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USING AI TO DETECT MONEY LAUNDERING NETWORKS

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.

Finance

WE NEED FINTECHS NOW MORE THAN EVER

STRUCTURED DATA

Lubaina Manji, Senior Programme Manager, Nesta Challenges

 

Whilst the sun is far from setting on the COVID-19 pandemic, predictions and hopes for a new “normal” are shimmering on the horizon.

 

Amid the trail of devastation left by the virus, there has to be some semblance of change and positivity to be taken. One such shift is the increase in digital services usage which poses a huge opportunity for our fintech community. Confinement has forced even the more sceptical of us to dabble in digital, and embrace how it has made many everyday tasks more easy and convenient.

 

Online and mobile banking has been helping many people stay on top of their finances for some time. Research conducted by Open Up 2020 Challenge last summer found half (48%) of people would like to use online tools and apps to help them manage their money[1].

 

Then along came a global pandemic that has undoubtedly forced the hands of even the more sceptical to log on, download and transact – quickening the pace of long-lasting change in terms of how we manage our money. Recent figures from deVere Group suggest the virus is behind a 72% rise in the use of fintech apps in Europe[2]. Never before have we been so reliant on technology in maintaining some sort of normalcy and in helping us continue day-to-day tasks, like everyday banking.

 

Another unfortunate byproduct of protecting communities from the virus means many people have been left out of work and with less or no income. In times of financial strain, the need for people to engage with their finances – be it budgeting, saving or shopping around for better deals – is far greater.

 

Issues of trust in traditional banking services and a lack of awareness of the helpful money management services available are some of the barriers preventing people from taking more control of their finances. But the solutions made possible through open banking can provide people with a lifeline to build their financial resilience and better manage their money.

 

Open banking has the potential to revolutionise financial services, by giving people control over their financial data in order to access innovative products tailored to them. Since it launched in 2018, open banking technology has opened the door for new fintech innovators to create cutting-edge tools designed to help people better manage their money – from budgeting, debt management, comparing and switching banks to automating savings and more. These could have a significant impact – it is estimated that UK consumers could gain as much as £12bn over the course of a year from open banking-enabled tools[3].

 

So far, it’s been effective – the UK FinTech’s State of the Nation report[4] totted up more than 1,600 fintech firms in the UK in 2019, whilst predicting this could more than double by 2030. Figures from the Open Banking Implementation Entity showed there were 243 regulated providers, 169 third party providers and 74 account providers as of April 2020[5]. The UK adoption rate of fintech is 42% – higher than the global average of 33% – making it ripe for opportunity[6]. Coupled with lockdown restrictions creating greater dependence on technology – including ATM cash withdrawals falling by half[7] – fintechs are well placed to be part of the solution – and offer help to those struggling to manage.

 

With more than a fifth (21%) of the adult population saying financial stress is having a bigger impact on their mental wellbeing than physical health concerns during the crisis, and a quarter more stressed about money than usual[8], fintechs can be part of the support available to them.

 

However, in order to fully realise the opportunity we need to ensure budding entrepreneurs with bold ideas have the means to turn them into reality. Nesta Challenges exists to design and run challenge prizes that incentivise people to help solve pressing social problems that lack solutions. Through our Open Up 2020 Challenge we are supporting 15 fintech finalists to develop their solutions to enable more people – particularly those underserved by traditional financial products – to manage their finances better, whatever their circumstances.

 

Of the 15 finalists, some offer app designed to help people budget,, save, switch and invest – aided with alerts and notifications that allow people to stay on top of their finances and make their money work harder for them for the long term. For example, Cleo is an AI financial assistant that is already helping more than 3 million customers monitor their spending, budgeting and saving, while Moneyhub empowers people to do more with their money by offering actionable insights from a review of all of their accounts.

 

Some of the apps are designed for those with more specific circumstances, such as Mojo Mortgages, which analyses income and transaction data for first time buyers to produce mortgage affordability scores and savings recommendations if they aren’t quite ready to apply. Finalists Portify and Wagestream cater for workers with irregular earning patterns.

 

As well as monetary grants, Open Up 2020 Challenge provides these companies with non-financial support and promotion to help them on their way to achieving their full potential – which in turn helps them reach many people to help them achieve their monetary goals.

 

While COVID-19 has created personal finance headaches for many, it has been inspiring to see how quickly fintechs have been able to innovate and develop digital solutions that help solve these problems and equip people to better manage their money.

[1] Open Up 2020 Challenge

[2] Forbes 2020

[3] Open banking Consumer Priorities for Open Banking report

[4] UK Fintech State of the Nation

[5] Open banking Highlights April 2020

[6] UK Fintech State of the Nation

[7] https://www.link.co.uk/about/statistics-and-trends/

[8] Open Up 2020 Challenge

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Technology

THE “MOBIUS STRIP” OF CYBER SECURITY

By Miles Tappin, Vice President, EMEA at ThreatConnect

 

Over the last few years, cyber criminals have become more agile and possess a higher quality of skill than ever before. However, these skills come at a cost to industries worldwide. According to the Allianz Risk Barometer 2020, companies now see cybercrime as the biggest threat to their business, taking the top spot for the first time and ranking above threats such as climate change, natural disasters and market developments.

With digital threats remaining front of mind for the C-suite, more needs to be done to ensure businesses are protected from the powerful effects that cyber crime can have on the bottom line, corporate reputation or day-to-day operations.

 

The rise of the “business savvy” hacker 

Awareness of digital threats is rapidly accelerating among businesses, but many aren’t prepared to tackle the mounting threats they now face.

According to David Ferbrache, Global Head of Cyber-Futures at KPMG and Chair of the National Cyber Resilience Board for Scotland, organised crime has become a lot less “crude” than it used to be. In essence, criminals are now becoming “business savvy” and are even undertaking reconnaissance missions to work out exactly who the best target is and how much they can extort.

Gone are the days of “hackers” being people who lurked in darkened rooms, anonymously terrorising the internet. They now want to be known as players in an evolving landscape who are taking advantage of your organisations’ pitfalls and planning far in advance to inflict the most amount of damage possible for maximum impact.

The main worry for the C-suite is that cyber criminals are getting smarter. They’re continuously learning from previous attacks, sharing insights and using this to exploit new vulnerabilities using emerging forms of technology. This continuous feedback loop is enabling them to act quicker.

For example, if a hack highlights a potential weakness, they will then target it in their next assault before organisations have a chance to respond. It becomes an ongoing cycle for the attackers. If the weakness isn’t fixed in time, then there is no doubt that it will continue to happen. Much to the dismay of organisations.

 

Threat intelligence informing operations

It’s long been argued that threat intelligence should inform operations when it comes to cyber security. This allows organisations to quickly identify threats and false flags, so security teams do not waste their time chasing down non-malicious communications. It should be noted that intelligence does not exist for its own sake. Intelligence, in particular threat intelligence, specifically exists to inform decisions for security operations, tactics and strategy. However, this relationship is not a one-way street.

Intelligence and operations should be cyclical and symbiotic. Intelligence informs decisions for operations resulting in actions being taken based on those decisions. Those actions, including clean-ups, further investigations, or other mitigations will create data and information in the form of artefacts. This includes lists of targeted or affected assets, identified malware, network-based indicators of compromise and newly observed attack patterns.

In turn, these artifacts can be refined into intelligence that can inform decisions for future operations. While some organisations do not have a formally defined intelligence function on their team, the concept of using what you know about the threat-space to inform your operations exists in all organisations. Regardless of whether an explicitly named threat intelligence analyst employee is on the payroll, the relationship between intelligence and operations is fundamental and present in all security teams.

 

Enter the “mobius strip”

With security risks and attacks set to increase year-on-year and the average annual cost to organisations ballooning, companies need to explore how they can make greater use of threat intelligence to respond to the new barrage of threats.

Threat intelligence may be the catalyst for taking an action or starting a process and informing how the process and decision making is done throughout. As threat intelligence drives your orchestrated actions, the result of those actions can be used to create or enhance existing threat intelligence. A feedback loop is created — essentially threat intelligence drives orchestration and orchestration enhances threat intelligence.

Increasingly, cyber security programmes are operating like a “mobius strip”, a continuous loop where intelligence informs operations and insights from these operations are fed back and form new intelligence. The “mobius strip” will prevent hackers in the long-term. By sharing important data between intelligence and operations it denies hackers the upper hand. Providing context to indicators during incident management is crucial to understanding what you might be dealing with and where it’s been seen before. At the same time, adding new intel generated from an incident or case back to your threat repository takes information that’s very relevant to your organisation and makes it available for future analysis.

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