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USING AI TO DETECT MONEY LAUNDERING NETWORKS

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MONEY LAUNDERING

By John Spooner, Head of Artificial Intelligence, EMEA, H2O.ai

 

Artificial Intelligence (AI) has evolved significantly from being a mere technology buzzword, to the commercial reality it is today.  The technology is making a positive impact across many industries, including the financial sector.  The financial services industry has a reputation of constantly innovating and advancing new technologies, in the pursuit of strengthening the customer base, and finding new revenue opportunities.  This is happening across all segments including capital markets, commercial banking, consumer finance and insurance.

The use of AI in the financial services is rapidly changing the business landscape, even in traditionally conservative areas.  According to a recent Bank of England survey of 500 UK financial institutions, two third

s of respondents were reported to have already been using machine learning in some form, with the median firm using live ML applications in two business areas.  This is expected to more than double within the next three years. Financial institutions today utilise AI for areas such as customer service, risk management, fraud detection and anti-money laundering, while adhering to regulatory compliance.

AI technology has proven to be reliable, especially when it comes to detecting money laundering, and is empowering leading financial services to tackle such issues in an increasingly effective manner.

 

MONEY LAUNDERING

John Spooner

Anti-Money Laundering

Money laundering is defined as “the concealment of the origins of illegally obtained money, typically by means of transfers involving foreign banks or legitimate businesses.” Reuters reported in 2017 that the total US and EU fines on banks’ misconduct, including anti-money laundering violations since 2009 amounts to $342 billion.

Money laundering poses a serious threat to the financial services sector.  According to the United Nations Office on Drugs and Crime, an estimated $2 trillion is “cleaned” through the banking system every year. Fines for banks that fail to prevent money laundering have increased by 500 fold  in the past decade, and is now worth more than $10 billion per year.  As a result, banks have constructed large teams, and allocated them the time-consuming tasks of identifying and investigating any suspicious transactions, which often takes the form of multiple small transfers within a complex network of players.

Traditional Approaches for Tackling Money Laundering

Typically, investigation teams use rule-based systems like FICOFiservSAS AML or Actimize to identify any suspicious transactions. This rule-based workflow consists of the following three steps:  Firstly, an alert is generated by the alerting system; secondly, the investigator reviews it using information from different sources and finally, the alert is approved as True Positive or classified as False Positive.   A False Positive can be defined as an error in data reporting, in which a test result improperly indicates the presence of a condition that in reality is not present.

However, the problem with rule-based systems is that they create a large number of false positives, usually in the range of 75 to 99 percent.  These means that a vast amount of time and manual effort is being wasted to investigate these false alerts.  The high number occurs because the rules can become outdated quickly and it take time for the systems to be recoded.

 

How AI Can Address False Positives

Anti-Money Laundering (AML) programmes that are used in capital markets and retail banking extensively deploy rule-based transaction monitoring systems, spanning areas across monetary thresholds and money laundering patterns. However, bad actors can adapt to these rules over time, and tweak their methods accordingly to avoid detection. This is where AI-based behavioural modelling and customer segmentation can be more effective, in discovering transaction behaviours and identify behavioural patterns and outliers, that indicates any potential laundering.

AI, especially time series modelling, is particularly effective at examining a series of complex transactions and finding anomalies.  Anti-money laundering using machine learning techniques are able to identify suspicious transactions, and also irregular networks of transactions. These transactions are flagged for investigation, and can be scored as high, medium, or low priority, so that the investigator is able prioritise their efforts. As the actors modify their behaviour, so does the AI that is underpinning the programmes, meaning the number of false positives stays low while maintaining a high number of true positives.

AI can also provide reason codes for the decision to flag transactions. These reason codes tell the investigator where they might need to search to uncover the issues, and help to streamline the investigative process.  AI is also able to learn from the investigators throughout the review, clearing any suspicious transactions and automatically reinforcing the AI model’s understanding and ability to avoid patterns that don’t lead to laundered money.

 

AI vs Rule-based Systems

AI-powered AML systems provide many advantages over an existing rule-based system.  This includes being able to dramatically reduce false positives, provide a curated set of alerts to the investigator and the ability to ingest domain specific IP customised for money laundering.  The AI technology can be strategically placed between the AML rule-based system and the investigator, which allows companies to gain a rapid return of investment.  Overall, the average investigation time is dramatically reduced from between 45 to 90 days, to mere seconds. It also greatly reduces any human inaccuracies and hours required per person, and can fit rule-gaps with innovative features.

Address Money Laundering and Drive Productivity

When used effectively, Artificial Intelligence (AI) can be a critical factor to success in the financial services industry.  It enables financial services companies to not only efficiently build personalised banking experiences, fraud and money laundering models but will also improve employee and business productivity.  As money laundering networks become ever more complex, the time is now, for progressive financial intuitions to start embracing AI in order to effectively combat money laundering, and to focus even more effectively on driving overall productivity.

Banking

Wealth Managers and the Future of Trust: Insights from CFA Institute’s 2022 Investor Trust Study

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Author: Rhodri Preece, CFA, Senior Head of Research, CFA Institute

 

Corporate responsibility is more important than ever. Today, many investors expect more than just profit from their financial decisions; they want easy access to financial products and to be able to express personal values through their investments. Crucial to meeting these new investor expectations is trust in the financial services providers that enable investors to build wealth and realise personal goals. Trust is the bedrock of client relationships and investor confidence.

The 2022 CFA Institute Investor Trust Study – the fifth in a biennial series – found that trust levels in financial services among retail and institutional investors have reached an all-time high. Reflecting the views of 3,588 retail investors and 976 institutional investors across 15 markets globally, the report is a barometer of sentiment and an encouraging indicator of the trust gains in financial services.

Wealth managers may want to know how this trust can be cultivated, and how they can enhance it within their own organisations. I outline three key trends that will shape the future of client trust.

 

THE RISE OF ESG

ESG metrics have risen to prominence in recent years, as investors increasingly look at environmental, social and governance factors when assessing risks and opportunities. These metrics have an impact on investor confidence and their propensity to invest; we find that among retail investors, 31% expect ESG investing to result in higher risk-adjusted returns, while 44% are primarily motivated to invest in ESG strategies because they want to express personal values or invest in companies that have a positive impact on society or the environment.

The Trust Study shows us that ESG is stimulating confidence more broadly. Of those surveyed, 78% of institutional investors said the growth of ESG strategies had improved their trust in financial services. 100% of this group expressed an interest in ESG investing strategies, as did 77% of retail investors.

There are also different priorities within ESG strategies, and our study found a clear divide between which issues were top of mind for retail investors compared to institutional investors. Retail investors were more focused on investments that tackled climate change and clean energy use, while institutional investors placed a greater focus on data protection and privacy, and sustainable supply chain management.

What is clear is that the rise of ESG investing is building trust and creating opportunities for new products.

TECHNOLOGY MULTIPLIES TRUST

Technology has the power to democratise finance. In financial services, technological developments have lowered costs and increased access to markets, thereby levelling the playing field. Allowing easy monitoring of investments, digital platforms and apps are empowering more people than ever to engage in investing. For wealth managers, these digital advancements mean an opportunity for improved connection and communication with investors, a strategy that also enhances trust.

The study shows us that the benefits of technology are being felt, with 50% of retail investors and 87% of institutional investors expressing that increased use of technology increases trust in their financial advisers and asset managers, respectively. Technology is also leading to enhanced transparency, with the majority of retail and institutional investors believing that their adviser or investment firms are very transparent.

It’s worth acknowledging here that a taste for technology-based investing varies across age groups. More than 70% of millennials expressed a preference for technology tools to help navigate their investment strategy over a human advisor. Of the over-65s surveyed, however, just 30% expressed the same choice.

 

THE PULL OF PERSONALISATION

How does an investor’s personal connection to their investments manifest? There are two primary ways. The first is to have an adviser who understands you personally, the second is to have investments that achieve your personal objectives and resonate with what you value.

Among retail investors surveyed for the study, 78% expressed a desire for personalised products or services to help them meet their investing needs. Of these, 68% said they’d pay higher fees for this service.

So, what does personalisation actually look like? The study identifies the top three products of interest among retail investors. They are: direct indexing (investment indexes that are tailored to specific needs); impact funds (those that allow investors to pursue strategies designed to achieve specific real-world outcomes); and personalised research (customised for each investor).

When it comes to this last product, it’s worth noting that choosing advisors with shared values is also becoming more significant. Three-quarters of respondents to the survey said having an adviser that shares one’s values is at least somewhat important to them. Another way a personal connection with clients can be established is through a strong brand, and the proportion of retail investors favouring a brand they can trust over individuals they can count on continues to grow; it reached 55% in the 2022 survey, up from 51% in 2020 and 33% in 2016.

 

TRUST IN THE FUTURE

As the pressure on corporations to demonstrate their trustworthiness increases, investors will also look to financial services to bolster trust. Wealth managers that embrace ESG issues and preferences, enhanced technology tools, and personalisation, can demonstrate their value and build durable client relationships over market cycles.

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UK Organisations turn to artificial intelligence to fight sophisticated cyberattacks

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New research by cybersecurity expert Mimecast finds that email attacks are becoming more frequent and sophisticated

More and more companies in the UK are using artificial intelligence (AI) and machine learning (ML) to fend off increasingly sophisticated cyber-attacks, according to new research from cybersecurity specialist Mimecast. The research finds that 40% of UK organisations are already using AI or ML in their organisations’ cybersecurity programme, with 30% planning to do so within the next 12 months.

The use of advanced technologies such as AI and ML is in direct response to the growing sophistication of cyberattacks that UK businesses are experiencing. 53% believe that increasingly sophisticated attacks will be their biggest email security challenge in 2022, leading to 80% believing it is at least likely their organisation will suffer a negative business impact from an email-borne attack this year.

 

Growing threat landscape

The research shows that email remains the largest threat vector for UK businesses, with 71% of respondents reporting an increase in the volume of email threats their organisation has faced in the last 12 months. This includes phishing with malicious links or attachments (56%), impersonation fraud or Business Email Compromise (53%), and malicious insiders (43%). However, it isn’t just email attacks that are on the rise, as 90% of UK businesses experienced at least one spoofing attack that uses a lookalike web domain or a clone of their organisation’s website in the last 12 months. The average UK company has experienced 11 of these attacks.

On top of this, employees are also presenting organisations with a very real threat to their cybersecurity. The survey identifies that IT decision makers have relatively low confidence in their colleagues’ cyber awareness , believing that there is a risk of an employee making a serious security risk due to oversharing company information on social media (84%), poor password hygeine (80%), using personal email (80%), or using cloud storage and other shadow IT functionality (81%). When an employee does full victim to an attack, it frequently results in more widespread consequences. 85% of respondents say threats have spread from one infected user to other members of the organisation.

 

AI to the rescue

To overcome this growing threat landscape, more and more UK organisations are turning to advanced technologies to strengthen their cybersecurity position. The 40% of UK organisations that are already using AI as part of their cybersecurity strategy are already seeing a number of benefits, including increased accuracy in terms of threat detection (54%), reduced human error within cybersecurity team (51%), and reduced workload/working hours for cybersecurity team (45%).

Despite these very real benefits, there is the very real danger that many UK organisations will miss out due to a lack of budget dedicated to cybersecurity. The research highlights a clear discrepancy between the amount IT decision makers believe should be spent on cyber resilience and how much budget is actually allocated by business leaders. IT decision makers in the UK believe that 16% of their IT budget should be allocated to cyber, but at the moment they see less than 12% allocated. Missing out on new technology innovations such as AI is identified as the most likely consequence (49%) for organisations where the cybersecurity budget is not as high as respondents believe it should be.

Elaine Lee, AI expert at Mimecast, said: “There is no doubt that cyberattacks are becoming more frequent, as UK businesses adjust to the world of hybrid work. On top of this increase in frequency, we are also seeing a rise in the sophistication of attacks. This is creating a perfect storm and making it more difficult than ever for organisations to keep their businesses secure. With this in mind, it is no surprise to see so many organisations turn to advanced technologies such as AI to bolster their cybersecurity defences. AI solutions can help businesses to automate security processes, ensuring they are better able to fend off attacks, as well as providing their security experts with more time to focus on high-level analyses that require human interaction.”

Lee continued: “Organisations that have yet to invest in AI technologies as part of their cybersecurity strategy should do so. Cyberattacks are going to continue to be a major threat to UK businesses and these businesses need to respond accordingly with sufficient budget. A successful cyberattack has the potential to cause serious ramifications for a business, including both financial and reputational damage. Now is the time to take this threat seriously and get prepared.”

 

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