Fraud Management Solutions

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Enterprise Fraud Management
B.Radha Krishna
Practice Manager, Risk & Fraud Management
SAS Institute India Pvt. Ltd

Copyright © 2011, SAS Institute Inc. All rights reserved.

Agenda

Business Issues
Key Themes of Enterprise Fraud Management
• Customer Classification
• Detection Methodologies
• Investigation
− Alert Management
− Case Management


Business Issues

REGULATORY EMPHASIS
INDUSTRY DRIVERS
CUSTOMER ISSUES
ANALYST VIEW


Findings of Forensic Security – Guidelines for
prevention of frauds


Working Report on Electronic Banking
Fraud detection
a) Detection of fraud
Despite strong prevention controls aimed at fraud deterrence, fraudsters do manage to perpetrate frauds. In such
cases, the earlier the fraud is detected, the better the chance of recovery of the losses and bringing the culprits to
book. System triggers that throw up exceptional transactions, opening up channels that take note of
customer/employee alerts/disputes, seeding/mystery shopping exercises and encouraging
employees/customers/ well- wishers to report suspicious transactions/behaviours are some of the
techniques that are used for detection of frauds. The exceptional/suspicious transactions/activities reported
through these mechanisms should be investigated in detail.
b) Transaction monitoring

Banks should set up a transaction monitoring unit within the fraud risk management group. The transaction
monitoring team should be responsible for monitoring various types of transactions, especially monitoring of
potential fraud areas, by means of which, early alarms can be triggered. This unit needs to have the expertise to
analyse transactions to detect fraud trends. This unit should work in conjunction with the data warehousing
and analytics team within banks for data extraction, filtering, and sanitisation for transaction analysis for
determining fraud trends. Banks should put in place automated systems for detection of frauds based on
advanced statistical algorithms and fraud detection techniques.
c) Alert generation and redressal mechanisms
Appropriate mechanisms need to be established in banks, to take note of the disputes/exceptions or suspicions
highlighted by various stakeholders including transaction monitoring teams in banks and to investigate them
thoroughly. Banks should have a well publicised whistle blowing mechanism.
Source : RBI Website
Chapter 6 : Cyber Fraud
Working Report on Electronic Banking


Financial Crimes in 21st century

Criminals Financial Institutions
Sophisticated methods Prevention siloed by line of
business
Dynamic attacks
Detection is product or
Hit multiple channels &
channel-specific
products
simultaneously Separate investigation teams
Engage insiders Act on account or customer
Networked via web Rules and models have high
false-positive rates
Attack from remote
jurisdictions Changing payments
landscape: SVC, ACH, Wire,
Exploit unwitting Mobile
customers


Top Business Drivers, Strategies, and
Technology for Fraud and Financial Crimes
Management (2011)- Tower Group
Business Drivers in Financial Crimes Management
Underground fraud Employee fraud Data breaches/ New delivery channels New sourcing
economy is organized, spurred by “Wikileaks” raising open new routes to strategies expand
sophisticated, efficient underground market marketplace and fraud supply chain risk
executive fears
New technology (cloud, Anticipated regulatory New hires and role Technology upgrades Social networks
grid, virtual) brings new changes necessitate changes expose new pushed off for too long expand avenues for
risks computing tech. upgrades vulnerabilities must be addressed fraudsters

FSIs’ Strategic Responses to Reduce Fraud Risks
Manage risk holistically, Manage fraud, Improve data Leverage fraud Merge AML and fraud
including fraud risk security, compliance in governance information for new strategy, technology,
coordinated fashion business opportunities and processes
Standardize security and Adopt enterprise fraud Manage valuation, Use risk-based not Upgrade technology
business process with management with liquidity, counterparty standardized to comply with new
supply chain LOB responsibility risk with eye to fraud approach to fraud regulations

Important Technology Trends in Financial Crimes Management
Layered security and new Converging risk, Develop cloud security Adaptive analytics Enterprise case tool
authentication approaches security, and fraud strategy and vendor with governance, risk
for regulatory compliance platforms short-list mgmt. emphasis
Proactive scanning for new Cross-channel Adaptive life-cycle Visualization tools for Compliance module
patterns in real- time, risk- profiling, security, and monitoring and root cause and early enrichment (red flags,
based mode event coordination adjustments warning SARs, etc.)
Source: TowerGroup


Top 10 Fraud Types
Automated clearing house (ACH) and wire transfer fraud
Attacks on Institution Networks
ATM Skimming
Credit Account ‘Bust-Outs’
Variations on Phishing Schemes
Increasing Check Fraud
Internal Fraud
Mobile Phone Scams
Online Application Fraud
Prepaid Cards

Source: Chartis Research


Enterprise Fraud Management
Key Themes

CUSTOMER CLASSIFICATION
DETECTION METHODOLOGIES
INVESTIGATION
ALERT MANAGEMENT
CASE MANAGEMENT


Enterprise Financial Crimes
Breadth of disciplines as defined by the market
Anti-money Laundering
Compliance

Fraud

Brokerage
Activity Monitoring Cards Broker
Sanctions Blocking Deposit Surveillance
Know Your Payments Trade
Customer/Customer Surveillance
Remote Banking
Due Diligence
Internal
Loans
Rings

Enterprise Investigations Management (Case Management)


Customer Risk Classification

High/Medium/Low Account Score Classify
Opening Weighted
Risk Classifications Questionnaire Responses H/M/L

Transactional and
list-based classifiers Parties Products Services

on monthly basis. • PEPs, NGOs,
MSBs
• Correspondent
Banking
• Wires
• On-line
• Cash Intensive, • Private Banking Banking
Periodic review and etc.

suggestion of new
Risk Classification
for the customer Lists Behavior H/M/L
during Assessment
process.


Detection Methodologies
Using a Hybrid Approach for Fraud Detection
Enterprise Data Suitable for known Suitable for unknown Suitable for complex Suitable for associative
patterns patterns patterns link patterns

Rules Anomaly Predictive Models Social Network
Customer Account Detection Analysis
Rules to filter Detect individual and Predictive assessment Knowledge discovery
fraudulent transactions aggregated abnormal against known fraud through associative
Trans- Appli-
and behaviors patterns cases link analysis
action cations
Examples: Example: Example: Example:

Internal • Mort. payments from • ACH transactions on • Like credit / debit • Association to known
Employee different accounts account exceed norm transaction patterns fraud
Bad Lists
• Check serial # out of • # unsecured loans on • Like account opening • Identity manipulation
range network exceed norm & closure patterns
• Transactions to
3rd Party Call
Flags Center • Card order follows • Check velocity • Like network growth suspicious
Logs address change exceeds norm rate (velocity) counterparties
• New ACH payee

Hybrid Approach
combination of all 4 approaches at account, customer, and network levels


Risk Ranking Alerts

History
Risk Score
Factor

Scenario

Alerts
Application + Activities
Scoring Duration = length of time that one alert should be considered when scoring a possible future alert

Execution Probability Rate Risk Ranking Bayes Weight

•The number of distinct •Ranks alerts based on the •The percentage of scenario
entities to match a scenario or scenario(s) that are matched, suspects that would engage
risk factor during the scoring any risk factors that apply to in the behaviour. The most
duration divided by the total the same entity, as well as common scenario schemes
number of entities of the any scenarios and risk factors should be assigned a value of
appropriate subject matched within the scoring 10, and rare schemes should
duration. be assigned a value of 1.


Scenarios and Risk Factors in Practice

Customers
Accounts
Transactions
Filtering

Alert with low risk score
Customers Alert with medium risk score
Accounts Possible alert
Transactions
Alert with high risk score

Customers
Accounts
Transactions = scenario/ risk factor hits


Effective Fraud Management Program
Key Drivers
Data Quality
Integrated view of relationship
Hybrid Detection techniques
Case Management


SAS Enterprise Financial Crimes Platform
Integrated Analytics & Case Investigation – Our Competitive Differentiators

Product
Enterprise
SAS Fraud SAS Fraud Anti-Money
Case
Management Framework Laundering
Management

Target
Banking Banking
Industry
Banking
Government Government

Banking Insurance Insurance

Health Care Health Care
Insurance
Telco Other


SAS Enterprise Financial Crimes Framework
Insurance
Agent, Call Center Company

Open Update Close
Manage
Claim Claim Claim

Simple Case
Call Center, Company

Or SIU
Complex Case
SAS Fraud Framework

Data Management
SIU
Modeling

Detection

Alert & Case Initiation

Investigation


Q&A


make connections • share ideas • be inspired
India’s Largest Analytics Forum

Thank You
B.Radha Krishna
radhakrishna.b@sas.com


Fraud Management Solutions

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