA Data Analysis Software (CaseWare), 6, 77–89

case study, 80–81

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

purpose, 83, 85–86

stages of using, 87–88

Interactive analytics, 93–95

collaborative analysis, 95

unified data views, 94

visualization, 94

Inventory fraud of operation supply and demand (case study), 35–50

analysis, 36–49

duplicate invoices, 37, 38, 39

even dollar amounts, 45–46

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

further analysis, 49–50

specifics, 36

L

Link analysis, 92

M

Millar, Peter, 331

N

Nigrini, Mark, 83

O

Occupational fraud, impact of, 3

P

Palantir Tech, 147–148

Peterson, Marilyn, 16

Predictive analytics, vs. fraud analytics, 57–76

comparing and contrasting predictive modeling and data analysis, 72–75

composite methodology, 70–72

CRISP-DM vs. fraud data analysis, 66–68

methodologies

comparing and contrasting, 60–64

conflicts within, 69–70

overview, 58–60

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

2010, 51, 141

2012, 3–6

fraud detection, 3–4

impact of occupational fraud, 3

perpetrators of fraud, 5–6

victims of fraud, 4–5

S

SAS Analytics, 8, 127–130

large data volumes, 129–130

model development life cycle (with SEMMA), 60–64

SAS/SEMMA vs. fraud data analysis, 68–69

visualizing big data, 128–129

“South Sea Bubble” scandal, 2

T

13 Step Score Development vs. fraud analytics, 64–66

V

Visual Network Analytics (Centrifuge Systems), 7–8, 87

VisuaLinks (Raytheon), 7, 143–146