Konstantin Bodragin, Business Analyst and Digital Marketing Officer at Bruc Bond
Over the last couple of decades, AML has taken centre stage in the banking world. Nowadays, AML, shorthand for anti-money laundering, drives strategic planning and organisational structuring. AML concerns keep many a manager up long into the night, as the risks are huge, the penalties for infractions potentially devastating, and the criminals – especially in the era of COVID-19 – ever more enterprising. While the prevention of money laundering is paramount, the weight and risk faced by financial institutions may feel onerous to many. Luckily, the banking landscape is changing rapidly, with automation and AI making the burden significantly lighter to carry.
Banks and financial institutions face a two-pronged problem. On the one hand, the pace of digital payment is growing exponentially. Much of the world’s trade is now conducted through purely digital conduits. But it’s not only the volume of digital payments and users growing, so is the speed of transactions, with instant payment systems being deployed around the world.
The increases in speed and volume are of course good news for the bottom line, but require significant resources to handle effectively. Resources that many in the banking industry are struggling to provide adequately. The industry is shrinking rapidly, with bank closures, mergers & acquisitions, and a massive reduction in the workforce dominating headlines in the last decade. COVID-19 has only accelerated the trend, with bank after bank announcing imminent layoffs and reductions in trading. With the squeeze on resources, many banks would have struggled to keep up with the increased workload regardless of any other constraints, but here they are faced with the second prong: the complexities of AML.
AML regulations have grown thick and convoluted in recent decades, and with penalties as severe as truly massive fines and personal liability for offending compliance officers, it is taken extremely seriously. And for good reason. Fraudulent and criminal activity is costing the global economy many billions each year, with the lighter end of the spectrum meant to merely enrich the perpetrators, while at the other lies terrorist financing and socially damaging criminality. Nevertheless, it is a significant strain on banks’ already constrained resources, directly at odds with the growing pace of global digital trade.
To alleviate these pains, bankers and financiers of all varieties are scrambling to adopt the newest technologies to combat money laundering effectively, efficiently and with minimal costs. For this, AI seems to be the answer, and everybody wants a piece of the action. In 2020, you would struggle to find a fraud prevention company that doesn’t have the words ‘AI’ or ‘machine learning’ somewhere in its description.
Machine learning, one of the tools underpinning the AI fight against fraud, means the use of algorithms and statistical models to allow computers to perform tasks without specific instructions. In the context of payments, this means allowing computers to make decision related to AML compliance with no human intervention. While letting go of control is a scary prospect for many a financier, it may be the only right thing to do for effective AML implementation, both to prevent money-laundering incidents and to reduce the rate of false positives.
Current statistics indicate that for every fraudulent transaction stopped by a bank’s compliance team, some 20 legitimate transactions are prevented from going through by understandably overcautious compliance officers. Not only does this represent a serious hit to the bank’s bottom line, it wastes whatever precious resources are at the team’s disposal.
With current, manual methods, any suspicious transaction needs to be investigated in a process that can take anywhere from an hour to several days or weeks, often requiring the input of numerous team members and stakeholders across several departments. The cumulative resource drain is palpable, and the end result is that transactions are often rejected not due to any illegality, but because it is simpler, quicker and cheaper to do so. It is simply easier to suspect everyone and reject transactions outright. With AI systems, this process can take an entirely different shape.
Machine learning algorithms learn from human behaviour, create and continuously improve user profiles and use this information to validate transactions. Where this technology shines are with onboarding and transaction verification. Or rather, whenever a known user’s identity needs to be verified. A distinct change in a user’s behaviour is serious cause for alarm and indicates potential fraud, with someone pretending to be a user they’re not.
Unfortunately, AI cannot provide everything we want. When it comes to the cross-border and B2B space, AI is more limited in its uses. While businesses demand increasingly faster account opening and onboarding, the entirety of the process can’t be automated. The problem stems from a difficulty in standardising. Variations in geography, type of business, corporate structures, and even the individuals involved mean that a risk profile must be created for each case individually. Even if the processes could be automated to a higher degree, the risk to reward ratio may mean that the investment in AI isn’t sufficiently attractive. Simply put, financial institutions are rightly anxious about an automated system messing up in complex cases that could lead to massive fines or worse.
Moreover, there exists a question of accountability. When a decision is made by AI, how are you then able to find the exact reason behind why a transaction is not stopped when it should have been – other than to blame it on the algorithm? Using AI makes it very difficult to audit payments, as the fuzzy logic of Machine Learning is almost entirely obscure to us humans.
In short, yes, AI and automation are providing a much-needed breathing room for banks, financial institutions and fintechs looking to alleviate some of the AML burden. However, they are no panacea. Real-life, human bankers will stay with us for a while longer. And for those looking for banking with a friendly face, that may not be such a bad thing after all.
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!
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
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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