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AI VS. THE CROOKS: CAN MACHINES BEAT THE FRAUDSTERS?

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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.

 

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Business

When it comes to innovation, ignore your CEO and listen to your customer

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 By Alex Hammond, Partner, Airwalk

 

At its core, the 2008 financial crisis was a result of banks incorrectly managing risk, alongside regulators who were largely asleep at the wheel. As a result, regulators today are far more alert and apt to police industry developments. Likewise, bank leaders, wary of public perception and the fear of failure, are taking a cue from their risk, security and compliance teams and avoiding risk at all costs.

This environment has inadvertently slowed down the progress of innovation with financial service providers (FSPs) using risk and regulation as an excuse to not innovate. In reality, regulation merely provides oversight and ensures guard rails are in place to protect the bank and its consumers. Halting innovation is therefore more likely to have the opposite effect; causing regulation headlines down the line as banks will not be able to provide data transparency when an inevitable failure occurs.

FSP IT leaders are generally aware of this risk, but with more hoops to jump through and added complexity, many opt to only pursue major digital transformations or ‘big-bang’ opportunities. In some ways this is a hangover from the era of premise-era technology, when it was safer and more cost effective to upgrade systems every five (or even 10!) years than to continually innovate.

This is what makes cloud technology, data process automation and product-led innovation such game-changers.

Alex Hammond

Cloud-enabled continuous innovation

When it comes to implementing a programme of on-going innovation, cloud has completely transformed the process. For example, cloud service providers have off-the-shelf SaaS offerings that FSPs can use on a consumption model, test and see what happens. This eliminates the need to go out to market and procure a product or invest heavily in building their own. Utilising these services lets banks try new innovations at a significantly lower cost, at a smaller scale and allows them to throw things away with no regrets – something that is very difficult to do by themselves.

Furthermore, with access to cutting-edge technology and services, banks are no longer restricted to what they are capable of building internally. Continuous experiments and improvements are the gateway to innovation.

Technology to remove risk

Process automation also has an important role to play as it can remove a lot of risk in the innovation process. With automated processes and code there is much less room for manual error – the main culprit causing systems to fall over in the first place. By becoming technology-led and automating processes, delivery and operation, risk can be largely eliminated.

Establishing this baseline of components and processes in a stable and secure position puts you in a much better place to innovate and try new things. Without this foundation, the process of securing and testing will need to be repeated at the beginning and at every phase of any innovation process, making each iteration more complex and expensive.

A product-led proof of concept approach

FSPs must resolve their obsession with big-bang innovation to become more agile and iterative. Many organisations claim to do this, but their iterative development takes place internally prior to a large final release with no ongoing development. This may take months or years to bring to market – at which point the need may no longer even exist.

A clear opportunity to utilise proof of concepts and minimum viable products is being missed but this is just not in FSP’s psyche. The concept of generating the smallest possible development, testing it with the smallest number of customers and then scaling from there is just not the way these organisations currently operate. Yet, introducing incremental innovations to a limited population, allows banks to prove that it is something that people want. If not, it can be killed off before it eats up too much investment.

Instead, FSPs must become more product-led where success is based upon customer outcomes. This means being more customer focused, understanding their problems and desires and then delivering solutions to these. These organisations rarely leverage customer inputs and developments are assumption-based. There is no emphasis on validating things before they are done. Ultimately, these organisations must focus on their customer rather their senior executives, or their opinions.

FSPs, like every other industry player, need to evolve and keep pace with consumer expectations as digital services evolve in other areas of their lives. Unfortunately, technology progress generally does not move forward as quickly in financial services.  But by becoming cloud enabled, automated and product-led, banks can focus on incremental developments. In doing so, they become more agile and better able to drive forward innovation that is accepted by regulators and genuinely valued by customers.

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Banking

Why traditional banks need to embrace the agility of fintech competitors

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Paul Higgins, EMEA Banking Lead, Mendix

 

Tech has long played a role in the finance space. The legacy applications running on mainframes at banks hold upwards of 50 years of business process and regulatory compliance evolution – that represents enormous complexity but also significant value in a highly regulated industry. However, that advantage of experience and stability is being outweighed by the faster innovation cycles and lower costs that cloud-native fintechs and neo banks enjoy.

Admittedly, fintechs have been hit hard in the current macroeconomic climate, with some valuations declining dramatically. But the long-term outlook is that fintechs in Europe continue to gain relevance. According to a McKinsey paper, at least one fintech ranks among the top five banking institutions in each of the seven largest European economies, as measured by GDP.

Given the accelerated demand for innovative business solutions geared to the digital-first economy, traditional banks cannot afford to lag behind the emerging crop of challenger banks and new fintech players. In addition, the standardization of services that we are seeing in the sector means that banks must find new ways to differentiate themselves from competitors.

What can the traditional finance players do to survive and thrive in this new world? The key lies in embracing the agility of their fintech competitors.

Fintech vs traditional banks

Fintechs often home in on a single-use case, adopting the latest technology to create a focused, best-of-breed product for customers. This innovation and laser-focus has led to the emergence of fintechs with multi-billion valuations, like Stripe, Klarna, and Revolut. Although offering different products, they share a common goal of providing a great experience to their customers and users – Stripe for merchants, Klarna for online retailers and their customers, and Revolut for retail banking – with no sign of a paper process anywhere. And they iterate and bring improvements to market at a rapid pace.

That poses a major challenge for traditional banks. The legacy applications they operate come from a bygone era where yearly release cycles were the norm – compared to the monthly, fortnightly, or even weekly release cycles common now. The business lines at traditional banks identify the trends and opportunities, but simply cannot react fast enough to capture the potential.

There is a real risk that without the agility to quickly bring new products to market, banks will continue losing customers, especially younger generations, to fintechs. An approach is clearly needed, that provides flexibility across the business and application landscape, enabling them to integrate innovative technologies and match the pace of change we are witnessing.

Low-code application development increases agility

Adopting low-code across the enterprise has emerged as a crucial solution to meet the challenges posed by the digital-first economy. Low-code breaks down traditional silos of business and IT functions through its first and most important principle: model-driven development. Using a visual model to build an application gives both technical and business professionals a common language to discuss their goals and needs. As a result, cross-functional or fusion teams develop solutions that are relevant and powerful. Low code also helps automate much of the application development, thus reducing errors and accelerating time-to-market and ROI.

Low-code application development unburdens IT

In full-code application landscapes, up to 7 of every 10 employees in IT are focused on tasks to “keep the lights on.” Whether that’s incident and problem management, making small upgrades to applications, or rolling out patches and other bug-fixes. Which leaves precious few developers to tackle the growing backlog of requests from the business lines.

By comparison, low-code drastically reduces the number of people required to maintain and operate applications. A major factor here is the model-driven development already mentioned.  The combination of freeing up developers to actually work on building applications, and having those developers collaborate closely with business experts has a significant compounding effect – resulting in higher business value in a shorter amount of time.

For instance, in the case of Rabobank, whose direct savings bank served more than 500,000 people, customers were not happy with the interface, leading them to leave the platform.

The IT landscape was complex, with different systems per country, and meeting shifting regulatory and compliance requirements was not easy. So, any upgrade would be a challenge.  Using the Mendix low-code development platform, Rabobank reduced their IT costs for the direct bank by 50% while delivering a far superior customer experience. They streamlined their customer onboarding process and created web and native mobile versions of their savings portal, leading to fantastic scores in customer satisfaction and increased business.

Low-code application development provides a bright digital future

For years the banking and finance sector has been dominated by well-known brands. But according to a recent EY report, 37% of consumers now say that a fintech is their most-trusted financial services brand, compared with 33% who name a bank. Looking at younger generations, 51% of Gen Z and 49% of millennials named a fintech as their most-trusted financial brand – a sign of incumbent brands’ struggle for relevance with younger audiences. Players in the banking space who leverage new technologies, making their products, strategies, and services relevant to customers’ needs will lead the competition.

As the sector continues to evolve, allocating resources to increase innovation, agility, and efficiency should be a priority for banks. Low-code presents financial institutions the opportunity to combine the strengths of an incumbent – the experience and expertise, and a user base most fintechs can only dream of – together with the agility of a start-up.

 

https://www.merriam-webster.com/words-at-play/home-in-or-hone-in

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