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AI’s role in enhancing transaction monitoring and compliance





In the complex landscape of global finance, the battle against money laundering and terrorism financing is intensifying. 

In a recent whitepaper by RelyComply, the company took a deep dive into the world of AI and transaction monitoring and asked: is this the next frontier?

According to the RegTech company, criminals have become adept at exploiting the digital avenues offered by the internet age, posing significant challenges to financial institutions. Adopting artificial intelligence (AI) in transaction monitoring represents a pivotal shift in this ongoing struggle, promising a more effective defence against the flow of illicit funds.

The escalation of financial crime has rendered traditional, hands-on monitoring methods outdated and ineffective. With vast datasets to scrutinise, the need for automated systems to detect suspicious activity in real-time and identify transactional trends has never been more apparent.

In the view of RelyComply, AI’s role in this context is not merely an addition; it is a fundamental reimagining of transaction monitoring strategies designed to keep institutions a step ahead of criminal endeavours.

The synergy between human expertise and AI technology marks a significant evolution in compliance procedures. AI’s advanced data processing and anomaly detection capabilities effectively address manual monitoring limitations, such as the high incidence of false positives.

This fusion of technology and human insight creates more efficient compliance teams capable of responding swiftly and accurately to potential threats.

The financial and moral implications of failing to combat fincrime are profound. RelyComply stated in the whitepaper that with fines exceeding $10 billion in 2020 for non-compliance and the proceeds of crime funding activities like forced prostitution, drug trafficking, and terrorism, the stakes could not be higher. The global GDP affected by money laundering is estimated to be between 2 and 5%, highlighting the vast scale of the problem.

As regulations tighten and non-compliance costs mount, financial institutions are under increasing pressure to enhance their Anti-Money Laundering (AML) and Know Your Customer (KYC) systems.

The COVID-19 pandemic has added to these challenges, with many firms prioritising business continuity over compliance investments. However, the advent of AI in transaction monitoring offers a beacon of hope, providing a more efficient means of identifying and reporting suspicious activities.

AI’s role in this new compliance landscape is twofold: reducing false positives and detecting anomalous behaviour. Yet, despite its advanced capabilities, the human element remains crucial. Analysts are essential for interpreting AI-generated data and making informed decisions about the risk of criminal activity.

The blend of AI technology and human expertise represents the future of financial crime detection, promising a more secure and compliant financial system.

RelyComply explained in the whitepaper that AI epitomises the zenith of computer systems’ capability to replicate human cognitive functions such as learning, logical reasoning, and problem-solving. This technology employs data models to analyse occurrences and recommend actions decisively. Within financial technology, or FinTech, AI’s application has rapidly evolved, significantly altering the landscape of banking operations and customer service.

Machine learning (ML), a subset of AI, underscores how a computer enhances its intelligence over time through self-learning. This process is fueled by analysing patterns within data models, a task meticulously refined by data scientists. The interchangeable use of ‘machine learning’ and ‘data science’ highlights their intertwined roles in advancing AI’s capabilities.

Generative AI (GenAI), propelled into the limelight by its success in natural language processing tools like ChatGPT and image generation from user prompts, represents a significant leap forward. GenAI is increasingly becoming essential in the banking sector, enhancing user experiences through virtual assistants, personalising financial content, and streamlining product testing processes.

The global adoption of AI within finance is witnessing a significant surge, a stark contrast to the 29% adoption rate reported by Gartner in 2022. This upward trend is attributed to AI’s potential to refine banking operations and contribute to business value.

AI’s role in AML compliance stands out for its automation and data analysis capabilities, particularly in risk management. It provides efficient shortcuts for administrative tasks and leverages extensive data analysis to identify suspicious activities swiftly and accurately.

AI’s utility in transaction monitoring is profound, especially in reducing false positives and enhancing the detection of anomalous payments. By analysing historical data, AI can predict the risk associated with new alerts, improving the efficiency of AML compliance processes. Additionally, AI facilitates anomaly detection by comparing recent account behaviours with historical data, thus identifying unusual activities that could indicate criminal intentions.

However, deploying AI in transaction monitoring presents challenges, including the need for transparency, mitigating biases, and addressing model drift. These considerations are vital to ensuring that AI’s decision-making processes are ethical, interpretable, and adaptable to changing financial and criminal patterns.

Furthermore, integrating AI in financial AML systems requires a balanced approach between leveraging technological advancements and maintaining human expertise. In the view of RelyComply, the role of compliance teams and the implementation of regulatory technology RegTech are crucial in navigating the regulatory landscape and ensuring AML compliance.

Full whitepaper available at:

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How to identify authenticity in crypto influencer channels




Modern brands stake on influencer marketing, with 76% of users making a purchase after seeing a product on social media.The cryptocurrency industry is no exception to this trend. However, promoting crypto products through influencer marketing can be particularly challenging. Crypto influencers pose a significant risk to a brand’s reputation and ROI due to rampant scams. Approximately 80% of channels provide fake statistics, including followers counts and engagement metrics. Additionally, this niche is characterized by high CPMs, which can increase the risk of financial loss for brands.

In this article Nadia Bubennnikova, Head of agency Famesters, will explore the most important things to look for in crypto channels to find the perfect match for influencer marketing collaborations.



There are several levels related to this point.



Analyze approximately 10 of the channel’s latest videos, looking through the comments to ensure they are not purchased from dubious sources. For example, such comments as “Yes sir, great video!”; “Thanks!”; “Love you man!”; “Quality content”, and others most certainly are bot-generated and should be avoided.

Just to compare: 


Don’t rush to conclude that you’ve discovered the perfect crypto channel just because you’ve come across some logical comments that align with the video’s topic. This may seem controversial, but it’s important to dive deeper. When you encounter a channel with logical comments, ensure that they are unique and not duplicated under the description box. Some creators are smarter than just buying comments from the first link that Google shows you when you search “buy YouTube comments”. They generate topics, provide multiple examples, or upload lists of examples, all produced by AI. You can either manually review the comments or use a script to parse all the YouTube comments into an Excel file. Then, add a formula to highlight any duplicates.


It is also a must to check the names of the profiles that leave the comments: most of the bot-generated comments are easy to track: they will all have the usernames made of random symbols and numbers, random first and last name combinations, “Habibi”, etc. No profile pictures on all comments is also a red flag.



Another important factor to consider when assessing comment authenticity is the posting date. If all the comments were posted on the same day, it’s likely that the traffic was purchased.


2. Average views number per video

This is indeed one of the key metrics to consider when selecting an influencer for collaboration, regardless of the product type. What specific factors should we focus on?

First & foremost: the views dynamics on the channel. The most desirable type of YouTube channel in terms of views is one that maintains stable viewership across all of its videos. This stability serves as proof of an active and loyal audience genuinely interested in the creator’s content, unlike channels where views vary significantly from one video to another.

Many unauthentic crypto channels not only buy YouTube comments but also invest in increasing video views to create the impression of stability. So, what exactly should we look at in terms of views? Firstly, calculate the average number of views based on the ten latest videos. Then, compare this figure to the views of the most recent videos posted within the past week. If you notice that these new videos have nearly the same number of views as those posted a month or two ago, it’s a clear red flag. Typically, a YouTube channel experiences lower views on new videos, with the number increasing organically each day as the audience engages with the content. If you see a video posted just three days ago already garnering 30k views, matching the total views of older videos, it’s a sign of fraudulent traffic purchased to create the illusion of view stability.


3. Influencer’s channel statistics

The primary statistics of interest are region and demographic split, and sometimes the device types of the viewers.


When reviewing the shared statistics, the first step is to request a video screencast instead of a simple screenshot. This is because it takes more time to organically edit a video than a screenshot, making it harder to manipulate the statistics. If the creator refuses, step two (if only screenshots are provided) is to download them and check the file’s properties on your computer. Look for details such as whether it was created with Adobe Photoshop or the color profile, typically Adobe RGB, to determine if the screenshot has been edited.


After confirming the authenticity of the stats screenshot, it’s crucial to analyze the data. For instance, if you’re examining a channel conducted in Spanish with all videos filmed in the same language, it would raise concerns to find a significant audience from countries like India or Turkey. This discrepancy, where the audience doesn’t align with regions known for speaking the language, is a red flag.

If we’re considering an English-language crypto channel, it typically suggests an international audience, as English’s global use for quality educational content on niche topics like crypto. However, certain considerations apply. For instance, if an English-speaking channel shows a significant percentage of Polish viewers (15% to 30%) without any mention of the Polish language, it could indicate fake followers and views. However, if the channel’s creator is Polish, occasionally posts videos in Polish alongside English, and receives Polish comments, it’s important not to rush to conclusions.

Example of statistics


Wrapping up

These are the main factors to consider when selecting an influencer to promote your crypto product. Once you’ve launched the campaign, there are also some markers to show which creators did bring the authentic traffic and which used some tools to create the illusion of an active and engaged audience. While this may seem obvious, it’s still worth mentioning. After the video is posted, allow 5-7 days for it to accumulate a basic number of views, then check performance metrics such as views, clicks, click-through rate (CTR), signups, and conversion rate (CR) from clicks to signups.

If you overlooked some red flags when selecting crypto channels for your launch, you might find the following outcomes: channels with high views numbers and high CTRs, demonstrating the real interest of the audience, yet with remarkably low conversion rates. In the worst-case scenario, you might witness thousands of clicks resulting in zero to just a few signups. While this might suggest technical issues in other industries, in crypto campaigns it indicates that the creator engaged in the campaign not only bought fake views and comments but also link clicks. And this happens more often than you may realize.

Summing up, choosing the right crypto creator to promote your product is indeed a tricky job that requires a lot of resources to be put into the search process. 

Author Nadia Bubennikova, Head of agency  at Famesters


Nadia Bubennikova, Head of agency at Famesters

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Central banks and the FinTech sector unite to change global payments space





The BIS, along with seven leading central banks and a cohort of private financial firms, has embarked on an ambitious venture known as Project Agorá.

Named after the Greek word for “marketplace,” this initiative stands at the forefront of exploring the potential of tokenisation to significantly enhance the operational efficiency of the monetary system worldwide.

Central to this pioneering project are the Bank of France (on behalf of the Eurosystem), the Bank of Japan, the Bank of Korea, the Bank of Mexico, the Swiss National Bank, the Bank of England, and the Federal Reserve Bank of New York. These institutions have joined forces under the banner of Project Agorá, in partnership with an extensive assembly of private financial entities convened by the Institute of International Finance (IIF).

At the heart of Project Agorá is the pursuit of integrating tokenised commercial bank deposits with tokenised wholesale central bank money within a unified, public-private programmable financial platform. By harnessing the advanced capabilities of smart contracts and programmability, the project aspires to unlock new transactional possibilities that were previously infeasible or impractical, thereby fostering novel opportunities that could benefit businesses and consumers alike.

The collaborative effort seeks to address and surmount a variety of structural inefficiencies that currently plague cross-border payments. These challenges include disparate legal, regulatory, and technical standards; varying operating hours and time zones; and the heightened complexity associated with conducting financial integrity checks (such as anti-money laundering and customer verification procedures), which are often redundantly executed across multiple stages of a single transaction due to the involvement of several intermediaries.

As a beacon of experimental and exploratory projects, the BIS Innovation Hub is committed to delivering public goods to the global central banking community through initiatives like Project Agorá. In line with this mission, the BIS will soon issue a call for expressions of interest from private financial institutions eager to contribute to this ground-breaking project. The IIF will facilitate the involvement of private sector participants, extending an invitation to regulated financial institutions representing each of the seven aforementioned currencies to partake in this transformative endeavour.

Source: fintech.globa

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TD Bank inks multi-year strategic partnership with Google Cloud





TD Bank has inked a multi-year deal with Google Cloud as it looks to streamline the development and deployment of new products and services.

The deal will see the Canadian banking group integrate the vendor’s cloud services into a wider portion of its technology solutions portfolio, a move which TD expects will enable it “to respond quickly to changing customer expectations by rolling out new features, updates, or entirely new financial products at an accelerated pace”.

This marks an expansion of the already established relationship between TD Bank and Google Cloud after the group previously adopted the vendor’s Google Kubernetes Engine (GKE) for TD Securities Automated Trading (TDSAT), the Chicago-based subsidiary of its investment banking unit, TD Securities.

TDSAT uses GKE for process automation and quantitative modelling across fixed income markets, resulting in the development of a “data-driven research platform” capable of processing large research workloads in trading.

Dan Bosman, SVP and CIO of TD Securities, claims the infrastructure has so far supported TDSAT with “compute-intensive quantitative analysis” while expanding the subsidiary’s “trading volumes and portfolio size”.

TD’s new partnership with Google Cloud will see the group attempt to replicate the same level of success across its entire portfolio.


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