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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.

Technology

Why anti-spoofing fingerprint technology is essential for the continued growth of digital payments

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Anthony Eaton, CTO, IDEX Biometrics

 

The digital payments revolution is being driven by consumer demand for ever increasing convenience. This is leading the global digital payments market towards a value of US$204.1 billion by 2028. However, along with increased convenience, comes an implicit expectation to provide higher levels of security, especially when paying with contactless cards and digital wallets.

According to McKinsey, electronic payments are growing at twice the rate of GDP in North America and Europe. This expanding market has the fintech sector overstretched as they try to address operational risks without hampering customer experience and face increased fraud control expectations. If fintechs struggle to implement effective controls, they are likely to see heightened regulation in the future, which in turn can negatively impact consumer experience.

Amid this burgeoning market, fraudsters are continually looking for new vectors of attack. UK Finance’s 2021 Fraud Report showed that fraud losses on UK issued cards totalled £574.2 million in one year alone. To counteract such fraud, card issuers and digital wallet providers are deploying biometric fingerprint technology, which itself is evolving year-on-year to offer ever-increasing security levels.

The front-door attack

Fingerprint spoofing is considered a front-door attack on the biometric system.  It involves applying a fake finger, or so-called spoof, to the fingerprint sensor. When biometrics were first introduced on the iPhone in 2014 they did not deploy adequate anti-spoof technology. As a result it took just 48 hours before German hackers, the Chaos Computer Club, announced they had bypassed Apple’s new TouchID system with a fake fingerprint.

Attacks of this kind impact both consumer and industry confidence. As such, defending against this has been at the forefront of the emerging biometric payment card standards. Korean technology giant Samsung recently announced its entry into the biometric smart card space, and anti-spoof technology was at the centre of its story. This positioning reflects the need for added security and peace of mind in fraud prevention.

Anti-spoof: the heart of any biometric system

Anti-spoofing technology prevents fraudsters from defeating the fingerprint authentication process with false credentials. Today, it is used to increase security levels across a range of biometric systems, from smartphones to laptops and airport border control kiosks.

The biometric payment card has a compelling value proposition by bringing the biometric authentication process inside the secure enclave of the payment card’s Secure Element chip. The card’s off-grid nature ensures a much more limited surface of attack, compared with that of a highly connected smartphone. However, the challenges associated with implementing anti-spoof technology on this platform are not to be baulked at. The card has no battery and operates with limited on-board processing power. Without the luxury of the smartphone’s supercomputer-like processor a whole new wave of innovation has been needed.

As card issuers and digital wallet providers start to deploy fingerprint biometric payment cards to consumers, anti-spoofing technology must sit at the heart of their offering.

This can pave the way for a more secure future, from payment to digital and physical access, and to digital IDs and digital currencies.

Striking the balance between security and user experience

It’s clear that anti-spoofing technology must be included by default on biometric payment cards to reduce fraud and instil consumer confidence. But, despite the benefit of its added security it’s crucial to limit any potential impact on user experience. When paying for their shopping, consumers want to know that their card is safe, but more than that, they want to know their payment card will deliver a flawless user experience day-in, day-out.

When it comes to balancing security and user experience on a payment card, new design approaches have been required. The traditional approach to anti-spoof uses Neural Networks and Machine Learning techniques to train an image processing algorithm to detect the subtle characteristics of images captured from fake fingers. This requires an optimised processor and can quickly become impractical in a highly constrained smart card.

A second approach is to increase the security level of the traditional biometric authentication algorithm that matches a user’s fingerprint to the reference data captured during enrolment. This is very much a brute-force approach which, while helping to detect fake-finger attacks, will rapidly degrade user experience.

The optimum approach involves designing the fingerprint sensor, the biometric authentication algorithm, and the spoof detection system together – to all work in unison. Taking such a holistic, grounds-up approach opens up the design of biometric smart cards to new possibilities. Requirements can be met with margin allowing designers to achieve security targets and focus on delivering a flawless user experience.

Ready to fuel digital payment growth

To ensure the continued widespread adoption of biometric smart cards, it is important that all fingerprint biometric sensors are deployed with anti-spoofing technology while being optimised for user experience. Fingerprint biometric cards, when combined with anti-spoof technology allow for higher transaction limits and a faster, more secure transaction experience, while introducing increased obstacles to fraud.

Payment providers save money on fraud refunds whilst also increasing revenue thanks to higher limits and an enhanced customer base due to a secure and trusted reputation. The payment industry is already at a high level of security today. But with financial fraud on the rise, we must constantly improve to be ahead of cybercriminals and improve the customer experience for those using biometric payment services to enhance their lives.

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Banking

Digital Banking – a hedge against uncertainty?

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Ankit Shah, Head of Digital Banking, Apex Group

 

The story of the 2020’s thus far is one of crisis. First the world was plunged into a global pandemic which saw the locking down of people and economies across the world. Now we deal with the inevitable economic consequences as currencies devalue and inflation bites. This has been compounded by Russia’s invasion of Ukraine and subsequent energy politics.

And the outlook remains uncertain. Tensions continue to build between China and Taiwan and inflationary conditions are forecast to continue well into 2023. This uncertainty is impacting everyone, and every sector. And finance is no exception with effects being felt everywhere from commodity and FX markets to global supply chains.

But it’s not all doom and gloom. Rollercoaster markets and an ever-evolving geopolitical situation have made 2022 a tricky year far, but, despite the challenges, digital banking has proven resilient. In fact, the adoption of digital banking services has continued to grow over the last few years, and is predicted to continue.

So, what are the forces driving this resilience?

In an increasingly digital world and economy, digital banking comes with some advantages baked in, which have seen the sector continue to succeed despite the tumult in the wider world. In fact, the crises which have shaped the decade so far may even have been to the advantage of digital banking. Just as during the pandemic, technologies which could facilitate remote working saw a huge uptick in users, so to digital banking is well suited to a world where both people, and institutions demand the convenience that online banking services offer.

And while uptake of digital banking services is widespread amongst retail consumers, a trend likely to continue as digital first generations like Gen Z become an ever-greater proportion of the consumer market, uptake amongst corporate and institutional customers has been slower. This is largely down to a lack of fintech businesses serving the more complex needs of the institutional market, but, in a post-Covid world of hybrid working business, corporate clients are looking for the same ease of use and geographic freedom in their banking that is enjoyed by retail consumers.

This is not just a pipe dream – with the recent roll out of Apex Group’s Digital Banking services, institutions can enjoy the kind of multi-currency, cloud-based banking solutions, with 24/7 account access that many of us take for granted when it comes to our personal banking.

Staying compliant

One significant difference between retail and business accounts however, for banking service providers, is the relative levels of compliance which are needed. While compliance is crucial in the delivery of all financial services, running compliance on multi-million pound transactions between international businesses brings with it a level of complexity that an individual buying goods and services online doesn’t.

For digital banking services providers, this situation is further compounded by guidance earlier this year from HM Treasury – against the backdrop of the Russia-Ukraine conflict- requiring enhanced levels of compliance and due diligence when it comes to doing business with “a high-risk third country or in relation to any relevant transaction where either of the parties to the transaction is established in a high-risk third country or with a sanctioned individual.”

So, can digital banks meet these standards while also providing institutions with the kind of easily accessible, mobile service which retail customers enjoy?

The answer is yes and again, once initial hurdles are overcome, digital banking brings with it features which give it the edge over traditional banking services. Paperless processes, for example, mean greater transparency and allow for better and more efficient use of data. This means AI can be employed to search documents, as well as provide verification. It also means compliance processes, often notoriously complicated, become easier to track. Indeed, digitising time intensive manual process means the risk of human error in the compliance process is reduced.

Digital banking can also better integrate transaction monitoring tools, helping businesses identify fraud and irregularity more quickly. This can be hugely important, especially in the times of heightened risk we find ourselves in, where falling foul of a sanctions regime could have significant legal, financial and reputational consequences.

Cross-border business

Our world is increasingly globalised, and so is business. For corporate and institutional banking customers, being able to operate seamlessly across borders is key to the operation of their business.

This brings with it challenges, which are again compounded by difficult geopolitical and economic circumstances. In recent weeks for example, we’ve seen significant flux on FX markets which can have real consequences for businesses or institutional investors who are buying and selling assets in multiple currencies and jurisdictions. The ability to move quickly then, and transact in a currency of choice, is vital. Advanced digital banking platforms can help – offering automated money market fund sweeps in multiple core currencies to help their clients optimise their investment returns and effectively manage liquidity.

Control admin uncertainty

In times of uncertainty, digital banking can provide additional comfort via customisable multi-level payment approvals to enhance control of what is being paid out of business accounts, with custom limits available for different users or members of a team. Transparency and accountability are also essential, with corporate clients requiring fully integrated digital reporting and statements and instant visibility with transaction cost and  balances updated in real-time.

Outlook

For some, the perception remains that digital banking is the upstart industry trying to offer the services that the traditional banking industry has built itself upon. Increasingly however, the reality is that the pressure is on traditional banks to try and stake a claim to some of the territory being taken by digital first financial services.

With a whole range of features built in which make them well suited to business in a digital world, digital banking is on a growth trajectory. Until now, much of the focus has been upon the roll-out of services to retail consumers, but with features such as automated compliance, effortless international transactions and powerful AI coming as standard for many digital banks, the digital offering to the corporate world looks increasingly attractive.

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