How To Overcome The Most Common AML/CFT Challenges

UBS, a global firm providing financial services in over 50 countries, was fined US$15 million in 2018 for several shortcomings within its AML programs. One of its branches in San Diego did not have an adequately designed AML program to monitor risky accounts owned by non-US residents. Between 2011 to 2013, the branch moved over US$9 billion through 6,000 questionable accounts that were later found to be linked to organised crime.

Scandals like this, and the case of Danske Bank, are making it harder to claim that even more developed economies are effectively using technology to police their financial systems.

The necessary transition to Work-From-Home during the Covid-19 pandemic has also added complexity to AML/CFT compliance, from contactless customer onboarding to changes in buyer behaviour that impact the ability to monitor transactions, and the increased risks connected to online crime.

The need for technology-enabled AML/CFT systems and processes cannot be overemphasised.

Based on research conducted by LexisNexis Risk Solutions AML/CFT challenges can be broadly grouped into

1. Internal Challenges — these include internal capacity gaps and business model risks.

2. External Challenges — like Specific Crimes, Government regulations and market-based challenges

Multiple types of threats/challenges faced by financial institutions

For the sake of this discussion, we will focus on Internal gaps.

Internal Capacity Gaps

Many of the hurdles and hindrances to the fight against financial crime come from within organisations. Based on a survey of AML/CFT professionals conducted by LexisNexis Risk Solutions, respondents cited several internal process, resource and structural issues as holding them back. These include data quality, gaps in IT infrastructure and ineffective internal tools and antiquated technologies being used against very sophisticated attacks.

Many of these professionals dread the potential consequences of a compliance failure on their institutions. Their companies are open to the risk of reputational damage and loss of customer confidence. This aversion translates into a culture of over-cautiousness, leading to overreporting of irregular activity and at times unnecessary extra work.

That said, traditional mindsets and culture towards technology appear to be an underlying barrier to change.

Specific types of crime threats mentioned by the group as a result of Internal Gaps.

Leveraging technology

Regulators expect financial institutions to consistently monitor customer activity to identify suspicous patterns or behaviour. This can only be achieved successfully when data is effectively aggregated across and institutions processes, systems and geographic locations.

However, transactional data is often stored in different places and in various legacy systems, thus making it challenging to connect common attributes to a single customer or account. This limits the effectiveness of transactional monitoring and analysis. The capactiy to recognise suspicious transactional activity would be significantly improved if the siloed data could be analysed as a group.

Criminal organisations often launder funds between various financial institutions giving another reason to work towards aggregating data from multiple sources. It is more challenging to identify a suspicious transaction with information from only one source in a series of money transfers.

Regulators across the world have begun to strongly support the use of innovative solutions, such as Artificial Intelligence (AI) and Machine Learning (ML) to identify suspicious activity more effectively. Benefits of such innovative technologies, include strengthening AML compliance programmes, enhancing transaction monitoring capabilities and maximising the use of compliance resources.

A good example is a paper published in 2018 by the Singapore Police Force and Monetary Authority of Singapore. Its purpose was to facilitate the adoption of data analytics and provided a case study of a bank that used machine learning to reduce false positives by as much as 60%

The Federal Financial Supervisory Authority of Germany also produced a comprehensive report in 2018 to assess the benefits and risks of using AI saying that Big Data and Artificial Intelligence make it easier to identify irregularities and patterns. AI increases the efficiency and effectiveness of compliance processes, such as the prevention of money laundering and fraud.

In Conclusion

Technology can increase operational efficiency and effectiveness of financial crime compliance, yet organisations are still spending almost twice as much on costly labour.

An increased understanding of how data and technology deliver deeper insights is essential for decision-makers to implement the right systems and approaches to mitigate AML/CFT risks in this new era.

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References

1. FUTURE FINANCIAL CRIME RISKS 2020 WHAT KEEPS AML & CFT PROFESSIONALS AWAKE AT NIGHT? — LexisNexis Risk Solutions
2. https://www.moneylaunderingbulletin.com/moneylaundering/training/home-front-143931.htm
3. https://globalinvestigationsreview.com/benchmarking/europe-middle-east-and-africa-investigations-review-2020/1227362/anti-money-laundering-trends-and-challenges