Financial Crime Prevention - Infographics | Feedzai https://feedzai.com/resource_type/infographic/ Tue, 17 Oct 2023 13:12:07 +0000 en-US hourly 1 https://feedzai.com/aptopees/2020/08/fav.png Financial Crime Prevention - Infographics | Feedzai https://feedzai.com/resource_type/infographic/ 32 32 Global Lessons from UK Finance’s Annual Fraud Report 2022 https://feedzai.com/resource/global-lessons-from-uk-finances-annual-fraud-report-2022/ Fri, 14 Jul 2023 13:17:27 +0000 https://feedzai.com/?post_type=resource&p=124978 Infographic]]>

Lesson 1.
Regulations positively impact scam losses

UK regulations requiring banks to reimburse for scam losses are proving critical for reducing financial harm. Other nations, including the US, Australia, and Singapore, are discussing similar regulatory measures.

Lesson 2.
Fraudsters aggressively shifted to physical card theft

Payment card fraud losses reached £556.3M, while card-not-present (CNP) theft saw the highest share of losses.

+6%

increase in
fraud losses

98%

of customers were
fully refunded

97%

Card ID Theft
compared to 2021

30%

Lost and Stolen
compared to 2021

Global trends will follow as consumers
resume pre-pandemic activities

Global trends will follow as consumers
resume pre-pandemic activities

Lesson 3.
Fraudsters move to mobile channels

Remote banking fraud losses reached £163.1M, a 46% decline from 2021.
As more customers adopt mobile banking, banks must work to keep mobile channels secure.

Lesson 4.
Advanced technology prevents scams

Banks must verify customer identity, analyze and aggregate behavioral biometrics, device intelligence, and transactional patterns to identify and block suspicious behavior.

  • Internet banking fraud: 76.3%
  • Mobile banking fraud: 15.7%
  • Telephone banking fraud: 7.9%

Lesson 5.
Track scam sources

Different channels present different challenges. For example, while social media sees more scam attempts, scam losses through telecommunication channels are much higher.

Originating Channel

Social Media

Telecommunications

Volume of Scams

58%

18%

Share of Losses

18%

44%

Tracing the source and assessing the financial impact of scams allows banks to enhance
their fraud prevention. This knowledge also educates customers about potential scam threats across channels.

Ready to learn how to combat top fraud threats, including scams, card-not-present, account takeover, and more?

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]]> 5 Steps to Choose a Machine Learning Platform for Risk https://feedzai.com/resource/5-steps-to-choose-a-machine-learning-platform-for-risk/ Wed, 12 Apr 2023 17:20:23 +0000 https://feedzai.com/?post_type=resource&p=120703 Infographic]]>

Infographic

Whether you’re shifting away from a legacy system or starting new, focusing on these five areas will set you up for success.

Download our infographic

Ask vendors the right questions

Is the platform future-proof?

How much control do you have?

Do you understand how the system arrived at its risk score?

Does the system enable omnichannel data?

Is it scalable?

Is expertise included in the price?

How specific does it get?

Does it offer flexible case management?

Is it self-configurable?

Determine the best deployment strategy

You can host your solution one of three ways: on your premises, in your vendor’s cloud, on your own cloud. Determine the deployment method that works best for you; there isn’t a one-size-fits-all approach.

Ask vendors the right questions

FI assumes operational and maintenance responsibilities, including procuring hardware, licenses, and QA governance inclusion of the system into existing frameworks.

Platform sits on the FI’s existing infrastructure.

Hidden/unanticipated operational costs can contribute to long-term spending (maintenance scalability).

Ongoing troubleshooting issues and remediation are less effective due to environment access and privilege restrictions applied.

Licensing agreements may limit FI’s scalability agenda.

Vendor Cloud

Vendor assumes responsibility for operation and maintenance
of the platform.

No hidden costs in lecensing agreement.

FIs have transparency into the platforms operational costs
and how it can scale.

No platform operation and maintenance.

Vendor manages troubleshooting and remediation.

Ensure the AI Platform is Unbiased and Makes Fair Decisions

Consider ethical AI questions at the beginning of your machine learning journey for fair risk decisions. Making AI ethics a priority can save your organization valuable time and money.

Focus on ‘Fairness-Awareness’ from the start of your AI journey so you develop models that are both fair and accurate.

Ensure your vendor provides easy-to-understand and clear explanations for how the model reached a decision.

Regularly audit models for bias and provide the reports to regulators when submitting decision-making reports.

Find a Vendor That Can Remove Silos Between Fraud and AML

Aligning fraud prevention and anti-money laundering solutions creates a more nimble approach to fraud prevention, realizes lower total cost of ownership, and enhances AML compliance. Benefits include:

Elimination of data silos

Upgraded operations

Boosted AI performance

Improved customer relationships

Ensure a Seamless Machine Learning Implementation Experience

Implementing a machine learning platform requires careful planning and preparation.
Here’s how to ensure a painless shift to a machine learning platform.

Prepare for big changes

Get a handle on your data

Look for a partnership, not a sale

Designate ownership of the platform

Think flexibly

Related Resources

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]]> Rules vs. Models in Anti-Money Laundering Platforms https://feedzai.com/resource/rules-vs-models-in-anti-money-laundering-platforms/ Tue, 13 Dec 2022 19:31:59 +0000 https://feedzai.com/?post_type=resource&p=116422 Infographic]]>

Machine Learning

Rules vs. Models in Anti-Money
Laundering Platforms

The value of money laundered globally each year is estimated to be 2-5% of global GDP. That falls between $800 billion and $2 trillion in USD. Machine learning models can help banks avoid the risks of ineffective AML solutions and help stop financial crime.

Download Infographic

The Truth About
Money Laundering

The estimated amount of money laundered globally in one year is 2 – 5% of global GDP, or $800 billion – $2 trillion in current US dollars. Machine learning can help banks avoid the repercussions of ineffective AML solutions and can actually help stop financial crime.

2–5%

of global GDP

$800 – $2

Billion do not delete Trillion

The Truth About
Money Laundering

The estimated amount of money laundered globally in one year is 2 – 5% of global GDP, or $800 billion – $2 trillion in current US dollars. Machine learning can help banks avoid the repercussions of ineffective AML solutions and can actually help stop financial crime.

2–5%

of global GDP

$800 – $2

Billion do not delete Trillion

Why Implement Machine
Learning For AML?

It creates more efficient and effective teams by automating case enrichment and prioritization for investigators.

It allows for more
accurate risk scoring.

Automation significantly
decreases the number
of false positives
generated.

In one instance, a bank
reduced the time (taken to work alerts) from several
weeks to a few seconds.

ACAMS

Rules-Only vs. Rules
with Machine Learning Models

Legacy AML systems provide high-volume, low-value alerts because they run on engines that only use rules. The overwhelming amount of false positives a rules-based system creates is akin to crying wolf.

AML programs powered by
machine learning often utilize
both rules and models, not just
rules. Using both rules and
models dramatically reduces false
positives, increases operational
efficiency, and requires less
maintenance.

How rules-based
risk engines work

Rules-based risk engines work by using a set of mathematical
conditions to determine what decisions to make.

Pros

Analysts can quickly create and implement new rules in robust and innovative systems.

A clear rule with specific calculations makes it easier to demonstrate to regulators why and when the system flagged the event as suspicious activity.

Cons

Rules alone aren’t sufficient because they have too many limitations.

Produce too many false positives.

Too complicated to understand context and dive deeper than formulas.

Require a great deal of manual effort to maintain.

Have fixed thresholds that criminals understand and purposely avoid.

Have trouble detecting relationships between transactions.

Only use YES/NO scenarios.

Cons

Produce too many false positives.

Pros

Analysts can quickly create and implement new rules in robust and innovative systems.

Cons

Rules alone aren’t sufficient because they have too many limitations.

Cons

Produce too many false positives.

A clear rule with specific calculations makes it easier to demonstrate to regulators why and when the system flagged the event as suspicious activity.

Too complicated to understand context and dive deeper than formulas.

Require a great deal of manual effort to maintain.

Have fixed thresholds that criminals understand and purposely avoid.

Have trouble detecting relationships between transactions.

Only use YES/NO scenarios.

How machine learning
risk engines work

Machine learning for AML strengthens rules
with models, which further reduces highvolume,
low-value alerts.

1

Data science teams feed the machine massive amounts of historical data about known and suspected money laundering cases.

How machine learning
risk engines work

Machine learning for AML strengthens rules
with models, which further reduces highvolume,
low-value alerts.

4

The machine predicts the risk of money laundering based on known and suspected money laundering cases or by referencing cases that were reported to the regulator.

2

Machine learning algorithms use the insights from these datasets to create statistical models, not deterministic rules.

3

The machine learns what money laundering has looked like in the past and, equally important, what normal behavior the looks like as well.

1

Data science teams feed the machine massive amounts of historical data about known and suspected money laundering cases.

4

The machine predicts the risk of money laundering based on known and suspected money laundering cases or by referencing cases that were reported to the regulator.

2

Machine learning algorithms use the insights from these datasets to create statistical models, not deterministic rules.

3

The machine learns what money laundering has looked like in the past and, equally important, what normal behavior the looks like as well.

1

Data science teams feed the machine massive amounts of historical data about known and suspected money laundering cases.

2

Machine learning algorithms use the insights from these datasets to create statistical models, not deterministic rules.

3

The machine learns what money laundering has looked like in the past and, equally important, what normal behavior the looks like as well.

4

The machine predicts the risk of money laundering based on known and suspected money laundering cases or by referencing cases that were reported to the regulator.

Things to Note

Machine learning models are only as good as their training data. The machine can’t learn without good, labeled data.

Machine learning models take time to learn, making them slower to implement. But once they are deployed, machine learning makes up for that time by providing more accurate alerts.

Machine learning saves your data science team countless hours they would have otherwise spent building and adjusting thousands of rules.

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]]> Key Findings from the UK Finance Annual Fraud Report https://feedzai.com/resource/key-findings-from-the-uk-finance-annual-fraud-report/ Thu, 08 Sep 2022 09:25:17 +0000 https://feedzai.com/?post_type=resource&p=113707 Infographic]]>

Infographic

Key Findings from the

UK Finance 2021
Annual Fraud Report

UK consumers lost over £1.3 billion to fraud in 2021. The latest Annual Fraud Report from UK Finance described the situation as an “epidemic of fraud,” with authorized push payment (APP) fraud cases notably high at 195,000 reported cases.

Download to learn:

  • how fraudsters scammed UK consumers out of £171.7m using investment scams
  • how impersonation scams rose by 50% as fraudsters used social engineering to exploit victims’ fears 
  • how CEO fraud surged by 165% bad actors pretending to be a CEO or boss
  • 3 things both banks and consumers can do to prevent fraud

Download Infographic

Authorized Push Payment(APP) fraud is surging

195,000

new cases reported in 2021

39%

increase in APP cases

£583.2m

estimated losses

Types of
APP Fraud

Work with Partners to Future-Proof Your Operations

Work with a trusted provider who understands that fraudsters constantly shift tactics. Stay up to date on the latest trends and future-proof your operations with partners who can protect any transaction type.

57%

increase in losses
from 2021 to 2022

£171.7m

estimated losses in 2021

Impersonation
Scams

Fraudsters use social engineering
tactics to play on victims’ fears and
steal their money.

50%

increase in losses
from 2021 to 2022

£215m

estimated losses in 2021

CEO
Fraud

Work with a trusted provider who understands that fraudsters constantly shift tactics. Stay up to date on the latest trends and future-proof your operations with partners who can protect any transaction type.

165%

increase in losses
from 2021 to 2022

£12.7m

estimated losses in 2021

3 Ways Banks

Can Reduce Fraud
Losses

  • Invest in cross-channel fraud detection, leveraging a full customer view to build profiles of normal activity
  • Offer consumers contextual advice based on APP scam types
  • Create layered controls to provide added protection

3 Steps
Consumers

Can Take to Protect
Themselves from
Scams

  • Never give out your card or internet banking credentials to anyone
  • Follow emerging APP fraud types to stay vigilant
  • Be skeptical – if it’s too good to be true, then it probably is

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]]> Top 5 Scams & How Banks Can Stop Them https://feedzai.com/resource/top-5-scams-how-banks-can-stop-them/ Thu, 25 Aug 2022 13:00:02 +0000 https://feedzai.com/?post_type=resource&p=110148 Infographic]]>

Infographic

Top 5 Scams & How Banks
Can Stop Them

Download Now

What is a Scam?

An attempt to steal sensitive information from a victim for illicit monetary gain.

Phishing

Smishing

Vishing

Pharming

70%
increase
over 2020

$5.8

Billion

US consumers lost to scams in 2021

$2.8

Billion

consumer complaints last year

Financial Cost of Scams in the US 1

1

Imposter
Scams

$2.3 Billion in
Fraud Losses

Up from
$1.2 Billion
in 2020

Top 5
scams

Grandparent Scams

Fraudsters pretend to be their grandchild in distress and ask to send money quickly

Charity scams

Claims to represent a
charity to solicit donations

Types of
imposter
scams

Government
Impersonation Scams

Claims to be officials from government
agencies; often says victim committed
a financial crime

Phishing Scams

An email, text (smishing), call (vishing), or social media scam tactic to deceive victims into revealing personal information. Gets victims to submit bank account or credit card details

Tech support scams

Pose as technical support staff and claim there is an issue with the victim’s computer. Gains remote access to devices and victim’s sensitive information or installs malware

2

Up from $246 million in 2020

Online Shopping Scams

$392 Million in
Fraud Losses

Claims to be a legitimate online seller, but orders never arrive, and refunds are never issued.

Prizes, Sweepstakes, and Lottery Scams

$255 Million in Victim Losses

3

27%
increase

4

Internet Services Scams

$222.4 Million in Losses 2

Claims service will be disconnected without a payment or offers huge discount with large up-front payment

5

Business and Job
Opportunity Scams

$208.7 Million in Losses

Promises jobs, government grants, or other money-making opportunities for a fee

5 Ways Banks Can Protect Customers Against Scams

  1. Watch for unusual customer behaviors via transactional data
  2. Modify existing operational policies and to spot unusual patterns
  3. Set transfer limits and rules
  4. Embrace machine learning
  5. Collaborate with other Fis on consumer education and awareness campaigns
Download Now

1 Fraud Reports by Federal Trade Commission
2 Fraud Reports by Federal Trade Commission

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]]> Global Fraud Hot Spots and How Banks Can Cool Them Off https://feedzai.com/resource/global-fraud-hot-spots-and-how-banks-can-cool-them-off/ Tue, 26 Jul 2022 11:59:26 +0000 https://feedzai.com/?post_type=resource&p=109155 Infographic]]>

Infographic

Global Fraud Hot Spots
and How Banks Can Cool
Them Off

Fraud Hot Spots Around the World

Fraud is a global problem. Some fraud types are more likely to originate from specific geographies than others. Here are some of the most troublesome fraud hot spots, the common types of fraud that originate from each location, and how financial institutions can address fraud hot spots.

Download Infographic

The Top 5

Global Fraud Hot Spots

#1

Turkey

Favored Fraud Types:
Mobile trojan banking malware with SMS grabbers and screen overlays, on-device fraud (ODF), and integration with automated transfer systems – a malware-as-a-service feature that enables criminals to move money using APIs.

Frequent targets:
Australia, Germany, Italy, Poland, Spain, UK, and USA.

#2

Nigeria

Favored Fraud Types:
419 or letter scams, romance scams, and CEO fraud.

Frequent targets:
India, UK, and USA.

#3

India

Favored Fraud Types:
Tech support scams, remote access trojans or tools (RATs), account takeover (ATO), and new account fraud.

Frequent targets:
UK and USA.

#4

Morocco

Favored Fraud Types:
ReelPhish and SIM Swap.

Frequent targets:
Latin America, Middle East, and Southern Europe.

#5

North Korea

Favored Fraud Types:
State-sponsored hacking and cyberattacks.

Frequent targets:
Japan, South Korea, NATO allies, and US-based industries (banking, entertainment, finance, cryptocurrency, and infrastructure).

3 Ways Banks Can Address Fraud From All Hot Spots

1

Work with Partners to Future-Proof Your Operations

Work with a trusted provider who understands that fraudsters constantly shift tactics. Stay up to date on the latest trends and future-proof your operations with partners who can protect any transaction type.

2

Know Your Customers’ Normal Behaviors

Build digital customer profiles based on every customer interaction to know how they normally behave. This makes it much easier to know if customers are behaving unusually or if a fraudster is attempting to access their account.

3

Know Your Customers’ Devices

Know your customers’ devices and how they normally interact with them. Assess interactions to determine if the device used to log into accounts is associated with the customer or if it is an unfamiliar one from an unfamiliar location.

Fraud comes in many forms and across different locations. Following these tips is an important step for banks worldwide to keep their customers safe in an expanding global economy.

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]]> AML Sanctions 101 & Sanctions Violation Prevention https://feedzai.com/resource/sanctions-101-sanctions-violation/ Tue, 31 May 2022 18:08:15 +0000 https://feedzai.com/?post_type=resource&p=107596 Infographic]]>

Infographic

Sanctions 101 & Sanctions Violation

Meeting ever-changing sanctions updates challenges anti-money laundering (AML) teams to do more than know their customers; they must understand and mitigate the unique risks of every customer.

Financial institutions (FIs) must have a solid understanding of sanctions and a plan for how to meet sanctions obligations, even as sanctions lists and laws evolve constantly.

Download Infographic

Infographic

Sanctions 101 & Sanctions Violation

Meeting ever-changing sanctions updates challenges anti-money laundering (AML) teams to do more than know their customers; they must understand and mitigate the unique risks of every customer.

Financial institutions (FIs) must have a solid understanding of sanctions and a plan for how to meet sanctions obligations, even as sanctions lists and laws evolve constantly.

Download Infographic

What are sanctions and why are they implemented?

The Association of Certified Anti-Money Laundering Specialists (ACAMS) defines economic sanctions as, “punitive or restrictive actions taken by individual countries, regimes, or coalitions with the primary purpose of provoking a change in behavior or policy.” They can be specific or general in their implementation and enforcement and may restrict:

  • TRADE
  • FINANCIAL TRANSACTIONS
  • DIPLOMATIC RELATIONS
  • PHYSICAL MOVEMENT

Who can be
sanctioned?

Entities

Individuals, organizations, countries, jurisdictions, regions, vessels, aircraft, and the like.

Trading

goods, services, and technologies.

What are sanctions and why are they implemented?

The Association of Certified Anti-Money Laundering Specialists (“ACAMS”) defines economic sanctions as, “punitive or restrictive actions taken by individual countries, regimes, or coalitions with the primary purpose of provoking a change in behavior or policy.” They can be specific or general in their implementation and enforcement and may restrict:

  • TRADE
  • FINANCIAL TRANSACTIONS
  • DIPLOMATIC RELATIONS
  • PHYSICAL MOVEMENT

Who can be
sanctioned?

Entities

Individuals, organizations, countries, jurisdictions, regions, vessels, aircraft, and the like.

Trading

goods, services, and technologies.

How do sanctions affect banks?

  1. Generally speaking, FIs are forbidden from interacting (e.g., transacting with, onboarding, etc.) with any sanctioned entity.
  2. Continuous sanctions watchlist screening is required for every customer transaction.
  3. Payments can’t be made to anyone on a sanctions list (including pass-through payments), nor with the understanding that the ultimate beneficiary is a sanctioned entity.
  4. Firms must have a detection program with controls to avoid and mitigate the effects of risks on national and international lines.

Example

A South African bank’s customer makes payments to the UK and the US. The South African bank must conduct sanctions screening against watchlists as required by their regulatory oversight bodies in the UK and US, as well as South Africa, to comply with minimum sanctions requirements.

How do sanctions affect banks?

  1. Generally speaking, FIs are forbidden from interacting (e.g., transacting with, onboarding, etc.) with any sanctioned entity.
  2. Continuous sanctions watchlist screening is required for every customer transaction.
  3. Payments can’t be made to anyone on a sanctions list (including pass-through payments), nor with the understanding that the ultimate beneficiary is a sanctioned entity.
  4. Firms must have a detection program with controls to avoid and mitigate the effects of risks on national and international lines.

Exemple

A South African bank’s customer makes payments to the UK and the US. The South African bank must conduct sanctions screening against watchlists as required by their regulatory oversight bodies in the UK and US, as well as South Africa, to comply with minimum sanctions requirements.

Sanctions Cases in the Court

  • 2004, United States: Kashani v. Tsann Kuen China Enterprise Co
  • 2015, United States: U.S. v. BNP Paribas SA
  • 2020, United Kingdom: Lamesa Investments Ltd v Cynergy Bank Ltd

Sanctions Cases in the Court

  • 2004, United States: Kashani v. Tsann Kuen China Enterprise Co
  • 2015, United States: U.S. v. BNP Paribas SA
  • 2020, United Kingdom: Lamesa Investments Ltd v Cynergy Bank Ltd

FIs can comply with
sanctions obligations by:

  • Using fresh data sources to monitor for changes in sanctions lists
  • Performing continuous screening across customers and payments
  • Implementing granular screening algorithms and controls to mitigate risks
  • Adopting effective compliance policies and technology across all operating jurisdictions

Download Infographic
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]]> Account Takeover Fraud: The Problem and Solution https://feedzai.com/resource/account-takeover-fraud-the-problem-and-solution/ Mon, 04 Apr 2022 15:31:16 +0000 https://feedzai.com/?post_type=resource&p=105546 Infographic]]>

Infographic

Account Takeover Fraud: The Problem and Solution

Download Now

Account takeover (ATO) fraud was the top fraud scam of 2021, according to Feedzai’s Q2 2022 Financial Crime Report. In this particularly troublesome type of identity theft, a fraudster takes control of a legitimate customer’s bank or online merchant account to transfer money, steal sensitive information, or make purchases with the victim’s payment cards.

Download the infographic to learn:

  • How U.S. losses from identity theft (including ATO) are projected to reach $635.4B by 2023;
  • The different actions that fraudsters take after gaining access to a customer’s account;
  • How 38% of ATO victims closed their bank accounts after experiencing fraud;
  • 3 steps banks can take to prevent ATO fraud.

Download Now
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]]> 15 Things to Know About APP Fraud https://feedzai.com/resource/15-things-to-know-about-app-fraud/ Thu, 30 Sep 2021 16:41:24 +0000 https://feedzai.com/?post_type=resource&p=99018 Infographic]]>

Infographic

15 Things to Know About APP Fraud

Download Infographic

Authorized push payment (APP) fraud has become one of the most common types of fraud thanks to the expansion of smartphones and connected devices. In the U.S., consumers lost approximately $3.3 billion to APP fraud in 2020. Meanwhile, across the pond in the U.K., an estimated £479 million was lost to APP fraud scams. 

APP is now so common that some banks face pressure to reimburse victims for their losses. Download our infographic to learn more about APP fraud, including:

  • How purchase scams alone cost U.K. consumers roughly £57.1 million in losses;
  • A breakdown of the five most common APP scams: purchase, impersonation, romance scams, and more;
  • Tips and advice for banks to stop APP fraud more effectively. 

Download the infographic!

Download Infographic
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]]> The 3 Stages of Fraud Lifecycle https://feedzai.com/resource/the-3-stages-of-fraud/ Tue, 23 Feb 2021 15:24:34 +0000 https://feedzai.com/?post_type=resource&p=95028 Infographic]]>

A fraud attack isn’t just a single event. Fraud attacks are made up of several distinct stages. As fraud attacks and data breaches become increasingly common, it’s important for banks and merchants to understand the full lifecycle of a fraud attack and prepare for each stage. 

Download our infographic to :

  • Customer Access: How fraudsters gain access to legitimate customers’ accounts
  • Transaction: The range of activities fraudsters can take upon breaching an account
  • Monetization: How fraudsters ultimately profit from their activities

Download the infographic!

Download our free Resource

Infographic

The 3 Stages of Fraud Lifecycle

Download Infographic

A fraud attack isn’t just a single event. Fraud attacks are made up of several distinct stages. As fraud attacks and data breaches become increasingly common, it’s important for banks and merchants to understand the full lifecycle of a fraud attack and prepare for each stage. 

Download our infographic to :

  • Customer Access: How fraudsters gain access to legitimate customers’ accounts
  • Transaction: The range of activities fraudsters can take upon breaching an account
  • Monetization: How fraudsters ultimately profit from their activities

Download the infographic!

Download Infographic
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]]>