Finance

Preventing fraud and detecting money laundering in real-time

Published

4 months ago

June 8, 2023

admin

Mathew Hobbis – Chief Architect FSI, Solace

The number of payment channels has grown exponentially. The time it takes to settle a transaction has gone down from days to minutes. Traditional banks have had to move from a couple of channels to potentially 10-15 within their organisation. The more channels, the more vulnerable the system becomes to fraudsters and criminals. The two big challenges for financial institutions right now are payments fraud at the consumer end of the spectrum, and the growing threat of organisational money laundering.

Here’s the conundrum. Modern financial organisations have to mitigate against such criminal activity for the safety of their users and its own reputation. But they must do this without adding any friction into the payments process that would put off or dissuade users of their services.

They need a solution that can not only keep pace but can carry out the additional checks in real-time across systems that often encompass legacy, on-premises deployments, as well as modern container deployments, and public cloud for AI and ML capabilities. In the real-time world of today, this can only mean using the new generation of event-driven architecture (EDA).

The more channels, the more opportunities for payments fraud

McKinsey charts a rise in fraud in a recent article series: “Skyrocketing levels of fraud, enabled by the accelerated adoption of digital commerce and the ever-increasing sophistication of fraudsters, have overwhelmed traditional controls in recent years. This surge has led to increased fraud losses and damaged customers’ experience and trust.”

For retail banks, payments fraud impacts both consumers and their bottom line. The Association for Financial Professionals®’ latest Payments Fraud and Control Survey, underwritten by J.P. Morgan, found 71% of financial professionals report their organisations were victims of payments fraud. Not only do fraudulent payments negatively impact banking customer experience and confidence, the cumulative cost is also large – one recent study by Juniper Research warns online payment fraud losses alone will globally reach $343 billion between 2023 and 2027.

Anti-money laundering (AML) spells the danger of more serious crimes

Money laundering is a major threat for banks because it usually goes hand in hand with serious organised crimes – including drug or people trafficking, weapons dealing or even terrorism.

The estimated amount of money laundered globally is between 2 and 5% of global GDP – and the reputational damage of undetected money laundering can be catastrophic. The Bank for International Settlements also explains “spotting different money laundering patterns is complex, requiring different data points and data sources as well as the ability to connect them across different systems in order to better identify suspicious flows and patterns.”

There are three key areas where technology and event-driven architecture (EDA) can help address these growing threats. The first is the tech to help you better detect. Banking and payments organisations must be able to quickly identify and action these fraudulent or criminal transactions, across all channels. Many are turning to data modelling and Artificial Intelligence (AI) and Machine Learning (ML) that can learn to recognise questionable transactions. But this can be further enhanced with EDA to manage fraudulent and money laundering transactions at scale.

The second issue and challenge for organisations is speed, specifically feeding transaction data, in real-time, to the AI / ML processes which often live in the public cloud. This is where EDA provides the real-time integration allowing legacy core-banking/mainframe systems to communicate with modern micro-service payment frameworks and cloud-based AI/ML for fraud and anti-money laundering (AML).

Finally, they must be able to stay one step ahead. EDA and the Event Mesh allows flexibility in how software components are wired together and flexibility in where they are located. This allows the platform to ‘evolve’, to react quickly and effectively to changes in the financial landscape. Flexibility, or ‘re-wiring’, and platform evolution needs to be a ‘business as usual’ activity as fraud and fraud detection is a constantly evolving game where financial institutions are pitted against criminals. Who can act the fastest wins.

Building a model – it all starts with scoring transactional data and setting triggers

The sort of activities that go into building a fraud prevention or anti-money laundering model with setting trigger points would include: type of transaction vs. is this consistent with a customer’s previous transaction history? Is it in an expected geography? If they travel a lot, then is the time and travel distance between their last transaction and this transaction reasonable? All this data must be fed into the model and assigned a score.

The score also depends on authentication requests. So typically, if you can identify a user together with their mobile phone, banks may pass the transaction because they are comfortable they know who the user is. But if a similar scenario occurs where the user has reached the same score, but there is no biometric data or mobile authentication, then this would be highly likely to trigger a different reaction – blocking or flagging the questionable transaction for escalation.

Now add AI and ML – fraud and money laundering detection starts to get powerful

When a bank has built a database of models, new transactions can then be checked against the models, and given an accumulated score, AI and machine learning then step up to the plate. These technologies, aided by EDA, can make rapid decisions and enable companies to flag abnormal transactions in real-time across all channels.

Layering these data models with AI/ML offers an opportunity for banks to get out in front and gain ground on fraudsters and money launderers. McKinsey research sees “Recent enhancements in machine learning are helping banks to improve their anti-money-laundering programs significantly, including, and most immediately, the transaction monitoring element of these programs.”

To be fully effective, AI/ML needs a big data set. They can only make decisions based on access to historic datasets. So, the first thing a bank has to do is to ‘train’ the model by buying data or scraping from its own historical datasets. And then the model runs through several fraudulent transactions, so it is now ‘trained’ on what a fraudulent transaction looks like. The objective is to build an understanding so AI/ML can pick out the right (fraudulent) activities.

Event-driven architecture helps police fraud and money laundering faster than ever before

Ideally, banks should build one model set for fraud and one model set for money laundering – then implement both models across all transactions and payment channels. And this is where event-driven architecture (EDA) enables them to leverage their fraud and money laundering data models and use AI/ML technology in real-time across an ever-expanding number of payment channels.

EDA allows banks to build an enterprise IT architecture that lets information flow between applications, microservices, and connected devices in a real-time manner as events occur throughout the business.

Meet the event broker who understands it all

EDA works with a middleman known as an event broker, which enables what’s called loose coupling of applications. This is essential because it means applications and devices don’t need to know where they are sending information, or where the information they’re consuming comes from. But the event broker does.

So, in the event-driven world, a bank just has to make sure a payments channel just sends the right event to communicate with the fraud detection or the anti-money laundering system and receive the same events to get the “yes or no” back.

The alternative is not really an option

It’s a much easier integration than trying to do this via standard REST APIs – which becomes a lot more challenging and will need to be built differently for every different channel a bank has now, plus any new channels. This means banks may have to change models based on not only changes in user behaviour, but changes driven by new products and services or to counter new types of fraud or money laundering.

With standard REST APIs – every time a bank adds a new channel, it has to change the way anti-money laundering and fraud systems work, because they have to know about this other channel. In the event-driven world they don’t know, don’t need to know – and they don’t care!

Banks can accurately support a high volume of transactions in the quickest response time, balance transaction authentication and authorisation with fraud detection without decreasing customer satisfaction, and route events securely across the whole payments ecosystem with efficiency.

A platform for the future – EDA opens the door to manage technical debt and quickly introduce new channels

EDA also provides a platform for the future – allowing banks to innovate outside of just countering fraud and money laundering. EDA will help traditional banks compete in the new world as they need to deliver products and services faster in order to compete. A large bank, with its legacy systems, can now compete against an online mortgage lender—and deliver a broader portfolio of products to customers with more speed.”

Yes, newer fintech market entrants have significantly less technical debt than traditional financial institutions. Imagine a new FX rate provider that can provide payments to every country and give customers the best FX rates. Everything is built on a modern infrastructure anyway – there is no legacy core banking app, and everything is microservice, as everything is in the cloud.

But EDA as an approach to enterprise IT architecture can help traditional banks introduce new services and link applications quickly and at scale, ensuring they can match these agile competitors and provide customers with the instant kind of feedback they seek from their banking services, while not being held back by large volumes of existing technical debt.

EDA – keeping financial institutions one step ahead

The challenge for larger banks is to move more towards real-time – even with a large amount of technical debt. EDA not only provides the springboard to payment modernisation; it also ensures a proliferation of payment channels does not come at the cost of increased fraud and money laundering.

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Business

How can law firms embrace automation and revolutionise their payments?

Published

18 hours ago

September 28, 2023

editorial

Attributed to: Ed Boal, Head of Legal at Shieldpay

Once again, AI is dominating international headlines. This time, it’s due to a closed-door meeting this month between tech leaders and US senators to discuss the technology’s regulation.

AI and automation isn’t just for the likes of Big Tech. We’re seeing predictive and automated technologies transform almost every sector and the legal industry is no exception. In fact, recent research from HBR Consulting found that 60% of law departments had implemented a legal data analytics tool last year and more than 1 in 4 indicated they were using AI for at least a single use case.

However, adoption isn’t without its challenges. Reticence remains among some and there’s also the danger of ‘transformation fatigue’ slowing real progress. If law firms want to reap the many benefits of automation – including revolutionising their payment processes – these challenges need to be carefully considered and thoughtfully addressed.

An area of great opportunity

Often seen as conservative, the legal industry has been gradually warming up to the idea of automation and technology.

While some pioneering firms have been quick to embrace automation tools, others remain cautious about disrupting their established workflows. As we navigate this landscape, it’s clear that certain areas of legal services are ripe for innovation.

One area is contract management. The process of drafting, reviewing, and managing contracts has traditionally been time-consuming and prone to human errors. Automation can alleviate these pain points by streamlining the entire lifecycle of contracts, from creation to renewal, thereby enhancing efficiency and reducing risks.

Another promising domain is legal research. Thanks to advancements in natural language processing and machine learning, legal professionals can now leverage AI-powered research tools that analyse vast volumes of legal data to provide accurate insights and case precedents swiftly.

But, while progress is undoubtedly being made, the legal sector still lags other sectors when it comes to innovation.

What’s getting in the way of progress?

This isn’t always down to a resistance to change. Often, it’s a result of firms spreading their resources too thinly across numerous technology initiatives.

Ed Boal

Attempting to tackle everything at once can result in ‘transformation fatigue’, where the benefits of individual innovations get diluted – leading to frustration and slower progress.

Before legal firms embark on digital transformation projects, a critical first step is introspection. Recognising and acknowledging areas where legacy processes and manual tasks still hold sway is paramount to optimising the impact of automation.

For many firms, archaic practices continue to consume valuable time and resources, diverting attention from higher value, billable tasks. One often-overlooked area is payments.

Legal firms play a critical role in complex transactions, from M&A and real estate deals to litigation and arbitration payments. The associated admin and processes represent a drain of firms’ time and resources. Spanning everything from collating stakeholder payment details and verifying payee identity to ensuring compliance with Know Your Customer (KYC) and Anti Money Laundering (AML) regulation, this adds unnecessary stress for lawyers – who would rather dedicate their time and expertise to their clients’ legal needs.

The repercussions of such time-consuming financial processes reverberate throughout the entire organisation. Administrative burden weighs heavily on the team, affecting productivity and ultimately, the bottom line: recent research from Shieldpay, surveying the UK’s Top 100 law firms, found that almost 1 in 3 (32%) say KYC collection and verification checks take 4-9 working days.

At the same time, firms are exposed to significant financial risk which can make handling client funds a costly endeavour. Not only are they penalised with fines if found to be in breach of stringent client account rules but firms are also subject to hefty premiums for Professional Indemnity (PI) insurance. No wonder 73% of all legal professionals and 90% of junior law professionals are concerned about the risks and time costs associated with holding client funds.

Revolutionising payment transactions

In short, manual payment processes are more than just an inconvenience for modern law firms. They can damage relationships with clients – who have come to expect a fast, painless and automated payout experience in a digital world – and impede revenue generation by tying up top talent in an endless cycle of paperwork and (unbillable) admin.

So how can firms take the pain out of legal payments?

Fortunately, new payment technologies have emerged as a formidable ally. Third-party payment providers offering solutions for law firms, such as escrow and paying agent services for specific transactional deals, or more embedded payment solutions such as managed accounts (TPMAs) – i.e. outsourced client account functions – offer secure and instant transactions, while prioritising transparency and automation.

TPMAs operate as an escrow payment service in which the third-party – a licensed external payments partner – receives and disburses funds on behalf of a firm and their client(s).

With advanced encryption ensuring data security, working with a regulated payment partner means legal professionals and their clients can engage in financial transactions with peace of mind – while law firms benefit from improved operational efficiency.

And the advantages don’t stop there. Enhanced transparency builds a sense of confidence and trust, while the elimination of manual data entry and repetitive tasks allows legal professionals to devote more time to legal services and fostering stronger relationships with their clients.

AI and automation has much to offer the legal sector. But its adoption must be carefully planned in order to avoid transformation fatigue that risks stalling progress altogether. With typically shallower pockets than Big Tech giants, it’s important for law firms to focus their efforts on specific areas that could benefit from automation, rather than rush to overhaul their entire way of working, all at once. This controlled phase-out is the key to avoiding adoption frustration, seeing a real impact on profits and productivity and setting firms up for real, lasting change.

Business

In-platform solutions are only a short-term enhancement, but bespoke AI is the future

Published

2 days ago

September 27, 2023

editorial

By Damien Bennett, Global Director, Principal Consultant, Incubeta

If you haven’t heard anyone talking about artificial intelligence (AI) yet, then where have you been? Conversations about AI and its advantages to society have been a key talking point over recent months, with advances being made in the generative AI race and ChatGPT opening a whole plethora of possibilities. Many have highlighted the advantages of AI, but notably it’s ability to create human-like content.

But these discussions have only scratched the surface of what AI is capable of doing. It is for far more than just essay writing, adding Eminem to your rave and photoshopping dogs into pictures.

In marketing, we have been using AI for years, for everything from analyzing customer behaviors to predicting market changes. It’s enabled us to segment customers, forecast sales and provide personalized recommendations, having a huge impact on how our industry works.

It is even, for the more savvy marketers of the world, becoming a key tool in maximizing budget efficiency – which is apt, considering over 70% of CMOs believe they lack sufficient budget to fully execute their 2023 strategy.

Now, as AI becomes more intelligent, the number of efficiencies it can unlock continues to rise. Not only can it help brands get the most out of their available resources and identify any areas of waste, but it can also help highlight new opportunities for growth and maximize the impact of your budget allocation.

The trick, however, is to veer away from the norm of using in-platform solutions with a one-size-fits-all approach and create your own, bespoke solutions that are tailored to your business needs.

Pitfalls of in-platform solutions

In-platform solutions aren’t by any means a bad thing. In fact, built-in AI tools have become increasingly popular, owing to their ease of integration, user-friendly interfaces and minimal set up requirements. They come pre-packaged with the platform, offering the user the ability to leverage AI technologies without the need for in-depth technical expertise or the upfront cost of building a solution from scratch.

However, the streamlined and accessible nature of in-platform AI solutions comes at the expense of complexity and customization. They are designed to serve a broad user base, but for the most part are built using narrow AI solutions with predefined features and workflows.

This makes them great for assisting with common AI tasks, but they lack the flexibility to tailor functionality towards unique business requirements or innovative use cases, limiting the potential efficiencies and cost savings that can be unlocked. Additionally, if a business’ competitors are using the same platform, they are probably using the same AI solution, meaning any strategic advantage gained from these will be reduced.

Bespoke AI solutions, on the other hand, may carry a higher initial investment – but can offer a significantly more attractive ROI over a short amount of time.

Why customized and adapted AI is the key

The difference between bespoke AI and in-platform solutions is similar to that between home cooked food and a microwave meal. Yes, it is more time consuming to prepare, and yes it likely carries more of an upfront cost, but the end result is going to be far more appealing and will carry more long-term value (financially… not nutritionally).

That’s because bespoke solutions, by nature, will have been tailored to address your brands specific needs and challenges. These custom-built tools allow for much greater efficiencies by streamlining workflows across different channels, automating more complex tasks, and providing deeper, more relevant insights.

The increased level of optimization can significantly improve productivity and reduce operational costs over time, offering a higher ROI. The increased flexibility of bespoke AI also allows brands to implement innovative use cases that can significantly differentiate them from their competitors.

The data analyzed can be specifically chosen to match business requirements, as can the outputs of the AI tool, providing a significant advantage when understanding and acting on the insights provided.

Additionally, these tools are, by nature, more scalable. They can be updated, upgraded and expanded as needs change, ensuring they continue delivering value as the business grows. They can also be designed to integrate with any existing IT infrastructure, from CRM systems and databases to marketing platforms and sales tools – leading to more efficient and effective decision-making.

Managing finances with AI

It’s no secret that AI in marketing automation has, and will continue to, revolutionize the way marketing is done. It has a bright, if slightly terrifying, future and can help CMOs to unlock new efficiencies, maximize the impact of their budgets and increase their ROI. And as this technology becomes more advanced, its impact will only increase.

But we already know that…and so does everyone else.

So, in order for businesses to make themselves stand out from the crowd , they must look to fully adopt the power of AI. Creating a customized and unique AI solution could be the way to set yourself apart from your competitors. A bespoke AI tool can provide brands and businesses with features unique to them and their business needs. As a result, companies will benefit from more useful data and better results to make more data-driven decisions for their business. Ultimately, this will help brands to maintain a competitive edge over their competitors, deliver ROI and most importantly optimize their budgets.