Rohan Langley

SAS

How to Catch a Thief Trends & Technologies in the Fight Against Fraud

Fixing Fraud: A Fraud Solution

Global Drivers & Challenges: The Changing Fraud Landscape

A Real World Example: Online Banking Fraud

The Changing

Fraud Landscape

Global Drivers & Challenges

Fraud is a global problem

Source: Association of Certified Fraud Examiners • Typical organisations lose 5% of revenues

to fraud each year

• Estimated to be a global loss of more than $3.5 trillion

• Industries most commonly targeted • Banking & Financial Services • Government and Public Administration • Manufacturing Sector

Tough challenges for organisations

Organisations are faced with tough challenges to focus on reducing losses AND ensure a smooth customer journey AND take effective, timely action.

“Cyber thieves have cost US companies and their banks more than $15 billion in the past five years, the FDIC found in a recent study.” Financial Times, 2012

“Tax evasion losses are estimated at $3.1 trillion for 145 countries in the world which represent 98% of the world GDP between them.” The Tax Justice Network

HSBC to pay $1.9bn in US money laundering penalties

Standard Chartered to pay $340m settlement over Iran investigation

RBS and HSBC among banks fined £2.6bn for forex rigging

BNP Paribas to pay $9bn to settle sanctions violations

VAT fraud costing Europe €€50bn a year Carousel scam escalates out of control as governments are accused of denial

Global trade in phantom cargoes swindles banks of £500m

Today’s frauds are

• Increasingly sophisticated

• Higher velocity/faster

• Cross industry

• Multi channel

• Advanced technologies

• Social with pressures on staff

Current fraud systems

are struggling to cope

• Siloed by line of business - no sharing of data

• Act on event or customer

• Rules and predictive models alone have limitations

• Few proactive steps taken to combat cross channel fraud

• Evidence insufficient to act upon

• Investigation time consuming

Fixing Fraud A Fraud Solution

Fixing Fraud How to detect staff behaving badly

Avivah Litan, VP Gartner

"Security and fraud risk exposure is increasing as organizations are threatened at multiple points of vulnerability.

Companies are re-evaluating how they tackle security since a fragmented approach is consistently leaving organizations at greater risk of attack.

A more holistic approach to security ensures all layers of protection function together.“

Protection across the enterprise

Make better use of your data

Network

Entity

Event

Hybrid analytics

SAS Fraud Framework

The fraud threat landscape

Hybrid analytics

Visual scenario configuration

Ad-hoc, in memory reporting and exploration

Concise and efficient investigation

Visualise – empower the business user

A Real World Example

Online Banking Fraud

• Phishing

• It usually starts through Phishing.

• Spear Phishing is where the attack is tailored specifically for you.

• Politicians, business people and other wealthy or eminent people are likely targets of Spear Phishing.

• The term can also describe attacks tailored for the customers of one particular bank.

• Social Engineering (“a trustworthy voice on a phone”) can also be used to extract information from you and convince you to accept an email.

Online Banking Fraud

• Money Mules are • New accounts specifically opened for exiting stolen

funds.

• Sometimes created using fake identification.

• Sometimes ordinary customers co-opted by criminal gangs in return for commission.

• By recruiting many different mules, fraudsters can split transactions into smaller parts, often circumventing fraud rules.

• Mule recruitment became very popular in the aftermath of the financial crisis, recruitment scams are particularly effective on people in financial distress.

- An example of a mule recruitment scam

Money Mules

Man in the Browser Attack

• It all starts with an email or a link to download a piece of malicious software

• Once a customer’s computer or smartphone is compromised, it can harvest passwords and deliver fake bank websites and text messages to the customer.

• Often coordinated to hit many customers at the same time.

Customer contracts Malware

Customer commences

Online Banking session

Passwords 2FA PIN

Customer keeps using fake session

Criminal takes over real session

Money transferred to mule account at same bank

Criminal

Customer

Bank SMS

Anomaly detection (example):

The client is accessing their account from a

new channel

Database Searches (example): Looking for

matches across the Black-lists

Business rule (example): Transaction above $xx to a

new beneficiary

Database Searches (example): Looking for

matches across the Black-lists

Predictive modelling (example): Model based on variables such payment amount and balance

SNA (example): Links to mule account such as a shared mobile number

Text mining (example): Transaction narrative showing suspicious

payments

Using the Hybrid Approach

Questions & Answers

For further information

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