How Gen AI is Powering the Rise of the Finance OneOffice

In an ever-evolving economic landscape, Chief Financial Officers (CFOs) constantly seek innovative ways to safeguard the financial future of businesses. Over the past decade, they have embraced Artificial Intelligence (AI) to improve decision-making, and now, the emergence of Generative AI (Gen AI) is poised to elevate their capabilities further.

AI and Gen AI have limitless possibilities for business leaders. PwC predicts that AI could add nearly USD 16 Trillion to the global economy by 2030,¹ while McKinsey estimates that Gen AI could contribute USD 2.6-4.4 Trillion annually.² This growth is evident in Finance and Accounting (F&A) as well. In a 2022 Gartner survey, 80 percent of CFOs expressed their intent to invest more in AI over the next two years.³

Investments in AI are accelerating the transformation of the “Finance OneOffice,” an integrated operation enabled by seamless access to data from across the organization. HFS Research highlights the importance of the “OneOffice Mindset” for overall F&A operations and strategy, fostering end-to-end alignment across the front, middle and back offices to deliver unmatched stakeholder experience.⁴

To maximize the potential of AI and Gen AI in finance, CFOs must ensure that the Finance OneOffice rests on a strong foundation of four pillars: Data Ingestion, Intelligent Automation, Finance Analytics and Self-service Reporting.

Figure 1: AI-augmented Finance OneOffice Framework

1. Data Ingestion

   Data forms the bedrock for AI-driven transformation, necessitating a digitized environment that allows anytime, anywhere access to data through cloud-based solutions.

   In high demand, finance data lakes act as centralized and scalable repositories that manage structured and unstructured data. With dynamic data ingestion and layering, these repositories convert data into accounting numbers and reports with visualization.

   Finance data lakes rely on system integrators, pivotal in sourcing data from various upstream systems, Enterprise Resource Planning (ERP) systems, bolt-on platforms, banking / customer portals and reporting tools. Real-time data ingestion through data streaming is critical for data-driven decision-making, offering deeper insights into financial performance and optimizing outcomes. Moreover, to further streamline data ingestion and integration, implementing Gen AI for generative coding, mapping and layering holds great promise. This enables a more efficient and automated way of handling data, reducing the time and effort required.

2. Intelligent Automation

   Intelligent automation is a crucial aspect of modern finance operations, but its success hinges on careful preparation. It is vital to build or revamp processes to be fit-for-purpose before embarking on any automation journey, thereby eliminating non-value-added activities and preventing the risk of faster garbage-in, garbage-out scenarios. To maximize existing finance technology stacks, organizations should prioritize optimizing ERP and consolidating systems and platforms. This step ensures the most efficient utilization of the existing infrastructure before venturing into new automation possibilities.

   The potential of AI-powered intelligent automation is boundless, even for complex and ad-hoc finance processes. It empowers financial professionals to streamline tasks like non-standard journal creation, reconciliation review and auto-closure, intelligent self-audit, and touchless invoice processing in multiple languages (using natural language processing) and currencies, among others.

3. Finance Analytics

   Finance analytics is the third crucial process, which involves applying advanced techniques to financial data for meaningful insights and informed decision-making. The adoption of machine and deep learning for descriptive, predictive and prescriptive analytics is increasing. This is helping drive insights across spend, product and profitability, demand and supply, fraud prevention, cash flow prediction, working capital optimization, foreign exchange exposure and hedging, expense rolling forecaster, variance analysis and investment optimization.

   AI-enabled finance analytics equips CFOs with powerful tools to gain deeper visibility into financial trends, forecast outcomes and optimize financial strategies.

4. Self-service Reporting

   Manual processes like collating, consolidating and reporting with commentaries can now be automated through Gen AI platforms, generating new content based on existing data. Annual reports, commentaries and disclosure management can be seamlessly produced in text and image formats.

   Integrating Gen AI into finance introduces significant benefits, particularly in risk reporting. These advantages include voice and text-based alerts, enabling quick detection of potential revenue leakage, duplicate payments and fraudulent activities. It also involves voice-based conversational querying on daily cash position, significant upcoming payments, Forex exposure and funding requirements.

Harnessing Private & Public Data

Gen AI empowers CFOs to create realistic and customizable financial simulations. This helps them test and evaluate different strategic options, optimize resource allocation and enhance financial resilience. By using private and public data, Gen AI-infused initiatives can take finance to new heights, unlocking innovative possibilities and streamlining processes.

• Private Data: Finance teams predominantly rely on internally generated data from upstream processes and core business activities. This vast repository of information aids in recording and reporting the company’s financial performance to its internal and external stakeholders. Consequently, finance finds itself in an advantageous position to embark on its Gen AI journey by initiating the training of internal models, such as the Large Language Model (LLM), through self-supervised and semi-supervised learning. By drawing insights from years of internal data, finance can forge ahead with Gen AI-enabled processes. Crucially, since these data sets are confined to the company's internal domain, any concerns regarding ethical data usage vis-a-vis public data are mitigated at this juncture.
• Public Data: Market and industry data available through the open internet and subscriptions prove invaluable for understanding market conditions, buyer behavior, competitor analysis and industry benchmarks. However, for the forecasts and interpretations by CFOs and executive teams to work well, the accuracy and reliability of data are crucial. It is imperative to source data and reports from authorized and unbiased providers to train internal models with rigorously vetted data. By doing so, finance professionals can make better-informed decisions that drive the company's growth and success.

Partnering for a Future of Limitless Possibilities

Embracing the transformative potential of AI, the conventional Record-to-Report approach evolves into a more insightful Record-to-Analyze paradigm, going beyond the rushed process of just racing to finish reports within stringent finance reporting cycles. With the assistance of AI-augmented intelligent automation, the finance team can leverage self-service report generation, thus focusing on creating actionable insights for the CFO. Using advanced Gen AI-based tools, the finance team can now provide the CFO with a wealth of informed perspectives and well-founded recommendations.

However, the successful transformation of the CFO's office hinges on incorporating critical levers such as risk management, performance management and change management into the transformation roadmap. Moreover, AI ethics and governance will continue to play a crucial role in ensuring the responsible and transparent use of data, upholding ethical standards, fostering trust and ensuring the long-term sustainability of financial decision-making processes.

To learn how WNS can help your organization in the AI and Gen AI powered Finance OneOffice journey, talk to our experts.

References:

1. PwC

2. McKinsey

3. Gartner

4. HFS Research