Source: Finance Derivative
By Dr. Jochen Papenbrock, Head of Financial Technology, EMEA, NVIDIA, and Prabhu Ramamoorthy, Partner Developer Relationship Manager, Financial Services, NVIDIA
Financial institutions globally, including banks, are subject to the Financial Action Task Force (FATF) for combating financial crime, terrorist financing, and preventing money laundering. Artificial intelligence (AI) is increasingly being used to fight financial crime and offers new opportunities to help detect and prevent fraud. AI and associated machine learning (ML) or deep learning (DL) models provide bank case managers, regulators and compliance officers with powerful new capabilities.
AI is the capability of a computer program, referred to as a machine, to think and learn and take actions without being explicitly encoded with commands. AI can be thought of as the development of computer systems that can perform tasks autonomously, ingesting and analyzing enormous volumes of data, then recognizing patterns in that data. ML, a subset of AI, is the practice of using algorithms to parse data, learn from it, and then determine or predict next steps. Improving on the traditional rules-based approach or the hand-coding of software routines with a specific set of instructions to accomplish a particular task, AI algorithms are trained using large amounts of data, enabling them to learn a task. DL is a subset of machine learning, with the difference that DL algorithms can automatically learn representations from data such as images, video, or text, without introducing human domain knowledge. The word “deep” in deep learning represents the many layers of algorithms, or neural networks, that are used to recognize patterns in data. DL’s highly flexible architectures can learn directly from raw data, similar to the way the human brain operates, and can increase their predictive accuracy when provided with more data
The easiest way to think of their relationship is to visualize them as subsets within overlapping AI — the idea that came first — the largest, then machine learning — which blossomed later, and finally deep learning — which is driving today’s AI explosion — fitting inside both.
Why is AI an Effective Tool?
AI models and algorithms can consume and synthesize massive volumes of data. Furthermore, AI can ingest the data and act on it in near-real time, enabling authorities to stay in step with the movements of bad actors rather than remaining days or weeks behind.
AI models are designed to detect anomalies in the patterns of data they are ingesting by scoring those behaviors relative to expected benchmarks, so that banking compliance officers are alerted when potentially nefarious interactions occur. The investigations tied to these alerts are often led by compliance personnel within banks, and are therefore time-consuming and costly.
Rules- vs. Model-based Approaches to Combating Money Laundering
Money laundering is a process that criminals use to hide the illegal source of their funds. By passing money through multiple, sometimes complex, transfers and transactions, the money is “cleaned” of its illegitimate origin and made to appear as legitimate business profits.
Technological advances in digital banking, online account opening, open banking and cryptocurrency have made tracking the source of funds and uncovering suspect patterns and behaviors far more resource-intensive for financial institutions and their regulators. Traditional methods of automation are unable to keep up with the increasingly sophisticated ways the financial system is abused, so the FATF has encouraged the digital transformation of Anti-Money Laundering (AML) and Counter-Terrorist Financing (CTF) efforts.
Legacy rules-based AML systems have a false positive rate > 90%, meaning investigators’ valuable time is wasted on the wrong transactions. These approaches rely on databases of human-engineered rules that are used to spot patterns indicative of fraud. Figure 2 illustrates a rules-based approach to identifying suspicious financial transactions. Here, a large set of rules is defined and then applied to all financial transactions. If a transaction matches any of the rules, an alert is triggered.If the alert is incorrectly triggered (false positive), it incurs a cost. If no alert was triggered, but one should have been (false negative), a new rule is designed. But fraudsters are becoming savvy at avoiding patterns that rules-based systems can easily recognize.
Many financial institutions are therefore transitioning to model-based systems that reduce false positives by more than 60% and increase anomaly detection by 200%. Using historical actual or suspected financial crime data, the machine learns what is considered normal and suspicious behavior and predicts the risk of money laundering at a more accurate rate, reducing detection from hours to milliseconds and stopping crime in its tracks. Rather than looking for patterns that exactly match pre-defined rules, model-based systems can learn to generalize and identify new fraud schemes that might be new interpretations of old ones. This makes it harder for criminals to avoid detection. They are no longer able to make small adjustments to get around a relatively static set of rules.
In addition to this, graph neural networks (GNNs) can be used by investigators to evaluate relationships between any number of parties to flag potential money laundering behavior. The concept is to construct a heterogeneous graph from tabular data and train a model to detect suspicious transactions and complex laundering activities, as criminals work collaboratively in groups to hide their abnormal features but leave some traces of relationships.
Generative AI and Large Language Models in Fraud Detection and Prevention
ChatGPT was the iPhone moment for AI, and brought Generative AI to the forefront of public discourse. In the financial services industry, Generative AI will play a key role in fraud detection.
Large Language Models (LLMs) can be useful in fraud detection because they can retrieve and analyze information from large unstructured data sources in a short timeframe. Unstructured data can be news, social media or internal organization-generated data in the form of contracts, onboarding documents, audio calls, satellite imagery, trade documents, and invoices/payments. The models can identify and connect different legal entities (companies, individuals, etc.) with associated actions, understand that context, and incorporate this complex information into an information retrieval algorithm for identifying suspicious activities.
Another fraud detection application of LLMs is to monitor conversations, chats and trade activities. Deep Neural Network (DNN) models can analyze the data, draw conclusions and trigger alerts to compliance officers.
Software and Hardware Needs for AI
The implementation of Generative AI requires dedicated hardware and software. On the hardware side, an AI acceleration platform – GPU accelerated compute, accelerated software libraries, data, and storage – is needed for unstructured and semi-structured data models. On the software side, AI frameworks and data scientist resources to code and deploy solutions at scale are needed.
Accelerated platforms make it possible to process several tasks simultaneously in a significantly shorter time. Before accelerated computing, unstructured and semi-structured language models took weeks or months to train, with results also taking time to be returned. Today, Generative AI LLMs and GNNs can be trained in hours or days, and their results returned in milliseconds. As the amount of data, including transactions and related contracts to those transactions, grows exponentially, more and more advanced models are trained. It is not only the models but also the data processing needed for the models that need to be accelerated to process large volumes of unstructured, tabular data to be useful for real time fraud applications. This is why, in turn, state-of-the-art computational accelerators such as NVIDIA are needed with integrated hardware and software solutions for end-end analytics and ML/AI pipelines.
AI is Here to Stay
Financial regulators, bank executives, and risk and compliance officers are prioritizing an investment in AI for financial compliance and fraud detection. AI technologies can help by analyzing large amounts of data and using advanced algorithms to identify suspicious activity. AI can adapt to changes without human intervention catching fraudsters in the act of committing financial crimes. For a successful implementation, financial institutions typically rely on a combination of models for use cases in different stages – for example, Generative AI and LLMs for unstructured data analysis, and ML and Graph Neural Networks for analyzing and visualizing transactions.
AI is here to stay and will continue to unlock opportunities for the financial services industry. Beyond its use in preventing financial crime, firms are leveraging AI to create more relevant customer experiences, improve risk management, drive operational efficiency, and deliver more value to customers.
Enhancing sustainable commitments in retail banking
Source: Finance Derivative
Mikko Kähkönen, Head of Payment Cards Portfolio at Giesecke+Devrient
Today, more consumers are keeping environmental pledges from banks at the forefront of their financial decisions, and those banks that fall behind their competitors on sustainable action are risking the loss of customers, particularly among the younger generation. This shift highlights a growing expectation from consumers for their banks to make and uphold sustainable commitments, signalling a change in consumer priorities where environmental responsibility is increasingly seen as essential, not just an optional extra. Giesecke+Devrient research shows that as many as 64% of Gen Z consumers would be happy to switch banks if their current provider didn’t meet their expectations.
However, sustainable commitments must be authentic to avoid any accusations of greenwashing. Unfortunately for the banking sector, consumer trust is being strained as greenwashing incidents have risen by 70% around the world. Banks can’t simply make claims that can’t be backed up; pledges must be supported by evidence. There’s a number of practical steps they can take to prove their credentials.
Banking on the evolution of cards
The bank card has increasingly become a physical symbol of the relationship between consumer and bank. As such, banks have taken steps to ensure that it is designed with sustainability in mind. Many are now created with recycled PVC material, commonly up to 100%, with a lower carbon footprint.
Some banks are elevating their sustainable credentials by utilising cards that are made from plastic collected in oceans and coastal regions, helping to clear up the world’s beaches. Alongside this, others are issuing cards made of polylactic acid sourced from (inedible) corn starch. This is a fully renewable biomass that could be industrially composted.
Sustainable cards can then encourage further sustainable initiatives. We’re more often seeing issuers now actively taking part in local conservation, community development and educational projects around the world to help benefit the planet. Communicating these efforts to customers can help reinforce sustainable credentials and leave tangible evidence that proactive action is taking place.
Contributing to the circular economy
Powering the sustainable credentials of issued cards is one aspect, but it’s also vital that banks encourage their customers to do the right thing with them once they expire and they need to be discarded of. We’re already seeing prominent banks making progress in this area. UK retail bank, Santander, has launched a pilot scheme in branches and ATMs that encourages customers to return their outdated credit and debit cards for recycling, for example.
The collected cards are then turned into plastic pellets to be used elsewhere, for instance to make outdoor furniture, sponsored by Santander, for local communities. As more banks opt for card recycling, consumers will be empowered to dispose of their old or expired cards in a green way and help to reduce ecological footprint.
Into the digital world
Outside of card innovations, retail banks can add to their credible green claims with digital solutions. As an example, the card issuance process has typically involved paper letters, with additional PIN letter, that are posted out to customers to activate their payment cards. Instead, an ePIN service can enable customers to instantly access their PIN via their choice of a mobile app or SMS message, reducing paper waste and waiting times.
There are also innovations taking place in terms of QR codes and augmented reality (AR) solutions to enable digital marketing offerings. This means that printed collateral doesn’t need to physically sent out in the post. The more that these types of communications are sent out digitally, the more that consumers see a tangible commitment to sustainable practices.
Banks can even take an additional step by deploying third-party partners to track the CO2 footprint involved with every purchase or payment. By opting for organisations that have a solid track record in green practices, such as supporting product certifications and information on eco-products and their claims, they can make steps to compensate for each transaction carbon footprint.
Contributing to the green story
To ensure they don’t come under any criticism regarding their environmental claims, banks and financial institutions have the opportunity to adopt sustainable practices that align with their customers’ expectations for eco-friendly commitments in both their physical and digital services. They can introduce banking cards made from recycled or entirely compostable materials, eliminating plastic waste.
Digitally, banks can minimise unnecessary paper use by employing online applications to simplify the process of delivering PINs. By innovating in these domains, they can fulfil their environmental responsibilities and establish that essential trust with consumers, contributing positively to the planet’s wellbeing.
Successfully dealing with the unintended consequences of change
by Daniel Norman, Change Management Consultant at Symatrix
Most people dislike change. We are drawn to stability and established routines and feel unsettled when anything happens to disrupt the ‘status quo’. It’s bad enough when the local supermarket moves the bread section – but when the company we work for introduces a new digital system that completely changes how we work, it feels like ‘the sky is falling in’.
When change happens within businesses, there may initially be some resistance from employees: whether it be in the form of avoiding new systems, skipping training, clinging to old methods, or even quitting altogether. Change in business is a constant, however, and it is usually driven by a desire for improvement, and typically over time, becomes the new normal.
Good change management is all about smoothing this process of transition and that means engaging with people and helping them to seamlessly switch to a new model or ways of working. Change management is not just concerned with implementing new systems or processes; it is just as much about listening intently to colleagues, customers, and stakeholders.
It’s working with people to get things right, building a deep understanding of the challenges we and our colleagues face, and shaping the vision for a future that resonates with people. Change is most successful when everyone feels they have a part to play in moving things forward. And that’s true of all change initiatives, large and small.
Finding a way forward
When it comes to managing change, it’s important to recognise that everyone will have their own journey; they’ll work through things at their own pace, and that’s more sustainable than pretending we’ll all arrive at the same point at the same time.
It’s also important to focus on creating a supportive environment, or the right conditions for people to adapt, with as little friction as possible. The goal is to establish conditions that minimise friction and foster a collective sense of purpose. This philosophy is crucial in creating a environment conducive to individual and organisational growth.
Getting the planning process right
When planning for change, it’s essential to consider both the intended and unintended consequences. Just as technological advancements like social media have transformed communication but also introduced challenges such as misinformation and mental health concerns, organisational changes can have extensive, unforeseen impacts. A thorough exploration of current operational practices, beyond process maps or managerial assertions, is therefore, always a vital feature of any effective change management approach.
For that reason, it can often be a mistake to pull out those process maps the team updated 12 months ago or rely on the word of line managers that will tell you ‘this is how we operate’ without taking into consideration the work-arounds or simplifications that employees have developed over time.
Teams will naturally evolve, and patterns of work; ways of doing things that aren’t written down, will always be there. A good change manager must always be cognisant of that. Even small changes, like when a key person in the team changes roles, can have a big impact.
To manage change well, it’s important to talk to the people who will be most affected by it. This helps change managers to plan and effectively execute the change journey. By ignoring these key considerations, organisations risk their change strategy stalling from the outset and the opportunity for operational efficiencies may therefore never be fully realised.
Throughout the process, it is crucial to continuously monitor and measure the impact of change on all key stakeholders. One effective way of doing that is by embracing the principle of change curves: a popular model organisations can use to understand the different stages people and the organisation go through when a change occurs.
An effective strategy involves mapping stakeholders against this curve, whether as individuals or groups, during project check-ins. This approach can help project leaders gauge the current position of every team member on the curve, the impact of the project’s upcoming phase on them, or their colleagues, and additional support measures that could be implemented. Such an assessment facilitates a more tailored and effective change management strategy, ensuring stakeholders are adequately supported throughout the transition.
Not everything will run like clockwork, of course, no matter the change management approach that is put in place. Challenges, setbacks, and opportunities for improvement are inherent to any process, but proactive anticipation and planning for potential worst-case scenarios and unintended consequences significantly enhance our ability to support our colleagues and teams effectively. This strategic foresight is crucial in managing transitions smoothly and realising the intended benefits of initiatives.
A positive route ahead
Change, especially in business, are inevitable and often aimed at fostering improvement and growth. However, the journey through change is deeply personal and varies from one individual to another. By acknowledging this, creating a supportive environment, and engaging with all stakeholders, organisations can navigate the complexities of change with minimal resistance and maximum efficiency.
Effective change management, therefore, is not just about the technical implementation of new systems but about genuinely listening to and working with people to adapt and thrive in new circumstances. It’s about understanding the nuanced ways teams operate, the unofficial shortcuts and workarounds they’ve developed, and considering the broader implications of change beyond immediate operational efficiencies. Through a thoughtful approach that anticipates challenges and values stakeholder input, organisations can not only manage change but turn it into a catalyst for positive transformation and growth.
It is clear then that while people may inherently dislike change, with the right conditions, support, and leadership, the transition can become a journey of collective progress and innovation. Change, managed well, can transform the initial discomfort into an opportunity for development, making the once feared ‘sky falling in’ scenario a launchpad for reaching new heights.
Embedded finance: What consulting firms need to know
By Michael Pierce, VP of Sales at Toqio
Consulting firms are the architects of change in the business world, offering insights and solutions that guide companies toward growth and success. They navigate the intricate landscape of markets and industries, providing invaluable advice to their clients. In this evolving milieu, an opportunity is arising as embedded finance enters the scene, creating a unique and prospectively vital synergy between consultants and platform providers.
Embedded finance, especially within the scope of B2B enterprises, is a hot topic right now among consultancies and the outlook seems to be quite positive.
To date, much of the initial traction in embedded finance has been in the consumer sector, with products such as no- or low-interest financing, buy-now-pay-later (BNP), and others. On the B2B side, there is an increasing amount of mobilization. In recent months we’ve seen incumbent banks either entering the banking-as-a-service (BaaS) market or enabling their services through open banking partnerships, while strategy firms are busy advising corporate entities on the potential routes they can take. Early adopters have already made embedded finance a cornerstone of their digital or financial transformation programs: MVPs and proofs of concept have been on the rise.
As we all peer forward, the market is starting to look for scalable use cases to take advantage of these massive, predicted opportunities. Companies are searching for solutions that go beyond the hype.
For consulting firms, the messaging remains positive. The fundamentals of embedded finance drive strong service revenue. Even more importantly, the business cases for their clients stack up as well. Numerous opportunities are on the table when consultants incorporate embedded finance platforms into their projects, including increased revenue, improved retention rates, access to a wider range of data for better decision-making, and many more.
Embedded finance helps to break down barriers faced by many companies when trying to access affordable financial services. By integrating financial services directly into the supply chain, companies can enjoy many benefits, such as liquidity management, credit accessibility, risk mitigation, and many others. That’s one of the reasons why embedded finance platforms are proving to be the latest addition to the consultant’s toolkit. They offer a wide array of solutions that enable businesses to integrate financial services into their products and services. What makes embedded finance platforms especially appealing to consultants is their adaptability and scalability.
Consulting firms understand the need for versatile solutions capable of addressing various business requirements. Versatility and adaptability are key, giving consultants the flexible tools they need to deliver on time and within budget.
Embedded finance platforms are a natural extension of consulting firms’ capabilities as they offer a comprehensive range of financial solutions that integrate perfectly into existing business processes. This alignment provides consulting firms with several advantages, such as enhanced client services, data-driven insights, streamlined processes, scalability, and versatility.
The compatibility between consulting firms and embedded finance platforms is readily apparent. Consultants excel at diagnosing business issues and embedded finance platforms provide a precise prescription for financial enhancements.
There is an extensive list of benefits that consulting firms can get from platforms like this. Diversifying their business is just one of them as embedded finance platforms augment the services that consultants offer. They allow consultants to present clients with solutions for intricate business ecosystem operations, such as payment processing, receivables management, and liquidity optimization.
Partnering with an embedded finance platform can also open up new revenue streams as well as being able to scale the solutions built with more agility. Consultants can use them to address the unique needs of projects of any size, whether working with an SME or a multinational enterprise.
The relationship between consulting firms and embedded finance platforms isn’t just about expanding services, it’s about offering integrated financial solutions that improve efficiency, profitability, and competitiveness. This partnership drives results. In a world where businesses seek comprehensive solutions, embedded finance platforms empower consulting firms to address complex financial challenges effectively.