Explore how Generative AI in finance can drive advanced financial modeling, strategic risk assessment, conversational decision support and regulatory intelligence.

1. The Future-forward CFO
Harnessing the Power of
Generative AI in Finance
Beadle Navaraj
Finance Practice Lead & Senior Partner, CFO Advisory Services

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Artificial Intelligence (AI) has transformed the
finance function by automating processes,
optimizing operations, enhancing enterprise
visibility and providing data-driven insights.
However, the emergence of Generative AI has
unveiled a vast realm of possibilities for enhanced
decision-making, customer satisfaction, employee
experience and risk monitoring. This transformative
technology is re-defining the CFO’s role,
positioning them as the Chief Future Officers.
In a research report titled, ‘The economic potential
of Generative AI,’ McKinsey notes that Generative
AI has the potential to add USD 2.6-4.4 Trillion in
annual value across industries.1
Gartner believes
that CFOs should spearhead the adoption of this
powerful technology – taking proactive ownership
rather than solely relying on their IT departments –
to ensure that it aligns with the business strategy
and full range of company operations.2
1
The-economic-potential-of-generative-ai-the-next-p
roductivity-frontier-vf.pdf (McKinsey & Company)
2
CFOs should lead adoption of generative AI: Gartner
| CFO Dive

3. The New Advisor in the
CFO’s Office
The Potential of Generative AI in Finance & Accounting Operations (FAO)
Open Data
Generative AI elevates the decision-support capabilities of traditional AI, such
as accurate planning and forecasting, as well as fraud and revenue leakage
detection, to unprecedented levels.
■ Market
■ Industry
■ Competitors
■ Customers
Inputs from AI
ethics and
governance to
source data
responsibly
and avoid fake,
duplicate,
personal or
unauthorized
sources
■ Business strategy and
goals
■ Upstream data / data
warehouse feeds
■ ERP data
■ Plans and budgets
■ Bank feeds
■ Tax working
■ Investment data
■ Financial statements
■ Executive papers and
presentations
■ Contracts and
agreements
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Financial
Modeling
Regulatory
Compliance
Risk
Assessment
Business
Partnering
Generative AI
Decision
Support
Reporting
Company Data
The spectrum of input and output – from data and trained models to the
generation of context-specific content in text, speech, audio and image formats

4. Let’s dive into a few use cases of
Generative AI in finance:
1) Advanced Financial Modeling
Generative AI can create sophisticated financial models that consider multiple
variables, scenarios and market conditions. For instance, it can augment
AI-generated reports with recommendations on capital markets (using
sentiment analysis and new classification) to increase ROI on investments.
Such recommendations can be leveraged in market analyses to optimize the cost
of capital and identify potential buyouts by analyzing financial statements and
annual reports of targeted companies.
2) Strategic Risk Assessment
Powered by Large Language Models (LLM), Generative AI can analyze massive
amounts of historical and incoming data, market trends and emerging patterns to
provide real-time reports on potential fraud and revenue leakage.
Further, it can provide voice-based alerts to financial controllers and internal
auditors, enabling proactive action to mitigate risks and uncertainties. The system
can also flag repeat defaulters and delayed collections, facilitating early or
preventive action.
3) Conversational and Interactive Decision Support
While AI tools provide predictive insights, Generative AI takes this capability to
unparalleled heights. CFOs and treasury teams can now interact with audio-based
conversational AI to query the daily cash position of the company, upcoming large
payments, working capital shortfalls, Forex exchange exposure, hedging
requirements, cash forecast and other Finance and Accounting (F&A)
requirements. These conversational AI systems generate responses in real-time,
presented either as audio outputs or in the form of text / images, depending on
the nature of the user’s query.
4) Compliance and Regulatory Intelligence
This conversational capability is invaluable in handling various accounting
conventions and its interpretations (including International Financial Reporting
Standards (IFRS) and local Generally Accepted Accounting Principles (GAAP)),
policies and control frameworks. Generative AI can also alert CFOs on new
regulations and produce summarized tables of all regulatory changes.
Apart from ensuring compliance and reducing associated risks, Generative AI can
streamline onboarding processes by assisting in the analysis of customers,
suppliers and partners through comprehensive reports containing essential
information, including background information, financials, sanctions and
Anti-money Laundering (AML) checks.
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5. Building Effective
Safeguards
5) Simplified, Enhanced Reporting
Generative AI can enable CFOs to access daily views of the income statement and
balance sheet, with interactive drill-downs that offer insightful interpretations of
reported numbers. In addition, the tool can generate notes and disclosures on the
financial statements based on the variances between budgeted and actual
figures.
The time and effort expended in producing annual reports, board papers,
accounting policies, whitepapers, investor analyses and executive packs can be
drastically reduced, significantly easing the burden on finance teams during
internal and external reporting cycles.
6) Integrated Business Partner
Generative AI empowers CFOs to become strategic partners to other business
units. It can create a real-time report summary for cost center managers and
business partners – analyzing revenue and expenses and trends, patterns and
insights for accurate planning, budgeting and forecasting. With such insights,
CFOs can contribute to cross-functional decision-making processes, identify
growth opportunities, optimize resource allocation and drive business
performance.
While Generative AI gathers momentum and its positive impact is unquestionable,
ethical concerns about potential biases, lack of interpretability and increased
vulnerability to adversarial attacks remain. According to Harvard Business Review,
79 percent of senior IT leaders in a recent survey reported having security concerns
around this technology and another 73 percent were concerned about biased
outcomes.3
Without clear ethical guidelines in its design and deployment,
Generative AI in accounting operations can lead to unintended consequences
and pose significant risks to the organization.
There is also a need to ensure change management for better outcomes. Effective
communication and training will be important for organizational readiness.
Change management strategies must focus on building processes to address
employee concerns, foster collaboration between humans and AI systems, and
promote a culture of continuous learning and adaptation.
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3
Managing the Risks of Generative AI (Harvard Business Review)

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