Navigating the ISO 20022 Migration
This article is written by Kyriba Understanding ISO 20022 ISO 20022 is a universal translator for financial data that uses XML-based data elements, allowing different financial systems to communicate seamlessly. It can also replace local proprietary bank-specific payment initiation or statement reporting formats. Consider a future where managing multiple messaging formats is a thing of the past and transactions are processed in real-time. ISO 20022 will provide a foundation that enhances data management and quality, improves interoperability, and supports real-time capabilities, making this a reality. Interestingly, ISO 20022 is not a new initiative. It was established in 2004, two decades after SWIFT introduced the MT messages. Since then, its adoption has experienced two major accelerations: the first occurred around 2010 when Europe adopted ISO 20022 XML as the de facto standard for SEPA directives. The ongoing acceleration, which began in 2022, is driven by the need for a unified messaging standard across interbank systems. Over the last decade, nearly every new interbank system has been developed using ISO 20022. The spread of real-time payments has contributed significantly to the creation of new interbank systems. More than 70 countries have updated their local ACH systems to align with ISO 20022. Now, with International Payments (based on correspondent banking), and most domestic real-time gross settlement payment systems (CHIPS, CHAPS, Fedwire, Target and their equivalent in many countries) migrating to ISO 20022 by the end of 2025, “nearly all of the interbank space will be conducted using ISO 20022 in a few years” according to Kyriba’s VP of Product Management – Payments Guillaume Metman. Looking beyond the technical specifications, why does the ISO 20022 migration matter to businesses? Let’s break down the impacts: The push for ISO 20022 adoption is about seizing an opportunity to transform financial operations significantly. By adopting this standard, businesses can position themselves to take full advantage of new technological advancements and regulatory changes, ensuring they remain competitive and efficient in the evolving financial landscape. Preparing for the ISO 20022 Migration The mandate to adopt ISO 20022 primarily focuses on cross-border transactions and bank-to-bank exchanges. While there is not currently a migration deadline for corporate-to-bank communications, there are strong reasons for corporations to consider adopting ISO 20022. For instance, SWIFT is introducing capabilities that will allow businesses connected to the SWIFT network to exchange ISO 20022 messages with the same functionalities as banks. This includes network validation and interoperability with other standards. Additionally, by the end of 2026, structured (or hybrid) addresses will become mandatory, and unstructured addresses will be phased out for cross-border and high-value payments. This means that sourcing structured data from legacy formats will become more challenging. Finance and operations teams should follow strategic steps to ensure a smooth and successful migration to ISO20022: The Possibilities of ISO 20022 The ISO 20022 migration will transform financial operations, ushering in a new era of efficiency. “ISO 20022 is the oil for the machine,” Brice Goemans, SWIFT Corporate Products Owner, said on a recent panel. It enables better tracking, dashboarding, and overall transparency in payment processing, allowing organizations to streamline processes, offering numerous benefits: Are You Ready for the ISO 20022 Migration? The ISO 20022 migration is just the beginning. Organizations navigating this transition must prepare for a landscape that demands higher-quality data, robust compliance measures, and innovative payment methods. The time to act is now. By preparing for the ISO 20022 migration, businesses can ensure they remain at the forefront of financial innovation, ready to meet the demands of a rapidly changing global economy. Whether it’s through strategic planning, collaborating with banks, or upgrading existing systems, the steps taken today will pave the way for a more efficient and resilient tomorrow. Check out this on-demand webinar to learn more about the ISO 20022 migration. Read more from Kyriba Join our Treasury Community Treasury Mastermind is a community of professionals working in treasury management or those interested in learning more about various topics related to treasury management, including cash management, foreign exchange management, and payments. To register and connect with Treasury professionals, click [HERE] or fill out the form below to get more information. Notice: JavaScript is required for this content.
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Optimize Your Cash Forecasting with AI
This article is written by Kyriba Imagine a world where manual processes and guesswork don’t bog down forecasting. Instead, your forecast is created easily using real-time data and predictive analytics. This is the potential of artificial intelligence (AI) in modern financial operations; this is the power of AI in cash forecasting. AI’s ability to process vast amounts of financial data in real-time, predict cash flow trends, and provide actionable insights is already changing the game for Treasury teams. These advancements enable organizations to navigate economic volatility with unprecedented precision and confidence in the accuracy of their forecasts. Traditional Cash Flow Forecasting Methods Contribute to Liquidity Gridlock Cash flow forecasting is a cornerstone of Treasury management. Traditionally, the process has relied heavily on historical data, manual data entry, and complex spreadsheets, requiring Treasury teams to spend considerable time consolidating data from multiple sources, which leads to inefficiencies and inaccuracies. The time-intensive nature of these traditional methods means that treasury teams are often operating one step behind and with increasing volatility in the market, that can be detrimental to future growth. Staying ahead of the curve demands a more efficient, accurate, and dynamic approach to cash forecasting–one that AI is uniquely positioned to deliver. By leveraging AI, organizations can become better equipped to handle economic uncertainties and make informed decisions. The shift from simple forecasting to a broader liquidity planning approach involves surrounding traditional cash flow forecasts with real-time data from diverse sources. This expansion allows organizations to formulate a true enterprise liquidity strategy, helping them understand and manage liquidity risk while ensuring financial stability and resilience. It All Begins with a Data Strategy A critical component of AI in cash forecasting is having a robust data strategy in place that specifies how a company collects, stores, manages, and analyzes its data. Having the right data strategy is a game changer and an essential first step for integrating AI, and real-time insights, into your cash forecasting. By tapping into real-time data processing, treasury teams can craft a full picture of their company’s liquidity and thus are better equipped to make quick, informed decisions, and optimize their liquidity performance. Additionally, introducing real-time data into the cash forecasting process helps mitigate risks- something any CFO would be happy to hear. Through scenario planning and sensitivity analysis, companies can gauge how changes in the economy, environment, and customer behavior might impact their financial position, allowing them to tweak their strategies, hedge against risks, and stay one step ahead. Connect All Data Sources to Activate the Full Benefits of AI Once a data strategy has been established, the next step is connecting all of your data sources to a single source of truth a.k.a. a data lake. By ensuring seamless integration and communication between banks, ERPs, applications, and data trading platforms, you provide the fuel AI uses to leverage intelligence capabilities effectively. This approach is specific to your organization which means that the outcome is hyper-relevant and extremely context-rich. With a data lake in place, AI tools can quickly analyze vast amounts of integrated data. This provides context-rich insights that enhance the precision of your forecasts and make it easier to achieve financial stability and business resilience. Leveraging AI for cash forecasting and liquidity performance management has enabled organizations to achieve remarkable outcomes: $1.04M average net interest benefit from 47%+ reduction of idle cash $55M average free cash flow per $1B revenue from Supply Chain Finance program 87% reduction in overall risk impact with BI-enabled exposure management Source: Kyriba Value Engineering Analysis of 341 Corporations Top Applications of AI in Cash Forecasting The integration of AI in cash forecasting extends beyond basic financial management, offering solutions that are as varied as they are impactful. Some key applications where AI is making a significant difference are: These applications enhance the accuracy of cash flow forecasting and broaden the scope of overall financial strategy, making it more powerful and responsive to both internal and external changes. By harnessing AI, organizations can both improve their immediate financial forecasting abilities and strengthen their strategic planning capabilities to set themselves up for future success. Just Scratching the Surface In a recent webinar, Kyriba’s Viena Swierczek, Solution Engineer, and Lisa Husken, Value Engineer, highlighted how AI, especially as it relates to cash forecasting, refines existing processes and paves the way for groundbreaking approaches in financial management. “AI is not just about automating existing processes,” Lisa Husken, Kyriba Value Engineer, said. “It’s about enabling entirely new ways of thinking about financial strategy and execution.” “We are just scratching the surface of what AI can do in the financial sector,” Viena Swierczek added. “The next few years will be crucial in defining how deeply integrated AI becomes in our everyday decision-making processes.” This forward-thinking perspective invites finance leaders to consider the broader opportunities of AI beyond immediate operational improvements. Read more from Kyriba Join our Treasury Community Treasury Mastermind is a community of professionals working in treasury management or those interested in learning more about various topics related to treasury management, including cash management, foreign exchange management, and payments. To register and connect with Treasury professionals, click [HERE] or fill out the form below to get more information. Notice: JavaScript is required for this content.
Decoding the Impact of ChatGPT for Treasury
This article is written by Kyriba Generative AI, represented by groundbreaking models like ChatGPT, is poised to revolutionize treasury management. However, its integration brings forth a spectrum of implications, from promising opportunities to potential risks. We highlight the potential benefits and risks associated with integrating generative AI in treasury and finance operations. Exploring Generative AI Generative artificial intelligence (AI) is a subset of artificial intelligence technology. Unlike traditional AI systems that are often limited to specific tasks or domains, generative AI models are designed to generate novel outputs, including text, audio, images, and even entire virtual environments. Put simply, these models are usually trained on large datasets to learn patterns and relationships within the data. This allows generative AI models to create new content that is coherent and relevant to the input provided.. One of the main characteristics of generative AI is its ability to produce diverse and realistic outputs that mimic human creativity and intelligence. Although it has the potential to automate manual tasks and improve efficiency, there are concerns about fake or misleading content and the potential for misuse, like deepfakes. ChatGPT Is a Pioneer in the GenAI Space A prominent example of generative AI in action is ChatGPT, developed by OpenAI. Since its launch, ChatGPT has garnered widespread acclaim, reaching 180.5 million users by March 2024. OpenAI and ChatGPT gained further notoriety by recently partnering with Microsoft for a multi-year alliance valued at $10 billion. Google has also taken notice; even though ChatGPT is not a search engine, it is already being viewed as competition for the online search giant. Google’s management has developed a chatbot of its own, though in a recent demo, it made a mistake that has already cost the company $100 billion in market value. What GenAI Means for Treasury GenAI, including ChatGPT, offers a range of new options for treasury teams, including the ability to ask questions of your treasury data – typed or by voice – as well as build new reports, queries, dashboards and even new programming and scripts. The biggest challenge for treasury teams is how to manage GenAI in a secure and safe manner. Different organizations will have different policies around the use of tools such as ChatGPT, including when open source language models can be used versus closed models such as ChatGPT Enterprise. To be responsible to all their customers, including those organizations that prohibit the use of ChatGPT altogether for their finance teams, most treasury system providers will not (and arguably should not!) embed ChatGPT directly into their software platforms. Rather they are opening their data models through APIs to make treasury data available to GenAI tools – which allows treasury teams to make their own choices about what, if any, GenAI tools they integrate into their treasury processes. For those that choose to introduce ChatGPT and similar tools into their treasury operation, here are examples – both good and scary – where we have seen GenAI increase its relevance for treasury: Payments Fraud Detection It’s been well documented that AI is being used regularly for both payments fraud and payments fraud prevention. Generative AI has now become the latest tool in fraudsters’ arsenals – making it a ‘scary’ scenario for treasury teams to combat. Cybercriminals are using generative AI tools–including ChatGPT–to help craft sophisticated phishing messages. There has been a 1,265% increase in malicious phishing emails since Q4 2022. Check Point, another cybersecurity firm, noted that using ChatGPT helped it create an end-to-end social engineering campaign from phishing emails all the way to embedded malware within an email attachment. The result was disturbingly convincing and confirmed that GenAI tools have lowered the bar for code generation, making it easier than ever for bad actors to attempt fraud. In response, cybersecurity experts all say the same thing: use automated systems to detect and prevent cyberattacks at machine speed. In treasury, this presents an opportunity to use AI in the payment process to detect suspicious payments, using adversarial networks to identify payment anomalies compared to an organization’s own payment history. While this is an example of GenAI being used for nefarious purposes, embedding a closed AI model to use only an organization’s own payment data to identify potential fraudulent payments is a great way to use AI to fight the AI employed by fraudsters. Enhancing Excel with AI Imagine a world where generative AI technologies seamlessly integrate with essential business tools, transforming traditional treasury management systems without disrupting existing structures. This vision is now a reality with Excel, supercharged with AI tools like ChatGPT and Copilot through OpenAI’s partnership with Microsoft. This integration not only enhances workflows with secure and streamlined interactions but also revolutionizes how data is managed and presented. Excel’s AI-powered features offer a GenAI function to engage with your data, capturing insights from both internal models and external sources. With Copilot, users can effortlessly create complex and easy-to-read spreadsheets. Need an extra column? Copilot will create it. Forgot that unique combination of formulas? Copilot has you covered. ChatGPT’s large language model unifies the data you need, while Copilot formats it into the most impressive spreadsheet you’ve seen. Microsoft addresses automation and data security concerns with Appsource store’s treasury apps, which automate the secure delivery of data into your spreadsheets. These embedded apps allow data to be queried from the cloud and presented directly into your spreadsheet. Simply sit back and enjoy the AI ride as your treasury management capabilities become more efficient and secure. Bank Payment Formats with AI One of the most challenging capabilities for both finance and IT teams is the bank payment format transformation process. This difficulty comes with the multitude of format variations banks require, making a standard such as XML ISO20022 PAIN v9 into more of a guideline with sometimes dozens of different file format requirements, even within the same bank. ChatGPT becomes a great solution to build these unique file format customizations on the fly for a payments system user or the IT professional tasked with updating the file delivery from their ERP to the bank. The ERP or treasury system exposes,…
Optimize Your Cash Forecasting with AI
This article is written by Kyriba Imagine a world where manual processes and guesswork don’t bog down forecasting. Instead, your forecast is created easily using real-time data and predictive analytics. This is the potential of artificial intelligence (AI) in modern financial operations; this is the power of AI in cash forecasting. AI’s ability to process vast amounts of financial data in real-time, predict cash flow trends, and provide actionable insights is already changing the game for Treasury teams. These advancements enable organizations to navigate economic volatility with unprecedented precision and confidence in the accuracy of their forecasts. Traditional Cash Flow Forecasting Methods Contribute to Liquidity Gridlock Cash flow forecasting is a cornerstone of treasury management. Traditionally, the process has relied heavily on historical data, manual data entry, and complex spreadsheets, requiring Treasury teams to spend considerable time consolidating data from multiple sources, which leads to inefficiencies and inaccuracies. Cash Flow Forecasting Challenges: Imagine a world where manual processes and guesswork don’t bog down forecasting. Instead, your forecast is created easily using real-time data and predictive analytics. This is the potential of artificial intelligence (AI) in modern financial operations; this is the power of AI in cash forecasting. AI’s ability to process vast amounts of financial data in real-time, predict cash flow trends, and provide actionable insights is already changing the game for Treasury teams. These advancements enable organizations to navigate economic volatility with unprecedented precision and confidence in the accuracy of their forecasts. Traditional Cash Flow Forecasting Methods Contribute to Liquidity Gridlock Cash flow forecasting is a cornerstone of treasury management. Traditionally, the process has relied heavily on historical data, manual data entry, and complex spreadsheets, requiring Treasury teams to spend considerable time consolidating data from multiple sources, which leads to inefficiencies and inaccuracies. Cash Flow Forecasting Challenges: The time-intensive nature of these traditional methods means that treasury teams are often operating one step behind and with increasing volatility in the market, that can be detrimental to future growth. Staying ahead of the curve demands a more efficient, accurate, and dynamic approach to cash forecasting–one that AI is uniquely positioned to deliver. By leveraging AI, organizations can become better equipped to handle economic uncertainties and make informed decisions. The shift from simple forecasting to a broader liquidity planning approach involves surrounding traditional cash flow forecasts with real-time data from diverse sources. This expansion allows organizations to formulate a true enterprise liquidity strategy, helping them understand and manage liquidity risk while ensuring financial stability and resilience. It All Begins with a Data Strategy A critical component of AI in cash forecasting is having a robust data strategy in place that specifies how a company collects, stores, manages, and analyzes its data. Key Questions Finance Teams Need to Ask When Building a Data Strategy: Having the right data strategy is a game changer and an essential first step for integrating AI, and real-time insights, into your cash forecasting. By tapping into real-time data processing, treasury teams can craft a full picture of their company’s liquidity and thus are better equipped to make quick, informed decisions, and optimize their liquidity performance. Additionally, introducing real-time data into the cash forecasting process helps mitigate risks, something any CFO would be happy to hear. Through scenario planning and sensitivity analysis, companies can gauge how changes in the economy, environment, and customer behavior might impact their financial position, allowing them to tweak their strategies, hedge against risks, and stay one step ahead. Connect All Data Sources to Activate the Full Benefits of AI Once a data strategy has been established, the next step is connecting all of your data sources to a single source of truth, a.k.a., a data lake. By ensuring seamless integration and communication between banks, ERPs, applications, and data trading platforms, you provide the fuel AI uses to leverage intelligence capabilities effectively. This approach is specific to your organization, which means that the outcome is hyper-relevant and extremely context-rich. With a data lake in place, AI tools can quickly analyze vast amounts of integrated data. This provides context-rich insights that enhance the precision of your forecasts and make it easier to achieve financial stability and business resilience. Leveraging AI for cash forecasting and liquidity performance management has enabled organizations to achieve remarkable outcomes: $1.04M average net interest benefit from 47%+ reduction of idle cash $55M average free cash flow per $1B revenue from Supply Chain Finance program 87% reduction in overall risk impact with BI-enabled exposure management Source: Kyriba Value Engineering Analysis of 341 Corporations Top Applications of AI in Cash Forecasting The integration of AI in cash forecasting extends beyond basic financial management, offering solutions that are as varied as they are impactful. Some key applications where AI is making a significant difference are: These applications enhance the accuracy of cash flow forecasting and broaden the scope of overall financial strategy, making it more powerful and responsive to both internal and external changes. By harnessing AI, organizations can both improve their immediate financial forecasting abilities and strengthen their strategic planning capabilities to set themselves up for future success. Just Scratching the Surface In a recent webinar, Kyriba’s Viena Swierczek, Solution Engineer, and Lisa Husken, Value Engineer, highlighted how AI, especially as it relates to cash forecasting, refines existing processes and paves the way for groundbreaking approaches in financial management. “AI is not just about automating existing processes,” Lisa Husken, Kyriba Value Engineer, said. “It’s about enabling entirely new ways of thinking about financial strategy and execution.” “We are just scratching the surface of what AI can do in the financial sector,” Viena Swierczek added. “The next few years will be crucial in defining how deeply integrated AI becomes in our everyday decision-making processes.” This forward-thinking perspective invites finance leaders to consider the broader opportunities of AI beyond immediate operational improvements. Also Read Join our Treasury Community Treasury Mastermind is a community of professionals working in treasury management or those interested in learning more about various topics related to treasury management, including cash management, foreign exchange management, and payments. To register and connect with Treasury professionals, click [HERE] or fill…
The Threat of Deepfake Frauds in Payment
This article is written by Kyriba Imagine this: your CEO’s voice or your CFO’s face—and a request for funds. Something in your gut is telling you that this situation feels ‘off’ but what can you do? It’s the CEO or CFO of the company after all. This is the reality of deepfake fraud, a clever ruse that is not only making headlines but also blurring the lines between truth and fiction with chilling precision. Every transaction and every interaction is a potential battleground, where the slightest misstep could lead to catastrophic losses. Misinformation and disinformation are the number one concern and near-term risk according to the World Economic Forum’s 2024 Global Risks Report for government leaders, executives, chief information security officers, and others who want to mitigate deepfake fraud. The Evolution of BEC Scams Business email compromise scams exploit the most vulnerable element in tools, technology, and processes: us. Leveraging BEC scams has remained one of the most profitable forms of cybercrime by exploiting weaknesses in human emotions and decision-making habits. In fact, despite increasing awareness of these types of scams amongst the general public, the FBI reported 21,489 BEC complaints, with losses amounting to $2.9 billion in 2023 alone. The integration of deepfake technology into these scams marks a significant step in their increasing sophistication and highlights the need for heightened vigilance and advanced cybersecurity measures. As criminals continue to use advanced AI to create more convincing frauds, the challenge for businesses becomes how to play defense against a technological threat and a psychological one. The Rising Threat of Deepfake Fraud A subset of “synthetic media” or “synthetic content,” deepfakes are defined as a type of artificial intelligence (AI) that—as the name suggests—are used to create bogus content, such as images, audio, and video. The rise of deep-fake fraud casts a shadow of doubt over every transaction. Deepfake software has become a powerful and dangerous tool in the hands of fraudsters. The technology can create the illusion of a legitimate transaction. You might think you are hearing from the CEO, the CFO, or the attorney related to a merger, requesting a legitimate payment. And by the time a company realizes it has been duped, it’s often too late. In early 2020, deep-fake voice technology was famously used in a $35 million bank heist in Hong Kong. A bank manager received a call and several emails from what appeared to be a company director he had spoken with before. The director claimed that his company was making an acquisition soon and needed a $35 million transfer to complete the process. The bank manager, recognizing the man’s voice and believing everything to be legitimate, complied and sent the money. Of course, the person who called the bank manager and sent the emails was not who they claimed to be, and the money was stolen. The theft has implications for companies of all sizes, as it represents the latest step on the evolutionary scale of a familiar scam that has duped well-meaning financial professionals into transferring millions into the wrong hands. The Deception Deepens with Video & Audio Deepfake technology uses artificial intelligence to combine still images of one person with video footage of another. In a relatively short amount of time, the technology has improved to the point where very few photos—and in some cases, just one—are needed to create a convincing video deepfake. Similarly, Deepfake audio, or “deep voice” technology is another nefarious innovation. Much like with video, the software may only need a 30-second or less snippet of audio to create a flawless deepfake, according to Rupal Hollenbeck, president at Check Point Software. In a case reminiscent of the Hong Kong heist, fraudsters created an elaborate and sophisticated scheme, posing as company executives during a virtual conference call. The result? A financial worker, despite initial suspicions, was persuaded into transferring $25 million into the fraudsters’ pockets. The Office of the CFO is the Last Line of Defense Against Deepfakes The consequences of this type of fraud is not limited to financial losses but also includes potential damage to an organization’s reputation and stakeholder trust. In response to this growing threat, it is imperative that treasury professionals operate within a culture of skepticism and integrate advanced security measures into their standard operating procedures. These new, sophisticated technologies require sophisticated solutions that combine cutting-edge technology with human expertise to detect anomalies. Five Steps to Avoid a Deepfake-out The following tips can help treasury and finance professionals identify audio and visual deepfakes. Ultimately, the best way to avoid these deep-fake scams is to follow prevention best practices and apply a critical eye. While it isn’t easy to be hyperaware of the threats around us, that is exactly what we need to be in this current environment. “It used to be that seeing was believing,” said Hollenbeck, “but not so much anymore.” Deepfakes are the latest in a long line of scams. The best way to avoid falling victim: slow down. According to Blackcloak CEO, Chris Pierson, “slowing down almost always yields a definitive answer.” Also Read Join our Treasury Community Treasury Mastermind is a community of professionals working in treasury management or those interested in learning more about various topics related to treasury management, including cash management, foreign exchange management, and payments. To register and connect with Treasury professionals, click [HERE] or fill out the form below to get more information. Notice: JavaScript is required for this content.