Preventing Fraud with a Multi-Channel Approach

December 6, 2017

Preventing Fraud with a Multi-Channel Approach

© 2017 Guardian Analytics. All Rights Reserved – Confidential and Proprietary. 2

Jesus OrtizVP Product Development

Margarete MuckerEVP, Operations

Karen WebsterCEO

Preventing Fraud with a Multi-Channel Approach

Guardian Analytics Presents

Featuring:


• Founded in 1956

• Headquartered in Silicon Valley, CA

• More than $9 billion in assets

• 100,000 Members

• Ranked 10th in total assets among 6,500 credit unions nationally

• Ranked #1 by the Credit Union National Association for the past 4 years for member giveback

About Star One Credit Union


The year 2006 brought a dramatic change in how FIs and technology companies could work together to reduce fraud.

Emerging Fraud & New Requirements

Emerging fraud

FFIEC MFA guidelines

Member protection

Rules can’t compete

MFA doesn’t stop fraud

Machine learning offers true protection

Star One’s Perspective Guardian Analytics’ Perspective


• We see a wide range of online attacks

• You never know which tactic a criminal will use

• Criminals can get past controls

• You may not know you have “fraud”

• Not all online account takeovers end in an unauthorized online transaction

• Cross-channel fraud starts online

• It’s not a matter of if, but when

Star One’s Perspective on Threats


Star One’s Attitude on Fraud Prevention

• Starting with payment fraud is too late

• We must protect our members from themselves

• Looking at behaviors, not transactions, offers true protection

Results from a Decade of Behavioral Analytics-Driven Fraud Detection

Case Studies from Star One Credit Union


1. Fraudster used phishing email to steal member account credentials

2. Fraudster logged in to account and created a new bill pay payee

3. $4,500 was sent to the payee – a victim of a romance scam cultivated by the fraudster

4. Upon receiving the money, the romance scam victim wired the money back to the fraudster

5. After Star One reversed the transaction and credited the member’s account, the romance scam victim suffered the $4,500 loss

Takeaways: The source of this fraud was the Phishing e-mail and the entry into Online Banking. If we would have had Guardian Analytics in place at the time of this fraud, we feel, it would have been prevented and would have revealed the suspicious activity at the time of the initial unauthorized login.

Case Study: Before Guardian Analytics


Case Study: In Beta With Guardian Analytics

1. Fraudster used social engineering to obtain member login credentials & personal info

2. Fraudster contacted call center to change account phone number

3. Fraudster logged into member account and viewed cleared drafts to learn member’s signature

4. Fraudster initiated wire transfer of $98k to bank in Texas

5. Since signature matched and phone number routed to fraudster, Star One approved the transaction

6. Member discovered the loss when reviewing monthly statement

7. When Guardian Analytics reconstructed the login analysis of the account, they produced a Red alert on the first fraudulent login

Takeaways: The source of this fraud was the public/private info the fraudster had in order to social engineer our call center. We feel, and have the proof, that the Guardian solution would have discovered this fraud even though it involved both online and offline activity.


1. Guardian Analytics surfaced a red risk alert for an account login

2. Further investigation into activity from the login showed check image views and password & MFA question updates

3. Star One member claimed good security practices, felt no breach to his system was possible

4. Upon running a malware program, member discovered a key logger on his system

Takeaway: Even practicing good security habits can leave a PC vulnerable. With a solution in place that really guards against unauthorized logins, we feel we have extended the security on our valuable Online Banking members who are amongst the most savvy and profitable to us.

Case Study: Fraud Prevented


1. Member disputed Visa charges that occurred in person and online

2. Star One looked at Guardian Analytics data and found no anomalous activity

3. When faced with results of the investigation, member “remembered” that she had in fact made the purchases she was disputing and rescinded her affidavit

Takeaways: The Guardian system helped us detect when there was no fraud present. We feel that after explaining our methods of investigating online fraud and specifically the use of the login analysis tool, that our member realized that we have were aware of actual verified logins performed by the member at the time she clamed there was fraudulent activity.

Case Study: Fraud from Within


7,540 average daily logins

0.26% red and yellow alerts

3.35% of alerts require qualifying

17% of qualified alerts are actionable

Efficiency: Average Daily Alert Volume

Guardian Analytics leads to • Fewer total alerts • More qualified alerts• Less time spent reviewing

false positives


Prevented Fraud Loss in 2017

Loss prevented from 9 Bill-Pay transaction alerts

Loss prevented from Reg E ACH disputes across 40 transactions

Loss prevented from Wire Transfer fraud across 4 transactions

$160,469 $197,180 $878,472


Guardian Analytics Approach to Fraud Prevention

Collect user data

from online activity and transactions

Compare to known user ‘behavior’

Flag behavior that is

statistically anomalous for that user

Create action to mitigate risk


Building an End-to-End Solution

2017: AML Beta

2016: Omni-

Channel Visual

Analytics

2015: ACH RDFI

2014: ACH ODFI &

Wire

2012: Mobile

2007: Online Retail


• We have brought online losses to near $0

• We experience low alert rates

• We have no rules/scenarios to write

• There is no change to our customer experience and we market our security as an advantage to our members

• Examiners & the board are happy

• It feels like criminals do not attack us as much anymore because nothing gets through!

Star One Perspective: Why Guardian Analytics?


Guardian Analytics Customers See Results

Cut fraudulent transactions by 90%

Lower false positives by 5x

Reduce callbacks by 80%

Capture fraud in real time


Modern Financial Crime Platform Architecture

Omni-Channel Risk Engine

Enterprise API Integrated Risk Database

Fraud Cockpit

Omni-Channel Visual Analytics

Online Mobile ACH Wire Supplier Portal

AML


Financial institutions

Commercial & retail account holders

Banking activities

450 40M 5B

Guardian Analytics at a Glance


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Data & Analytics

Preventing Fraud with a Multi-Channel Approach