Cookies help us display personalized product recommendations and ensure you have great shopping experience.

By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    data analytics
    How Data Analytics Can Help You Construct A Financial Weather Map
    4 Min Read
    financial analytics
    Financial Analytics Shows The Hidden Cost Of Not Switching Systems
    4 Min Read
    warehouse accidents
    Data Analytics and the Future of Warehouse Safety
    10 Min Read
    stock investing and data analytics
    How Data Analytics Supports Smarter Stock Trading Strategies
    4 Min Read
    predictive analytics risk management
    How Predictive Analytics Is Redefining Risk Management Across Industries
    7 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: Reducing False Positives in Customer Screening
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Business Intelligence > Reducing False Positives in Customer Screening
Business Intelligence

Reducing False Positives in Customer Screening

Editor SDC
Editor SDC
4 Min Read
SHARE

False positives are the scourge of the Money Laundering Reporting Officer (MLRO) responsible for protecting the reputation and security of a financial institution.  Every occurrence of a client record matching to a name on a sanction, risk or PEP register has to be investigated; the review and research of false positives costs institutions time and manual effort.  “Fuzzy” techniques are essential to find inexact matches, but they often produce large numbers of records for review and the vast majority of these will be false positives.

Contents
  • Achieving a Balance
  • Achieving a Balance

With some institutions swamped by the volume of false positives, the temptation to tighten match rules can be irresistible.  But whilst this might reduce the immediate pain of so many false positives, it often increases the probability of a more insidious risk, that of false negatives.  Whilst false positives cost time and effort, false negatives allow criminals access to the financial system and can result in fines for the institution and the MLRO as well as a loss of commercial reputation.

 

Achieving a Balance

Financial institutions are instructed to take a risk-based approach to anti-money laundering (AML).  But the regulators have also shown that the…

More Read

Market Research in 3-D! – For Market Research, Social Networks Is to 2009 as what the Online Survey was in 1998
7 Habits of Highly Successful Big Data Pioneers
The use of mobile BI and alerts
Integrating Predictive Analytics and BRM to Improve Health Plan Member Experience
Innovation Jam ’08 Report

False positives are the scourge of the Money Laundering Reporting Officer (MLRO) responsible for protecting the reputation and security of a financial institution.  Every occurrence of a client record matching to a name on a sanction, risk or PEP register has to be investigated; the review and research of false positives costs institutions time and manual effort.  “Fuzzy” techniques are essential to find inexact matches, but they often produce large numbers of records for review and the vast majority of these will be false positives.

With some institutions swamped by the volume of false positives, the temptation to tighten match rules can be irresistible.  But whilst this might reduce the immediate pain of so many false positives, it often increases the probability of a more insidious risk, that of false negatives.  Whilst false positives cost time and effort, false negatives allow criminals access to the financial system and can result in fines for the institution and the MLRO as well as a loss of commercial reputation.

 

Achieving a Balance

Financial institutions are instructed to take a risk-based approach to anti-money laundering (AML).  But the regulators have also shown that they are willing to flex their muscles if they judge that an MLRO is failing to take adequate steps to implement adequate AML procedures, including the accurate screening of clients.  No screening system can produce perfect results, so the challenge facing the MLRO is to implement a solution that produces minimal false positives without increasing the risk of missing genuine matches.

With simple matching approaches, there is a direct relationship between the number of false positives and the number of false negatives; decreasing one leads to an increase in the other.  Thankfully, there are ways of decreasing the number of false positives without increasing the risk of false negatives. 

TAGGED:compliancefalse positives
Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

Edge Computing in IoT
Unique Capabilities of Edge Computing in IoT
Exclusive Internet of Things
Turning Geographic Data Into Competitive Advantage
The Rise of Location Intelligence: Turning Geographic Data Into Competitive Advantage
Big Data Exclusive
AI Recruitment Software Solution
The Best AI Recruitment Software Solution: Transforming Hiring with Smarter Tech
Artificial Intelligence Exclusive
real estate data
How Big Data Is Changes How We Buy and Sell Real Estate
Big Data Exclusive

Stay Connected

1.2KFollowersLike
33.7KFollowersFollow
222FollowersPin

You Might also Like

Celebrate Corporate Compliance and Ethics Week!

3 Min Read

Are Security Pros Becoming Too Paranoid?

3 Min Read

A Sustainability Storm is Brewing for BI

4 Min Read

Are Unsubscribe Confirmation Emails CAN-SPAM Compliant?

4 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

ai chatbot
The Art of Conversation: Enhancing Chatbots with Advanced AI Prompts
Chatbots
AI and chatbots
Chatbots and SEO: How Can Chatbots Improve Your SEO Ranking?
Artificial Intelligence Chatbots Exclusive

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-25 SmartData Collective. All Rights Reserved.
Welcome Back!

Sign in to your account

Username or Email Address
Password

Lost your password?