Surging False Positives: The Problem of Finding the Needle in a Haystack

Surging False Positives: The Problem of Finding the Needle in a Haystack

Financial Institutions are prevented from allowing any illicit entries onto their books. We have seen in the recent past, heavy fines were imposed on financial institutions who did not implement proper controls to check financial transactions carried out by terrorist organisations to conduit money using banking channels. This scenario has impacted the risk appetite of financial institutions when it comes to on-boarding customers.

With 4th Anti-Money Laundering Directive (4AMLD), the demands of AML have also increased with institutions required to perform KYC compliance process to screen both individuals and corporates to ensure that they comply with regulatory requirements.

Financial Institutions conduct screening of customers and transactions against a long list of data. The chance of a near match or mismatch called false positives must be investigated and eliminated, and the process is complex due to the mechanics of screening data – it is like finding the needle in a haystack.

If you ask AML professionals what causes them the most headaches, the issue of ‘False Positives’ will no doubt be at the top of the list, particularly when it involves customer name screening and adverse news research. Each of these crucial processes runs into problems caused by false positives.

The result is perhaps inevitable in that these daily screenings produce a large number of false positives, or alarms that flag an issue that must be investigated. In fact, a recent research by AML technology firm Fortytwo Data states that banks were spending £2.7 billion per year investigating false positives due to outdated AML systems. At the same time, spending by financial institutions and firms operating in other AML compliance-regulated industries is projected to increase to £6.4 billion in 2017 on a global scale and reach its peak at around 2020.

Investigating through each alert is time-consuming and complex at an average cost of £20 each time, cumulatively creating a hefty bill when a bank has millions of customers to screen. Remediation departments have steadily increased in size and there are larger financial institutions that report their annual expenditure for AML screening alone exceeds £3bn. However, this can be reduced by streamlining compliance process and implementing solutions with advanced matching algorithms. Here are some of the methodologies to reduce false positives:

1)     You can use partial matching algorithm which retrieves all the records that are similar to that on the watch list. Records show these type of relationship when few elements of the first record match few elements of the second record.

Blog Pic 1.png

2)     Phonetic Matching is another method which uses fuzzy logic algorithms to match records based on how they are pronounced rather than how they are spelt.

Blog Pic 2.png


3)     Another emerging method is to teach an intelligent machine to automatically learn from previously discounted transactions to refine the entire process, altering scoring criteria automatically to reduce false positives.

4)     A search using your customer’s biometrics over a biometrically enabled list of sanctioned individuals would dramatically decrease false positives.

5)     Blockchain and DLT can be used to create digital identities for the customers. This digital identity would store all relevant information about the customer from addresses, account details, director’s details, PEPs etc which could be used during AML / transaction monitoring, thus increasing the accuracy of the monitoring and reducing the likelihood of false positives.

Financial Institutions should start tapping the potential of the Machine Learning, Artificial Intelligence and Blockchain technologies to mitigate false positives and adhere to the regulatory expectations at a reduced cost.

At Cenza, we leverage RegTech solutions to help your institution find the needle in a haystack, and onboard customers faster with improved quality and efficiency. Do reach out to us to know more.


Deepak Amirtha Raj is a Research & Strategy Analyst in the Risk and Compliance sector. He focusses on Business Strategy Research, Emerging Technologies and Advanced Analytics. He studied business at Saint Joseph’s College and had previously worked with Royal Bank of Scotland as Business Process Analyst.

David Howland has over thirty years of experience in the Financial Services industry, providing Regulatory, Compliance and Risk guidance/advice to firms around the world to meet the ever-changing and growing home state and global regulatory and legislative expectations.

Can Big Data & Analytics come to the rescue of TBML violations?

Can Big Data & Analytics come to the rescue of TBML violations?

The strategy to deliver a robust UBO Compliance Process

The strategy to deliver a robust UBO Compliance Process