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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.

Technology

PASSWORDS, BIOMETRICS AND BEYOND

By: Hicham Bouali, Pre-Sales Director EMEA of One Identity, a specialist in identity and access management

 

At any given moment, millions of acts of authentication are performed around the world. Most often, by entering a password. More and more, however, are performed with biometrics or with the help of a unique object, specific to the user. And it’s not only humans who authenticate themselves: machines are doing it on a massive scale, too.

 

How did it all start? And where does it lead us?

In its simplest form, authentication is about proving a user’s identity. And the easiest way to do this is, of course, to agree on a “secret” shared between the user and the machine. This is the principle on which the good old password is based on, and the technique that was implemented by the first multi-user machines installed in universities (the first microcomputers, considered as single-user, obviously did not need this).

But quickly, the password showed its limitations. What happens when it is stolen? How can we be sure it cannot be easily guessed? Why do we do when users choose weak passwords or forget them?

To overcome these limitations, a whole market of dedicated tools has developed, from the password safe (which allows to store passwords on one’s computer in a secure way) to HSMs (electronic boxes that generate highly random passwords), through SSO (connecting to different applications with a single password). Organisations started adopting these tools and developed their own policies around passwords.

As long as this remained limited to the walls of the company, it was still possible to manage a wide range of support solutions. But when the Web opened the floodgates, things became more complex: millions of users were able to access tens of thousands of online services asking for a password. Databases containing several million passwords could be stolen and identities could be usurped. And criminals were very quick at realising that, for the sake of convenience, Internet users sometimes reuse the same identifier, which accentuates the problem.

In short, the Internet has clearly shown that the reign of the password is coming to an end.

The end, really? Not exactly… Because the password still maintains two great advantage: the ease of use and its relative ease of implementation.

However, the Internet ecosystem has started to look for alternatives. With the advent of social networks, a few web giants have notably tried to propose a common authentication standard, which would allow anyone with an account on a social network to authenticate on other websited (the principle of federating identities using standards such as OAuth). It doesn’t quite solve the problem, but it does benefit ease of use.

At the same time, multi-factor authentication, which is still considered one of the most effective means of strengthening passwords, has emerged. By sending the user a very short-lived validation code (OTP : One time Password), by SMS for example, we ensure that even if the password has been stolen, the attacker will not have access to the associated phone and will therefore be unable to complete the authentication process. This worked until we realised that text messages were never designed for this, and the industry now turns almost exclusively to validation codes based on time synchronization with the server, generated on a hardware device such as RSA SecurID or a software device via a smartphone application.

Smartphone manufacturers have also (finally) managed to make biometrics authentication available and usable by anyone by introducing fingerprint and face recognition. This made it possible to equip a large part of the population with a second, truly powerful authentication factor. The password is thus still present, but solidly reinforced by biometric authentication or a single-use validation code. Progress has been made…

But in all this history, the industry has mostly adapted on a case-by-case basis, trying to overcome the weaknesses of the password. What is still missing is a true modern authentication standard that is easy to use, reliable and accessible to all. This standard could well be FIDO (Fast Identity Online), developed since 2012 by a consortium of tech giants including Amazon, Google, Facebook, Paypal, as well as Visa and Wells Fargo.

FIDO’s objective is not to make the password disappear (it is understood that it will always be useful) but to raise the other means of authentication to the same level of simplicity of deployment, in order to allow easy switching from one to the other. FIDO supports the use of passwords as well as biometrics (facial and digital), voice recognition and physical keys. Today, FIDO solutions enable strong authentication on a website or application at the touch of a button on a USB key inserted on the computer, while at the same time authenticating the service itself to protect users against phishing attacks.

Why is it so important to make all other authentication methods as easy to deploy as the password? Because during all this time, things were changing incredibly rapidly: applications were increasingly migrating to the Public Cloud, the perimeter was gradually disappearing, employees were increasingly working from unsecured networks with unsecured devices… So, it no longer makes sense to have to choose a single authentication method. Companies must be able to adapt dynamically to the authentication context (by taking into account the user’s identity in a broader risk analysis) in order to choose the right method at the right time.

The future of authentication is no longer in the methods themselves: the industry has made peace with the good old password and no longer intends to make it disappear at all costs, provided they have the choice! Rather, the future lies in the dynamic management of identities and authentication processes at the enterprise level, in a pragmatic way. Because yes, the password still has its use).

And that’s a new frontier!

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Finance

DIGITAL FINANCE: UNLOCKING NEW CAPITAL IN DISRUPTED MARKETS

Krishnan Raghunathan, Head of Finance & Accounting Services at WNS, explores how a digitally transformed finance department can give enterprises the ability they need to improve cash flow and revenue through better use of data and improved analytics-driven visibility.  

Businesses everywhere are scrambling to recover lost revenues and protect cash flow. But as countries globally grapple with a dreaded second wave of the pandemic, imposing far more stringent localised lockdowns and new restrictions, it is set to be the hardest winter in living memory for many sectors.

The likelihood of winter peaks, so often the saviour of sectors such as travel and hospitality, benefitting businesses is diminishing rapidly. While many have pivoted to a greater or lesser degree, few have been able to offset the impact of falling revenues on cash flow. Even retail, riding an e-commerce boom in many regions, is finding itself in choppy waters, with 17 percent of consumers switching brands due to the economic pressures and changing priorities caused by the pandemic.

As one McKinsey article notes, “With some companies losing up to 75 percent of their revenues in a single quarter, cash isn’t just king – it’s now critical for survival”. Where then do businesses find new sources of cash to sustain their operations through the coming months?

 

Tapping Overlooked Cash Opportunities

Krishnan Raghunathan

For many, the answer could depend on whether they have digitally transformed their finance department. Why? Because many organisations are sitting on unidentified opportunities, funds that could be vital in shoring up businesses over the next few months or plugging the gap between operating costs and government bailouts. Yet those that have been slow to start their digital transformation journey are at a disadvantage;. At the same time, it is possible to identify these hidden seams in an analogue organisation, the process is time-consuming, manually intensive and, without the right digital tools, prone to human error.

Where deploying digital tools helps is by bringing speed, automation and reliable data to the fore. Connecting them with digital finance and accounting systems can give businesses clear insights into how money is being spent, where wastage is occurring, and where opportunities for optimisation exist.

It might be something as simple as automating the accuracy checking, issuing and chasing of invoices and late payments. This could reduce errors and invoice disputes and ultimately lead to faster payments. Accuracy and organisation are also important in billing – better records enable faster billing for work completed, and in turn, should deliver quicker payments.

It could also be around having the ability to review the supply chain and procurement data and identify where a supplier is subsidising a larger customer’s product line through drawn-out payment terms, or where a variety of vendors are on different terms across the business. Using that data and overall knowledge of the business to negotiate better terms that work for both supplier and customer can create new opportunities. It could even be to identify late-paying customers, determine the reason for late payments, and use that intelligence to develop products or financing solutions that continue to support those customers (and improve loyalty) without increasing the burden on the balance sheet.

 

Generating Reliable Insights for Faster Decision-making

To do any of these manually would take months, generating data slowly that would quickly go out of date. But digital finance departments have evidence they can trust to inform business decision-making. That’s because old, manual processes built around Order-to-Cash lack the flexibility and agility that businesses require in today’s markets. The fact is that even before the global pandemic crisis, the pace of digitisation across all sectors was demanding new approaches to finance and book balance.

The opportunities are significant – from cognitive credit and improved forecasting accuracy to enhanced customer analytics. All use similar tools, based on artificial intelligence and quality, trusted data. Cognitive credit can be deployed to quickly make decisions on whether to advance or restrict credit, based on individual company positions and available data. Doing so enables businesses to either capitalise on opportunities (for instance, agreeing credit for a supplier that has run out but is a supportive and integral partner) or avoid risk (in the cases where a business might be in administration).

With more accurate forecasts, businesses can better manage their currency purchases and deposits, selling currency that is not required or buying more where predictions identify an upcoming demand.

It is the same with customer analytics – with a greater understanding of customer needs, businesses can make decisions based on the right mix of the product (and how it meets demand) and supply chain suitability (such as production costs and location in relation to customers).

In many ways, the events of the past year have accelerated the process. In doing so, the problem is the pandemic has also accelerated the speed at which failure to act can lead to obsolescence. Therefore, it is vital that businesses, and more particularly their finance and accounting departments, kick start their digital transformation. This will enable them to deploy the tools and analytics that is needed to capture data, generate insights and drive fast, accurate decision-making to uncover previously untapped sources of cash and reverse revenue degradation.

 

The Importance of Digitally Enabled Finance Teams

Forward-thinking CFOs have already begun the process of digitising their departments, but for those that have been slow to start, now is the time to push forward. It is only through digital tools and analytics that finance leaders can identify both the internal and external opportunities to recover revenue and improve cash flow. Whether that’s releasing working capital, minimising revenue loss and accelerating revenue recovery, reducing total cost of ownership or enhancing customer retention – only digitally enabled finance teams will be in a position to capitalise and, ultimately, bolster business performance during what will be a trading period like no other.

 

 

About the author: Krishnan Raghunathan

Krishnan Raghunathan is the head of Finance & Accounting (F&A) practice and operations at WNS. He also leads the international delivery locations in China, Costa Rica,  Spain, Sri Lanka, Romania, The Philippines, Poland and USA.

Prior to this, Krishnan was Chief Capability Officer for WNS, in that role he headed Horizontal practices across Finance & Accounting, Customer Interaction Services and Research & Analytics, Transformation & Process Excellence, Program Management (Transitions) and Solutions development.

He has more than 27 years of experience across Finance & Accounting, Business Process Management, Sales Solutions and Capability functions including 7 years in Accounting practice.

Before joining WNS in 2013, Krishnan led several challenging roles at Genpact, supporting strategic deals and consultative selling. In addition, Krishnan was also the business leader for a number of industry verticals at Genpact, including hospitality, transportation, logistics, media and professional services

Krishnan is a Chartered Accountant, a Certified Six Sigma Green Belt and a trained Six Sigma Black Belt

 

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