When the seconds count: How Analytics focused on security ...€¦ · Application Fraud Closer look at Synthetic Identities •Hard to measure the frequency of synthetic ID fraud

When the seconds count: How Analytics focused

on security improves defenses against Financial Crimes

Eric Herson, Principal Industry Consultant

SAS

Key talk points for today:

New ways to think about the linkage between Cyber and Fraud

This session will focus on the Industry Trends that have led to the global increase in both Cyber and Fraud

attacks.

How are Cyber and Fraud linked?

How do you protect your organization from Unknown Risks?

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SAS Fraud & Security Intelligence (Commercial) Cross Industry Fraud & Cyber Analytics

• AML Compliance

• AML Transaction Monitoring

• Customer Due Diligence

• Financial Crimes Optimization

• FCIU

• Fraud in Banking

• Cards/ACH/Wire Fraud

• Online Fraud

• Application Fraud

• Insider Threat / Banker Supervision

• Healthcare Cost Containment

• Medicaid Fraud

• Application Fraud

• Insurance Fraud

• Claims

• Application/Agent Fraud

• Procurement Integrity

• Government

• Tax Compliance

• Rx Drug and Opioid Abuse

• Child Well Being

• Law Enforcement Intelligence Management

• Benefits Fraud

• Emerging Technologies

• Visual Investigator

• Adaptive Learning & Intelligent Agent System (ALIAS)

Themes Machine Learning & Artificial Intelligence Analytic Impact (Operationalizing Analytics)Intelligent Case Management Decision Latency & Real-timeCyber Intelligence


Agenda

The changing face of Global Fraud

How Cyber is connected to Fraud?

• Trends: What are we seeing?

• Solutions: Where is the Industry going?

- Highlight: Synthetic Identities

- Real Time Fraud Decisioning Hub

- Insider Threat Detection

We Live in interesting timesGoal: Share Trends we are seeing Globally

Explore: The link between Cyber Events and Fraud

A Practitioner's Perspective. Industry Best Practices & Use Cases

Yes, the headlines are true. Exponential Growth in

Financial Crimes across all Industries

Swift presses banks on security as more hacks surface


SAS: (We know why we are here today…)

2017: Colombia accounted for 8% of cyberattacks carried out in

Latin America this year, with losses estimated at US$6.18bn (Business News Americas)

“Colombian economy suffered from hundreds of millions of dollars lost due to cyber-

crime. Cyber crime affects up to six million Colombians annually…Fraudsters have used

Business Email Compromise (BEC) platforms to target organizations. These are

organizations that deal with international suppliers who pay for their trade using

international electronic transfers..” (2017 DarkNetMarkets)

The Virus or the Hack was just a distraction. Their Prize was the Cash.

Colombia was the only country ahead of the UK in

terms of reported fraud and cyber security

incidents, with 95% of Colombian executives saying

they were affected by both crimes Kroll

International 2017


Comfortable? Complacent? Or, not feeling well?

34% of respondents say they have high confidence in their organization’s ability to detect and prevent (Cyber) and Fraud before it results in serious business impact.

56% of respondents say today’s fraud schemes are too sophisticated and evolve too quickly

45% of respondents say their current systems allow for only limited analytics

Source: ISMG, 2016 Faces of Fraud: The Analytics Approach to Fraud Detection, August 2016


Is it Cyber or is it Fraud?The Evolving Face of Fraud

“Cyber-attack is any type of offensive maneuver employed by individuals or whole

organizations that targets computer information systems, infrastructures, computer

networks, and/or personal computer devices by various means of malicious acts

usually originating from an anonymous source that either steals, alters, or destroys a

specified target by hacking into a susceptible system.” – Wikipedia 2018


The 83 Day Problem

Cyber Analytics focus on intraday / near real time “machine to machine” peer

grouping and comparisons. There are no “good versus bad” outcomes. Cyber

Analytics are looking for outliers and anomalies.

Fraud Analytics are developed off of historical data to predict and identify

fraudulent events in real time in the transaction stream. Fraud Models predict

behavior based upon known “good versus bad” outcomes of customers,

accounts, and transactions

Target of Cyber attacks: Information, Credentials, IP,

and of course Money!…

Defining Cyber

How does SAS Define Fraud?

Intersection of Cyber & Fraud

Cyber versus Fraud:

If you lose money, IP, data,

or reputation…. the labels

don’t matter


Current Risk-Based Approach to Cyber SecurityWhere Is the Focus?

Reconnaissance

(1)

Weaponization

(2)

Exploitation

(4)

Installation

(5)

Command & Control

(6)

Actions on Objective

(7)

Indicators of AttackDetect & Analyze

Compromise Indicator (IoC)Contain, Eradicate & Recover

Delivery

(3)

Cyber Kill Chain® courtesy Lockheed-Martin

•Most security operations are reactively focused on Indicators of Compromise

• Most security operations are reactive and focused on Indicators of Compromise. By that time the damage is done!

• Tactics, techniques, procedures are ever-changing. Organizations must analyze behavioral activity across multiple data dimensions to find the intrusions.


Diversity Scale

Trust

Challenges obtaining, managing & acting on security insights driven by issues of• Diversity: Context & data• Scale: Data, resources & systems• Trust: Analytics, results, risk view• Messy Data

Detecting Cyber is Messy


How is this being solved? The move from Reactive to Proactive Cyber Security

Requires Multidimensional, Data-Driven Insights

• Data enriched prior to detection

• Behavior simultaneously triangulated across key dimensions

• Contextualized events to streamline & optimize response

= An approach that surfaces Cyber Anomalies faster with lower False Positives

Threat

App IAM

EndpointNetwork


“Chicken and Egg” Question

What came first? Cyber or Fraud?


Linking Cyber to Fraud and Economic Loss

What do the following have in common?

• Insider Collusion

• $5.00 USD Router

• Spell Check: ‘foundation’ as ‘fandation’

• Casino Chips as “Stored Value Devices”

• “Rinse, Wash, Repeat” everyday, across the Globe

• Horrendous Loss of Reputation

Answer: The Bank of Bangladesh”

- $81 Million gone in less than 24 hours”

- The Cyber Fraud Cycle

Eventually: “It’s all about

the money”


Cyber leading to Fraud: We are all (potential) victims

Implications:• Entire US population advised to “freeze credit”• Life Time Exposure / Can’t run from the Dark Web

(Official Site was jacked into a Phishing site = more compromised accounts)

LATAM facing similar threats!


How do hackers compromise organizations?

All Cyber begins with a “compromise event”

And, how does this lead to “Business Email Compromise?

Setting the Stage

https://www.youtube.com/watch?v=F78UdORll-Q

https://www.youtube.com/watch?v=F78UdORll-Q


How do our clients get into this mess? Or, “this could never happen to me….”

2 NE_kick-off_vishing_long.mp4

<<<6 min video>>>

2 NE_kick-off_vishing_long.mp4


Cyber opens the door to Fraud Attacks

A closer look at Industry Trends

The Long Road to Now. How did we get here?


(Cyber) Fraudsters at work. A Real Time View

Don’t show this to your Boss. They won’t sleep either.

http://map.norsecorp.com/#/

Norse Attack Map

http://map.norsecorp.com/

http://map.norsecorp.com/


Fraud is Evolving: From “physical” to “digital”Globally changing the face of Fraud / Shifting Definitions

Payments: Any Electronically enabled Transaction

• Growing digital wallet choices

• 2018: Estimated about 30% of payments to be part of mobile, digital, and virtual currency

• “When was the last time you went into a Bank Branch?”

North America Europe Mature APAC Latin America CEMEA Emerging Asia

Cards Direct Debit Credit Transfer Checks

Source: https://www.worldpaymentsreport.com/reports/noncash_payments

What’s driving the increase in Fraud?


Payments: Increased Digital Account Opening ripe for App Fraud

A Growing Trend

***2014 – Only 12% of customers used digital channels to open accountsEnormous jump in 2017 & 2018 opens the door for Synthetic Identities leading to App Fraud

Co mpany Co nfidentia l – Fo r Inter nal Use OnlyCopyright © SAS Inst i tute Inc . Al l r ights reserved.

Financial Services Payment Fraud: “The threat is persistent, adaptive and sophisticated and it is here to stay.“*

• How are clients being impacted?

- Common for banks to see Payment fraud losses of 3 to 6% +

- “Fraud Is Now approx. $11 Of Every $100 of Digital Sales” (Payments.com. November 2016) .

- 2017 # predicted to be closer to $13

*Finextra, Aug 31 2016

Hacking / Cyber – these are the effects


Tipping Point with EMV

24

With the move to EMV or "chip cards" in the U.S. it’s

becoming harder to commit fraud at the point of sale.”

Impacts of EMV Reduced POS Fraud

Rise in CNP (Card Not Present)

Rise in older fraud types

Increased Fraud Applications and Account Takeover (ATO)

Counterfeit

Type of Fraud

TNFATO

Credit Losses

How did we get here? (US Centric View)

Criminals are very, very clever. Fraud is never static. And, will evolve & exploit the weakest link in your defenses.

“We have met the enemy and it is us”

Co pyright © SAS Inst i tute Inc . A l l r ights reser ved.

2018 Fraud Heat Map / Customer Survey

Synthetic ID

ATO

CFT

True Name Fraud

1st

L/S

Wire NSF

CNP

Net Loss

Freq

uen

cy

NRI

Bubbles: Complexity of case

The changing face of Fraud:

Synthetic Identities

Cyber Attacks opening the door to Fraud Attacks


• Synthetic identities are a combination of fabricated credentials

• The implied identity is not associated with a real person

• Unlike identity theft or manipulation, where the core identity of a person is impersonated or manipulated by a fraudster, a synthetic identity is an artificial identity with no particular person behind it

What are Synthetic Identities?

9 year old’s Identity tied to decreased person’s identity

Cyber Attacks & Fraud


Application Fraud Closer look at Synthetic Identities

• Hard to measure the frequency of synthetic ID fraud as there really is no consumer “victim”

• Gartner, Inc. estimates synthetic schemes constitute at least 20% of credit charge-offs and 80% of losses from credit card fraud.

• 88.3% of identity fraud is from synthetic Fraud = 73.8% of total dollars lost

• FTC – synthetic ID theft accounts for nearly 85% of the more than 16 million ID thefts each year.

• More than 1 in 4 NAF victims is between the ages of 18 and 24.

• Children provide even more valuable IDs

No real human experienced the loss…No one calls to complain…

Companies have no way of contacting “victim”

New account fraud allows perpetrator to control the fraud from initiation (Cultivate the account.)


App and Synthetic ID Fraud is a Cross Industry Issue

Retail –Unsecured LinesWhere no collateral put up and risk is base, personal trade line of credit, etc.

Retail -Secured LinesCollateral put up against value of loan, such as a house, car, etc.

Commercial Business based loans, can also include trade finance – of which SAS has targeted message

Telco/Utility eg. Phone provided on Postpaid plans based on customer credit history or determined risk

Goverment Programs & Benefits fraudsters increasing targeting of govt as fraud detection is much lower

Insurance -General, Life, HealthManipulation of information and/or identify to receive coverage, reduce premium, etc.

False or manipulated information provided to yield a more positive outcome

Not just a Bank problem. Seeing huge increase in Fraud in Government, Telco, & Insurance


What’s Needed? How do you fight Application Fraud?

Cyber Opens the Door… Fraud Detection Closes it…


Synthetic Identities and Application Fraud Three components of application fraud detection

• Identity Verification Analytics

• Confirms that an applicant’s details match historical records.

• Device Verification Analytics

• Analyzing and comparing device information to past experiences and the information provided by the applicant in the application.

• Identity Authentication Analytics

• Ensures that applicants are who they claim to be

Where are our clients taking us?

How do you prevent this type of Fraud?

Impossible to do this manually….

Perfect Application for AI and Machine

Learning Need Software and Analytics to Solve and Resolve


Synthetic Identities and Application Fraud

Software to find: Proof of Life• Confirmation of DL – 30x more

likely to be fraud if no DL associated with ID

• Single credit bureau – Identity identified in one credit bureau only have 10x greater chance of being synthetic

• Multiple addresses associated with single SSN / Identity # –2/20%, 4/60%

• Synthetics have higher credit shopping rates AND lower verification rates

• 73% of synthetic IDs have no family relationships in data

Our clients want to bring different data together: Process

Automation

Leading Indicators of a problem

Implement Software and Analytics to find

these anomalies


Hybrid Scoring Approach for App Fraud

Alert Generation

Process

Database Searches

Text Mining

Predictive Modeling

Anomaly Detection

Automated Business Rules

Levels Of Detection

Event

Entity

Network

Anomaly Detection for App Fraud requires a combination of Tools

C op yr i g h t © 2014 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .

APPLICATION & SYNTHETIC FRAUD CASE

• An application fraud case derived from a real case• The applicant imposes high risk due to network association and application history • Collusive fraud ring including both a known fraudster and third-party unknown individual• Fraudsters utilized different synthetic ID manipulation to bypass existing detection technology

How do you find “Proof of Life?”

“Degrees of Association”


Case Study – Australian Bank POV resultsNumbers and Beyond

5Kcontact phone numbers reference a Casino

Fraudsters flying below the radar. High volume, low value applications

“Bust out” risk

2.5Kcontact phone numbers reference same branch

1.4Kcontact phone numbers reference a slaughterhouse (abattoir)

60Kcontact phone numbers reference immigration agent

$3mPer month, more at risk applications found by Analytics than existing approach (CC and PL)4x

more application fraud found by Analytics than existing approach 2.5x

Faster investigation


Real Time Fraud Detection

Blocking Cyber: The Need for Real Time Fraud Decisioing

Cyber opens the door. Fraud Detection closes it…

Co pyright © SAS Inst i tute Inc . A l l r ights reser ved.

Enabling Technologies for Fraud

Gartner: Fraud Loss = “Authentication problem, not a Fraud Problem”

Problem Statements: • How do I easily bring in 3rd Party Data and insights into my Fraud Platform?• How do I map in new data sources without the need for extensive IT time & resources?• How do I launch and protect new products, channels, payment vehicles? • How do I have “end to end” contextual awareness?

• “Future proofing” Fraud Platforms

Overcoming the biggest Implementation Challenge. Software utilities that allows for simplified Data Mapping now and two years from now

Company Confidential - For Internal Use OnlyCopyright © 2016, SAS Institute Inc. All r ights reserved.37

Empowering“Contextual Awareness”

Step One: Simplifying Data Integration for Cyber and Fraud

Event - The need to “Score” everythingEverything is a transaction.

“Traffic Cop” intelligently feeding authentication, transactions, events, data into a Fraud

Decisioning Platform

Transform Convert any incoming customer transaction into a format consumable

Enrichment Add additional information to the transaction

Plug in any 3rd Party Data: Session, IP, Log in, Bio-metrics, Device ID, Proof of Life, Bureau, Scores, Flags, Reason Codes….

xt to the eve

Reduces Reliance on Static Data

Increase Reliance on Authentication and Dynamic Data


To Fight you need a: Fraud Decisioning Hub

Real Time Decision Engine

Need a Solution that addresses the 3 Pillars of Fraud: Prevention, Detection, and Resolution

What attributes are required for Fraud Control?


Fraud Detection and Machine Learning

(Who knew that Machines can learn? Answer: Fraud vendors did over 20 years ago)


Provides a holistic picture of a customer by looking across all their activity

• Information must be maintained at multiple entity levels - keeps track of the behaviors of different entities (card, account, terminal, device, address, user-ids, beneficiary)

• Can seamlessly integrate an expanding domain of new data types, sources and models

• Provides a mechanism to convert customer behavior into inputs for the model

• Enables the model to adaptively learn the customer’s (changing) behavior

The power by Analytics to manage every transaction as ‘individual”

These techniques finds Fraud at the first transaction

PROFILES,

SIGNATURES,

PATTERN

RECOGNITION

AKA MACHINE

LEARNING

In Real Time, using

layers of data or

intelligence to look for

anomalies &

evidence of Cyber

Need: Analytics Learn and Understand Customer Behavior


NEED: USE “NON MONETARY” EVENTS IN FRAUD DETECTION

• Non-monetary activities, e.g.

• Correspondence change: Address/phone/email change

• Daily limits change

• Online/Phone banking: logon, password change, secret QA change

• Any Change in Credentials :

• Master files

• Account, customer etc.

Non Mon and Monetary

Transactions influence Signatures.

Machine Learning Analytics learns and

understands your customer’s behavior

“Evidence of Cyber

Manipulation”


Cyber Attack / Fraud Case Study

Domestic and International Wire Fraud / Monitoring SWIFT Messages

Blocking SWIFT Transactions in Real Time


CASE STUDY TIER I US BANK

CEO Wire Fraud aka “Business Email

Compromise” / Cyber Hacking

• Situation (Circa 2013)

• “Comerica Effect”

• 1 in 3 US households served by bank

• Customer experience/retention risk of Commercial clients

• Reputation risk > Wall Street Journal & Wall Street

Experi-Metal, Inc., v. Comerica Bank (Docket Number:

2:2009cv14890) is a decision by the United States District

Court for the Eastern District of Michigan in a case of a

phishing attack that resulted in unauthorized wire

transfers of US$1.9 million through Experi-Metal's online

banking accounts. The court held Comerica liable for

losses of US$560,000 that could not be recovered

from the phishing attack, on the ground that the bank

had not acted in good faith when it failed to recognize

the transfers as fraudulent.“Wholesale payments, on the other hand, tend to be large in value

per transaction and in comparison with retail payments relatively

small in terms of the number of transactions generated daily.”

https://en.wikipedia.org/wiki/United_States_District_Court_for_the_Eastern_District_of_Michigan

https://en.wikipedia.org/wiki/Phishing


Focus: Commercial Payments (Wires and ACH) Fraud

Analytics that catch and block “Black Swans”?

Rare Event Fraud Monitoring:

• Low Fraud Incidence but potential of high losses

• Reputational Risk

Analytic Approach: Unsupervised Analytics

Analytics where there aren’t enough “bads” to develop or train a suite of neural net models• Goal: Find all of the “Good Patterns”

• Build a profile of what’s acceptable (Regular Behavior)• Machine Learning analytics to classify and ID the Outliers• More abnormal the pattern, higher the risk, higher the score

Operationalizing Fraud Analytics: Real Time, in the decision steam Wire Fraud detection and scoring: Block, Refer, Investigate

• Decision Rules that measure transaction aggregation and velocity by entity, country, and,…

• Analysis of “non mons”: all customer requested changes• Monitoring: Channel Data (Logon, Session, Device)

Stopping Wire Fraud in Real Time

Designed to monitor the bank’s “Wire Room” and sniff out Cyber Attacks


Focus: Commercial Payments (Wires and ACH) FraudRare Event Fraud Monitoring

Multi Entity Signatures:

Remembers Behavior of

relationships (accounts,

payee payer

relationships)

Highly Accurate in looking for Cyber penetration Anomalies

• Higher Detection Rates, often at the First Transaction• Lower False Positives = Enhanced Customer Experience• Protects Reputational Risk while stemming Economic Loss

• Results• Ability to suspend batches and payments intraday and avoid

“claw back” process

• Detection of fraud improved by over 70% (VDR)

• 4X improvement in Detection / with Lower False Positives

Cyber attacks may have opened the door. Fraud Solutions closed it….!


New

Uncovering: Internal Collusion & Insider Threat. Cyber often involves someone on the inside


Insider Threat, Employee Supervision and Sales Practices Oversight

Global Retail banking industry is under intense scrutiny• Sales Practices in retail banking has

become a highly visible issue in the eyes of regulators and retail financial consumers

• High profile, pervasive violations have led to major fines and long-lasting reputational damage

• Sales practices are now a Board-level issue at all major banks

• Regulators will now hold the Board of Directors accountable for pro-active monitoring of their sales force and incentive structure

• Failure to meet standards can result in prohibitions from asset growth

Industry participants are aware that their current surveillance operating model is not fit for purpose, forcing strategic platform reviews over the next 6 – 18 months.

The OCC sent formal letters to banks under its supervision including J.P. Morgan Chase & Co., Bank of America Corp. and Citigroup Inc. seeking information about sales practices and incentive-compensation structures


Market Growth vs Peers

Wells Fargo growth has significantly lagged competitors since initial consent order. By a difference of 50-90%

Wells Fargo – Case Study


Surfacing Insider Threats

VulnerableClient Abuse

Credit Abuse, App Fraud and

Collusive Fraud

ImproperSales

Practices

Bribery Corruption &Procurement

Fraud

Money Laundering

Payroll Fraud

Data (IP/PII) Theft

Rogue Trading,MarketAbuse

Analytics to monitor Employee Behavior / “Ignore the good and find the bad”

RulesSet-up rules to filter abusive behavior

Advanced AnalyticsKnowledge discovery, data mining, predictive assessment

Anomaly DetectionDetect individual and aggregate abnormal patterns

Social Network AnalysisPerform knowledge discovery through linkage analysis

Text Mining & AnalyticsExtract suspicious patterns, context & sentiment in the unstructured data

Requires a multi-tier hybrid approach to deliver more effective and expansive detection

Increase Abuse Detection

ReduceFalse

Positives

Banker Supervisionfocus area

Proactive behavior-based detection Cyber often involves someone on the inside


The future of Sales Practices & Insider Threat monitoringMoving beyond retail bankers to other lines of business

• Detect and prevent improper sales practices• Fake accounts

• Abusive sales

• Procedural / legal deficiencies

• Reduce internal fraud• Unauthorized account

access

• Theft and improper fees

Banker SupervisionFA & Broker Monitoring

Cap. Market AbuseInsurance Agent

GamingRetail Banking Wealth Management Capital Markets P&C Insurance

• Detect and prevent improper financial advise• Suitability / fiduciary

duty requirements

• Conflicts of interest

• Portfolio drift

• Reduce FA / broker misconduct• Unauthorized trading

• Registration & licensing

• Detect and prevent improper trading activity• Market manipulation

(spoofing, front-running, etc)

• Insider trading and collusion

• Reduce trader misconduct• Rogue trading

• Client fraud

• Tax offenses

• Detect and prevent manipulative insurance agent behavior• Agent gaming /

commission manipulation

• Application fraud

• Reduce loss due to fraud• Premium and claim

leakage


What is Text Mining….? Turning Natural Language into Data

• Image Recognition

• Proper Noun extraction

• Emails and Voicemails into text

• Entity Resolution

Another way to ID employees that are associated with your customers or known “bad actors”

Text Analytics on:

Email

Vmail

Web and Session Monitoring


Text Analytics of E-Communication ActivityEmployee Alert Details

User Activity Drill Down and Peer Comparison

Internal Threat Alert Dashboard

Finding Internal Fraud requires Smart Workflow to harness Analytics


Internal Fraud Case StudyQuickly Identify degrees of Association with “bad actors”

Clients have been able to uncover Fraud Rings prior to “bust out” and economic loss

Reputation Protection

“$500,000 loss last week ultimately traced to call center employees”

“14 Delinquent Mortgages linked to the same Attorney, employee, and…”

“Seeing same mobile # dozens of times”

“Low and slow forensic investigative tools”


Before we go….

New ways to think about the linkage between Cyber and Fraud

This session will focus on the Industry Trends that have led to the global increase in both Cyber and Fraud

attacks.

How are Cyber and Fraud linked?

How do you protect your organization from Unknown Risks?

Company Confidential - For Internal Use OnlyCopyright © 2016, SAS Institute Inc. All r ights reserved.

Nigerian Astronaut stuck in Space and wants to go home*

FRAUDSTERS ARE VERY CREATIVE

“Dear Mr. Sir,

REQUEST FOR ASSISTANCE-STRICTLY CONFIDENTIAL

I am Dr. Bakare Tunde, the cousin of Nigerian Astronaut, Air Force Major Abacha Tunde. He was the first African in space when he made a secret flight to the Salyut 6 space station in 1979. He was on a later Soviet spaceflight, Soyuz T-16Z to the secret Soviet military space station Salyut 8T in 1989. He was stranded there in 1990 when the Soviet Union was dissolved. His other Soviet crew members returned to earth on the Soyuz T-16Z, but his place was taken up by return cargo. There have been occasional Progrez supply flights to keep him going since that time. He is in good humor, but wants to come home.

In the 14-years since he has been on the station, he has accumulated flight pay and interest amounting to almost $ 15,000,000 American Dollars. This is held in a trust at the Lagos National Savings and Trust Association. If we can obtain access to this money, we can place a down payment with the Russian Space Authorities for a Soyuz return flight to bring him back to Earth. I am told this will cost $ 3,000,000 American Dollars. In order to access the his trust fund we need your assistance.“

[email protected]

Thank you!

Documents

When the seconds count: How Analytics focused on security ...€¦ · Application Fraud Closer look at Synthetic Identities •Hard to measure the frequency of synthetic ID fraud