![\"A](\"https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/2023\/01\/iStock-1358050964.jpg\")
\t\t\t\t<\/div>\n\t\t\t\n\t\t\tMicrosoft Cloud for Financial Services<\/h2>\n\n\t\t\t\t\t\n\t\t\t\t\t\tUnlock business value and deepen customer relationships<\/p>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tGet started today<\/span>\n\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t<\/div>\n\n\t\t\t\t\t<\/div>\n\t<\/div>\n<\/div>\n\n\n\nHow data became the third leg of bank business models <\/h2>\n\n\n\n
From its inception, banking has always been a business of data\u2014its movement and processing, and the insights derived therefrom. <\/p>\n\n\n\n
As financial intermediaries, banks survived for centuries based on data at the heart of a two-sided business model: taking deposits (liability ledger) and making loans (risk assets). Profit was the lucrative spread between these two pillars. Business cycles and financial crises have come and gone but this fundamental model has not changed. <\/p>\n\n\n\n
Technology has been integral to data management since the rudiments of data processing automation and Management Information System (MIS) dashboards. The rise of the modern real-time data economy, however, completely alters the environment in which banks operate. <\/p>\n\n\n\n
Retail banking was first to transform by monetizing fragmented data in correlation with context and other factors. That beginning marked a signpost to a new space where the value of insights became the third important leg of bank business models. With the power of AI and the simplicity of natural language copilots, we are at the start of a new epoch which marks a profound transformation in banking.\u00a0<\/p>\n\n\n\n
Developing this trajectory, it is clear that Business-to-Business (B2B) flows contain much richer datasets to be monetized across a broader spectrum of economic activity, from local Main Street to global supply chains. Corporate banking is the epicenter of this next wave of B2B value creation through its main business lines: working capital management, payments and transaction banking, and, in particular, trade finance. <\/p>\n\n\n\n
Unlocking B2B data insights is driving banking transformation <\/h2>\n\n\n\n
Trade finance is a natural starting place for bank modernization. It is unusually rich in untapped B2B details, it is super relevant to a bank\u2019s overall commercial banking proposition, and it offers the most easily addressed \u201clow hanging fruit\u201d for return on investment (ROI) due to the prevalence of so many manual processes. <\/p>\n\n\n\n
Note that this near-term upside should not be confused with the industry\u2019s longer-term policy agenda on \u201ctrade digitization,\u201d<\/em> which focuses on transitioning from traditional, paper-based processes to digital formats. Global bodies such as the Bankers Association for Finance and Trade (BAFT), the International Trade and Forfaiting Association (ITFA), and the International Chamber of Commerce (ICC) will, in due course, develop legal frameworks that facilitate this transition. But before that, there is a clear business case within banks to adopt currently available new technologies in a race to transform client experience, improve operating efficiency, and gain marketplace advantage from B2B data insights. <\/p>\n\n\n\nBanks are naturally rich in B2B data as a consequence of their existing franchises and the daily flow of transactions through their processing systems. Yet, insights from the graph of these non-linear B2B relationships languish trapped and untapped in legacy silos. With this in mind, Microsoft has been leading the development of new AI-focused technologies for knowledge workers in today\u2019s modern banking environment. These include natural language copilots, starting with Microsoft 365 Copilot<\/a>, custom copilots built with Microsoft Copilot Studio<\/a>, and Agentic AI<\/a> for more complex tasks. Concurrently, solutions like Microsoft Fabric<\/a> can unify data for analysis and action from disparate sources irrespective of the technical environments in which they sit. \u00a0<\/p>\n\n\n\nMicrosoft\u2019s data tools unlock data insights and help make trade finance processes more efficient and accessible. Importantly, they are all designed with the same security, compliance, and content entitlements that are already established within banks, so getting started is easier. <\/p>\n\n\n\n
A benefits-driven roadmap for trade finance modernization <\/h2>\n\n\n\n
The roadmap that banks are adopting for trade finance modernization follows five simple steps, starting with the basics of helping colleagues do their work better: <\/p>\n\n\n\n
\n- Generative AI copilots<\/strong> can transform operations and drive new efficiencies in many powerful ways. For example, copilots can help front-office trade sales and relationship managers identify new financing opportunities when advising clients. Natural language queries can convert a daunting amount of manual research into simple and repeatable investigative questions. A client\u2019s Annual Report, 10-K filings, and other sources can be analyzed in real time with opportunities summarized for action.
Microsoft\u2019s Financial Meeting Prep<\/a> on Microsoft Teams, launched with LSEG<\/a>, shows the simplicity of how this could work in trade finance. Financial Meeting Prep helps organize more effective meetings through a single view of all relevant content. It drives better meeting outcomes and improves engagement, job satisfaction, and revenue growth. By the same token, trade finance product managers can transform how they conduct research in developing and managing new products with Copilot for project<\/a>. Mundane tasks, like generating monthly product performance reports, can be automated with conversational copilots that are embedded in familiar tools like Copilot in Excel<\/a> and Copilot for Power BI<\/a>. This provides all users with proactive drilldown capabilities to discover desired insights without reversion to a lot of manual rework.<\/li>\n\n\n\n- Improved internal collaboration<\/strong> can be achieved with modern office tools. Many banks have legacy processes designed for linear workflows\u2014for example, sending credit applications as email attachments to multiple stakeholders for approval. This process is cumbersome, often involving a lot of back and forth to reconcile a \u201cgolden truth\u201d of client exposure sourced from multiple systems. Redesigning these team workflows with modern technology like Copilot Pages<\/a> provides a single, persistently updated canvas that allows for multiparty interactive collaboration that integrates all relevant data. <\/li>\n\n\n\n
- Operational efficiencies <\/strong>can <\/strong>be greatly enhanced with AI. Consider Letters of Credit processing, a mainstay of classical trade finance which remains paper-based to this day, with literally billions of pieces of paper circulating between parties at any given time. Banks must examine all these documents for compliance\u2014a costly effort requiring a skilled workforce. To ease this burden, Microsoft partners leverage Azure technologies<\/a> to automate much of the work, freeing bank staff to deal with exceptions rather than the bulk of mundane examination. Microsoft Document Intelligence Read Optical Character Recognition (OCR)<\/a> dematerializes trade documents while AI algorithms spot compliance issues, detect signs of trade-based money laundering (TBML), and meet other requirements to complete a transaction before payment. The result is improved quality and profitability, as well as new data insight APIs from digitized trade documentation. The next wave of this process will apply semi-autonomous Agentic AI that further understands context and can complete multiple assignments digitally. <\/li>\n\n\n\n
- Knowledge Management<\/strong> tools using natural language can advance the effectiveness of staff and banking operations. Retrieval Augmented Generation (RAG) technology<\/a> will reason over a bank\u2019s broad SharePoint catalogue of material and surface only relevant information for a given request. This will be especially useful in training bank staff who are not familiar with the day-to-day technicalities of trade finance. For example, legal documentation can be surfaced as needed for each appropriate use case. In certain circumstances, this could be extended as curated material directly to clients. Using natural language copilots can simplify how staff and clients learn and understand trade finance, which historically has been a specialized field.\u00a0\u00a0<\/li>\n\n\n\n
- Customer service tools <\/strong>can enhance the customer experience. One of the greatest areas for improvement with natural language processing and copilots is client service problem resolution. Agent-first workflow tools, such as Microsoft Dynamics 365 Contact Center<\/a>, immeasurably improve efficiency by putting all the facts at an agent\u2019s fingertips. Accessing a bank\u2019s catalogue of products, an agent can also upsell solutions while reducing time spent on “swivel chairing\u201d between different systems. These tools can also be designed to enable client self-help functions that reduce mundane repetitive calls to the bank, like status of a shipment or payment. Client queries with an agent can be in written form, spoken through Interactive Voice Response (IVR), or conversed with an avatar. \u00a0<\/li>\n<\/ol>\n\n\n\n
Get started on your modernization journey <\/h2>\n\n\n\n
Trade finance AI is not just for big banks that finance global supply chains. In fact, the impact of AI automation could be greater for regional and smaller banks where skilled staff are fewer and transactions are less frequent, but where client needs require receivables discounting, performance bonds, or other working capital assistance. Moreover, increasing demand for trade financing by small and medium-sized enterprises (SMEs) in developing nations is a significant driver of market growth. <\/p>\n\n\n\n
The benefits of modernization impact banks of every size and geography. To help understand how your organization can explore the new opportunities, begin by engaging with your Microsoft representative. They can help develop strategies and solutions that deliver immediate and long-term benefits to meet your bank\u2019s unique needs.<\/p>\n\n\n\n
Unlock business value and deepen customer relationships<\/p>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t
How data became the third leg of bank business models <\/h2>\n\n\n\n
From its inception, banking has always been a business of data\u2014its movement and processing, and the insights derived therefrom. <\/p>\n\n\n\n
As financial intermediaries, banks survived for centuries based on data at the heart of a two-sided business model: taking deposits (liability ledger) and making loans (risk assets). Profit was the lucrative spread between these two pillars. Business cycles and financial crises have come and gone but this fundamental model has not changed. <\/p>\n\n\n\n
Technology has been integral to data management since the rudiments of data processing automation and Management Information System (MIS) dashboards. The rise of the modern real-time data economy, however, completely alters the environment in which banks operate. <\/p>\n\n\n\n
Retail banking was first to transform by monetizing fragmented data in correlation with context and other factors. That beginning marked a signpost to a new space where the value of insights became the third important leg of bank business models. With the power of AI and the simplicity of natural language copilots, we are at the start of a new epoch which marks a profound transformation in banking.\u00a0<\/p>\n\n\n\n
Developing this trajectory, it is clear that Business-to-Business (B2B) flows contain much richer datasets to be monetized across a broader spectrum of economic activity, from local Main Street to global supply chains. Corporate banking is the epicenter of this next wave of B2B value creation through its main business lines: working capital management, payments and transaction banking, and, in particular, trade finance. <\/p>\n\n\n\n
Unlocking B2B data insights is driving banking transformation <\/h2>\n\n\n\n
Trade finance is a natural starting place for bank modernization. It is unusually rich in untapped B2B details, it is super relevant to a bank\u2019s overall commercial banking proposition, and it offers the most easily addressed \u201clow hanging fruit\u201d for return on investment (ROI) due to the prevalence of so many manual processes. <\/p>\n\n\n\n
Note that this near-term upside should not be confused with the industry\u2019s longer-term policy agenda on \u201ctrade digitization,\u201d<\/em> which focuses on transitioning from traditional, paper-based processes to digital formats. Global bodies such as the Bankers Association for Finance and Trade (BAFT), the International Trade and Forfaiting Association (ITFA), and the International Chamber of Commerce (ICC) will, in due course, develop legal frameworks that facilitate this transition. But before that, there is a clear business case within banks to adopt currently available new technologies in a race to transform client experience, improve operating efficiency, and gain marketplace advantage from B2B data insights. <\/p>\n\n\n\n Banks are naturally rich in B2B data as a consequence of their existing franchises and the daily flow of transactions through their processing systems. Yet, insights from the graph of these non-linear B2B relationships languish trapped and untapped in legacy silos. With this in mind, Microsoft has been leading the development of new AI-focused technologies for knowledge workers in today\u2019s modern banking environment. These include natural language copilots, starting with Microsoft 365 Copilot<\/a>, custom copilots built with Microsoft Copilot Studio<\/a>, and Agentic AI<\/a> for more complex tasks. Concurrently, solutions like Microsoft Fabric<\/a> can unify data for analysis and action from disparate sources irrespective of the technical environments in which they sit. \u00a0<\/p>\n\n\n\n Microsoft\u2019s data tools unlock data insights and help make trade finance processes more efficient and accessible. Importantly, they are all designed with the same security, compliance, and content entitlements that are already established within banks, so getting started is easier. <\/p>\n\n\n\n The roadmap that banks are adopting for trade finance modernization follows five simple steps, starting with the basics of helping colleagues do their work better: <\/p>\n\n\n\n Trade finance AI is not just for big banks that finance global supply chains. In fact, the impact of AI automation could be greater for regional and smaller banks where skilled staff are fewer and transactions are less frequent, but where client needs require receivables discounting, performance bonds, or other working capital assistance. Moreover, increasing demand for trade financing by small and medium-sized enterprises (SMEs) in developing nations is a significant driver of market growth. <\/p>\n\n\n\n The benefits of modernization impact banks of every size and geography. To help understand how your organization can explore the new opportunities, begin by engaging with your Microsoft representative. They can help develop strategies and solutions that deliver immediate and long-term benefits to meet your bank\u2019s unique needs.<\/p>\n\n\n\nA benefits-driven roadmap for trade finance modernization <\/h2>\n\n\n\n
\n
Microsoft\u2019s Financial Meeting Prep<\/a> on Microsoft Teams, launched with LSEG<\/a>, shows the simplicity of how this could work in trade finance. Financial Meeting Prep helps organize more effective meetings through a single view of all relevant content. It drives better meeting outcomes and improves engagement, job satisfaction, and revenue growth. By the same token, trade finance product managers can transform how they conduct research in developing and managing new products with Copilot for project<\/a>. Mundane tasks, like generating monthly product performance reports, can be automated with conversational copilots that are embedded in familiar tools like Copilot in Excel<\/a> and Copilot for Power BI<\/a>. This provides all users with proactive drilldown capabilities to discover desired insights without reversion to a lot of manual rework.<\/li>\n\n\n\nGet started on your modernization journey <\/h2>\n\n\n\n