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4 Reasons to Welcome the New FCA Financial Crime Consultation


Financial Institutions (FIs) in the UK should take great encouragement from the recent Financial Conduct Authority (FCA) consultation paper on updates to the Financial Crime Guide

The proposed new guidance on Transaction Monitoring practices brings clarity to FIs seeking to improve their financial crime systems and controls with new technology. The FCA has recognized machine learning, entity resolution, and network analysis as key tools in the fight against financial crime going forward.

“This opens a real opportunity for FIs to embrace new technology,” said Michael Shearer, Chief Solution Officer at Hawk. “These new approaches will help them be more efficient and effective, finding much more financial crime.“

Key Takeaways

  1. The FCA recommends gaining a holistic view of customer behavior
  2. They highlight the importance of monitoring transactions at multiple levels 
  3. They encourage focusing on the overall results that new systems can deliver, rather than a like-for-like comparison with traditional approaches
  4. They endorse the use of new approaches to automated monitoring 


1. Financial Institutions Need a Holistic View of Customer Behavior 

The FCA’s paper emphasizes that a holistic view of customer behavior will be critical to AFC programs going forward. Historically, FIs have analyzed financial crime risk on a transaction-by-transaction basis. This approach can be enriched by considering the customer behavior in the wider context. A full picture of the financial crime risk that customers pose will enable FIs to improve risk coverage and safeguard operations.

“Regulators are encouraging  FIs to look at the holistic behavior exhibited by the customer in question,” said Shearer. “When we look at the context of the customer business and compare the behavior we expect against what we actually see, we find more suspicious activity worthy of investigation.”


2. Monitoring Can Be Improved with Multiple Levels of Aggregation 

The FCA also notes that it's important to look at Transaction Monitoring at multiple levels. When FIs only scrutinize the transaction and account levels, they can miss important signals of financial crime risk. Geographical, peer-group, and linked-entity network analysis enriches transaction data and contextualizes customer behavior. The contextual intelligence these technologies provide empowers FIs to improve financial crime risk detection. 


3. Financial Institutions Should Focus on the Overall Impact and Effectiveness of New Systems

The FCA is also turning its attention to the overall effectiveness of Transaction Monitoring systems. The consultation paper recognizes that a new system may work in slightly different ways, i.e., it may not flag all the same events. However, what really matters is effectively detecting more risk overall, not producing a clone of a previous system. This acknowledgment de-risks the implementation of new Transaction Monitoring technology. Regulators are encouraging FIs to explore systems that better detect financial crime risk. 


4. Financial Institutions Should Embrace New Approaches to Automated Monitoring 

The FCA paper endorses using state-of-the-art technologies to detect financial crime. It encourages FIs to employ machine learning, network analytics, and entity resolution to identify more risk. Using these valuable tools will result in better risk coverage for FIs and progress in regulators’ fight against financial crime.

“These technologies are now essential in the fight against financial crime at scale” said Shearer. 


Hawk’s AFC Technology

Hawk’s AI-powered AFC technology employs behavioral analysis to provide FIs with a holistic view of customer activity, in keeping with FCA and other regulatory guidance. Our platform gives AFC professionals access to machine learning, entity resolution, and network analysis technology that helps them improve financial crime risk coverage and streamline operations. 


To learn more about Hawk’s AI-powered AFC technology, request a demo today.

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