Generative AI in Finance Functions – A Perspective
Generative AI in Finance Functions – A Perspective
With the massive surge in interest in Generative AI (GenAI), every organizational department is looking at its processes to identify those who can benefit from this new and seemingly miraculous technology. Given the nature of the technology – it is a language model – most initial use cases have centered around organizational functions like administrative, legal, HR, and marketing.
A recent article by the Boston Consulting Group (BCG) sheds light on what CFOs need to do to take advantage of the GenAI bus.
The current share of finance function activities with GenAI applications is relatively small but, the authors predict, is expected to show a typical S-shaped growth curve in the coming years. Since GenAI, at the moment, is excellent at creating content and summarisation, the applications revolve around these strengths of the technology, such as producing content ideas and generating basis analysis and insights with financial datasets.
However, as the technology evolves, the applications are expected to start moving towards more diverse analysis for planning and execution, help with scenario analysis, and eventually graduate to generating actionable, forward-looking insights for finance business partners and support end-to-end process augmentation.
How must CFOs go about transforming their functions?
CFOs need to follow a four-fold approach towards introducing GenAI into their functions. They are:
- Ideate: Create a GenAI strategy for your organization that aligns with the overall business strategy.
- Incubate: Identify and test the early, viable use cases. Get your PoCs going quickly. Fail fast.
- Implement: Implement controls and solutions that address ethical issues and bias concerns.
- Integrate: Work with strategic vendor partners to refine, align, and co-create strategy and innovation in this area.
What are the challenges that CFOs are likely to face?
The challenges are not necessarily unique to CFOs but to all leaders working on implementing GenAI in their respective functions/organizations.
- Data Accuracy: GenAI tools, being language models, struggle to perform accurate calculations. Organizations must be aware of this weakness of LLM models and provide workarounds.
- Data Privacy: Training GenAI models on public clouds could compromise proprietary data, leading to security breaches and leaks.
- Data Governance: GenAI tools lack contextual awareness and real-time information. They need more implicit or explicit governance models for validating outputs.
- Data Hallucinations: GenAI models are susceptible to hallucinations, leading to erroneous information being returned as outputs.
What are other finance functions saying about their adoption of GenAI?
As shown from the figures above, the current use cases overwhelmingly play to GenAI’s strengths in language processing, such as Intelligent Document Processing, Invoice Reconciliation, and Benchmarking/Peer Analysis. Survey participants also recognized the need to share example use cases to foster the adoption of tools and techniques, create internal portals for a one-stop location of resources, and create digital literacy learning paths as initiatives that can help the organization become more data-driven.
As expected, data security, reliance, integrity, and fairness ranked high among AI risks identified by participants.
Viable Use Cases in Finance
The CFO needs to get started with viable use cases in their functions. So what are they? Here is a small list of use cases, spanning the gamut from Transforming Core Processes, Reinventing Business Partnering, Managing and Mitigating Risk, Interactive Financial Data Visualization, and Automated Content Generation to enabling Finance Operations.
All the viable use cases rely on the traditional strengths of GenAI and LLMs and can thus be used for PoCs by CFOs to move the needle for their organizations.
GenAI is an enabling technology that is changing the way organizations process information. CFOs need to join their other C-suite colleagues in ideating, incubating, implementing, and integrating processes and solutions to support the organization’s overall business goals.
SENIOR VICE PRESIDENT,
Arun is the Sr. VP and Practice head for Analytics and AI at iLink Digital with over 24 years of cross-geographical experience in academia and industry. His exceptional proficiency lies in implementing cutting-edge algorithms and solutions for data analysis and pattern recognition in the domains of Engineering, Information Technology, and Biotechnology. His extensive wealth of knowledge and experience, coupled with a robust publication record, positions Arun as a key driving force in pioneering innovation and achieving transformative results within the field of Analytics and AI.
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