Index

A

ACL Analytics (ACL Services Ltd), 6, 3355

anomalies, 5055

ad hoc, 52

fraud detection in financial crimes and banking, 5253

six-step plan, 5455

inventory fraud of operation supply and demand (case study), 3550

analysis, 3649

further analysis, 4950

specifics, 36

techniques for fraud detection, 35

Actionable Intelligence Technology Financial Investigative Software (AITFIS), 8, 125, 130134

case study, 132, 134135

federal agencies using, 134

hierarchy/analytical chart, 133

AML Manager (Fiserv), 148

Anti-counterfeiting, challenge with, 93

Association of Certified Fraud Examiners (ACFE), 36, 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), 36

key findings and highlights of, 36

B

Bank fraud, challenge with, 95

case study, 97

Benford's Law analysis, 41,4345, 83

graph, 84

C

CaseWare. See IDEA Data Analysis Software

Centrifuge Analytics, 91105. See also Visual Network Analytics

anti-counterfeiting, challenge with, 93

bank fraud, challenge with, 95

case study, 97

fraud analysis, 95100

advanced link analysis and identity visualization, 99100

data preparation and connectivity, 95, 98

fraud management process, 100105

investigative analysis using data visualization, 102

investigative analytics, 103105

interactive analytics, 9395

collaborative analysis, 95

unified data views, 94

visualization, 94

link analysis, 92

Coderre, David, 57, 65

CRISP-DM, 59, 6064

vs. fraud data analysis, 6668

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, 145146

Financial crimes and banking, fraud detection in, 5253

cash transactions, 53

check tampering, 52

corruption, 5253

financial skimming, 53

financial statement fraud, 53

Fraud, schematics of, 1–14

detection, 1112

fraud analytics

defining, 26, 12

in its new phase, 610

refined, 1213

using, 1011

Fraud analytics

analytical process and, 2332

data analytics, 3132

designing, 2931

probabilities of fraud, 2829

steps in, 2428

strategies, 2728

defining, 26, 12

evolution of, 1522

fraud prevention and detection in, 1920

incentives, pressures, and opportunities, 21

strategies, 20

using fraud analytics, reasons for, 1719

in its new phase, 610

new trends in and tools, 137149

FICO Insurance Manager 3.3, 145146

Fiserv's AML Manager, 148

IBM i2 iBase, 146147

Palantir Tech, 147148

Raytheon's VisuaLinks Analytics, 143145

vs. predictive analytics, 5776

comparing and contrasting predictive modeling and data analysis, 7275

composite methodology, 7072

conflicts within methodologies, 6970

CRISP-DM vs. fraud data analysis, 6668

methodologies, comparing and contrasting, 6064

overview, 5860

SAS/SEMMA vs. fraud data analysis, 6869

13 Step Score Development vs. fraud analytics, 6466

refined, 1213

using, 1011

G

Garrett, Mollie, 140141

I

i2 Analyst's Notebook (IBM), 7, 107125

and fraud analytics, 113116

fraud and fraudsters, rapid investigation of, 108109

highlights, 111113

money-laundering scenario, using in, 121125

case study 122124

general process used in, 121

steps to perform, 121122

using, 116121

attribute types, 117118

directional data, 118119

list items, 119121

i2 iBase (IBM), 146147

IDEA Data Analysis Software (CaseWare), 6, 7789

case study, 8081

correlation, trend analysis, and time series analysis, 83

detecting fraud with, 79

fraud analysis points with, 8283

purchase fraud of employee as vendor, 8687

purpose, 83, 8586

stages of using, 8788

Interactive analytics, 9395

collaborative analysis, 95

unified data views, 94

visualization, 94

Inventory fraud of operation supply and demand (case study), 3550

analysis, 3649

duplicate invoices, 37, 38, 39

even dollar amounts, 4546

invalid product codes, 3739, 40, 41

invoices billed on weekends, 39, 41, 4344

quantity-on-hand vs. quantity-on-order analysis, 4649

unavailable, backordered, or deleted product codes, 39

further analysis, 4950

specifics, 36

L

Link analysis, 92

M

Millar, Peter, 331

N

Nigrini, Mark, 83

O

Occupational fraud, impact of, 3

P

Palantir Tech, 147148

Peterson, Marilyn, 16

Predictive analytics, vs. fraud analytics, 5776

comparing and contrasting predictive modeling and data analysis, 7275

composite methodology, 7072

CRISP-DM vs. fraud data analysis, 6668

methodologies

comparing and contrasting, 6064

conflicts within, 6970

overview, 5860

SAS/SEMMA vs. fraud data analysis, 6869

13 Step Score Development vs. fraud analytics, 6466

R

Ratley, James D., 3

Raytheon. See VisuaLinks

Relationship graphs, 100

Report to the Nations on Occupational Fraud and Abuse

2010, 51, 141

2012, 36

fraud detection, 34

impact of occupational fraud, 3

perpetrators of fraud, 56

victims of fraud, 45

S

SAS Analytics, 8, 127130

large data volumes, 129130

model development life cycle (with SEMMA), 6064

SAS/SEMMA vs. fraud data analysis, 6869

visualizing big data, 128129

“South Sea Bubble” scandal, 2

T

13 Step Score Development vs. fraud analytics, 6466

V

Visual Network Analytics (Centrifuge Systems), 78, 87

VisuaLinks (Raytheon), 7, 143146

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