How AI Is Reshaping Finance and the Accounting Shortage

Table of Contents

1. The Roots of the Accounting Shortage
2. How AI Is Changing the Game
3. Beyond Efficiency: Reinventing Roles in Finance
4. Case Study: Scaling Finance with AI
5. The Broader Impact on the Financial Sector
6. Peering into the Future of Finance
7. Balancing Technology and Talent

Key Takeaways

• A widening talent gap and rising demand for specialized skills are driving the accounting shortage.
• AI in accounting is automating repetitive tasks and processing vast amounts of data in real time, freeing finance teams to focus on strategic work.
• Financial institutions are leveraging fraud detection, credit decisions, and risk management powered by large language models and machine learning.
• To maintain customer satisfaction and competitive edge, organizations must blend human expertise with AI systems, upskill their workforce, and rethink traditional roles.

The Roots of the Accounting Shortage

Across the financial services industry, firms are grappling with an acute shortage of accounting talent. An aging workforce, evolving regulatory demands, and the complexity of modern finance have created a gap that universities and training programs struggle to fill. According to GLG’s analysis of the accounting crisis, shrinking accounting teams are unable to keep pace with core activities like journal entries, reconciliation, and month-end close processes.

At the same time, traditional accounting roles spend up to 60 % of their time on repetitive tasks—data entry, report generation, and basic compliance checks—leaving little bandwidth for higher-value activities such as risk management, strategic planning, and advising business partners.

How AI Is Changing the Game

Enter artificial intelligence (AI) and machine learning (ML). By automating the drudgery, AI systems can sift through vast amounts of data in real time, identify anomalies, and even suggest corrective actions. For example:

• AI-powered reconciliation tools match payments to invoices instantly, slashing manual effort by up to 80%.
• Large language models can draft narrative financial statements or answer queries about ledger balances, acting as an always-on analytics assistant—learn more in our AI in Accounting overview.
• Fraud detection algorithms monitor transactions for suspicious patterns across financial institutions, reducing exposure to credit risk.
• Direct Deposit solutions automate and track deposits, delivering funds faster and more securely.

This AI impact not only accelerates processes but also improves credit decisions by integrating external data—market indicators, credit scores, and customer histories—into dynamic risk models.

Want to dive deeper? Check out our blog on AI payment processing and see how you can automate your entire payables and receivables lifecycle.

Beyond Efficiency: Reinventing Roles in Finance

As automation takes over routine workflows, the value of human accountants shifts toward interpretation, strategy, and stakeholder engagement. Finance teams are evolving from number-crunchers to business partners, focusing on:

• Customer service and stakeholder communication
• Scenario analysis and forward-looking forecasts
• Designing risk-management frameworks that incorporate AI outputs

However, the transition isn’t without challenges. Leaders must invest in training programs that equip staff with data literacy, change-management skills, and a comfort level with AI development frameworks.

Case Study: Scaling Finance with AI

At Teampay (now part of Paystand), the finance organization faced ballooning volumes of purchase requests and AP invoices. By implementing AI-driven tools, they achieved:

• Real-time visibility into spend
• Automated three-way matching of POs, receipts, and invoices
• Reduced cycle time from requisition to payment by 50 %

Read more about their journey in our Scaling Finance playbook.

The Broader Impact on the Financial Sector

The adoption of AI isn’t limited to accounting teams. Across the financial sector, institutions use machine learning for:

• Credit-risk scoring that adjusts to market shifts
• Continuous fraud detection across payment rails
• Personalized service through AI chatbots, boosting customer satisfaction

These developments are driving a virtuous cycle: better analytics lead to faster, more accurate decisions, which in turn free up talent to tackle high-impact projects.

Peering into the Future of Finance

Looking ahead, several trends will shape the next wave of transformation:

• Generative AI in Finance – models that not only analyze but also generate forecasts, commentary, and dynamic dashboards.
• AI and blockchain – combining the transparency of distributed ledgers with smart contracts to automate reconciliation and smart triggers.
• Adaptive risk frameworks – systems that continuously recalibrate credit decisions based on real-time economic indicators.
• Embedded intelligence – finance capabilities are seamlessly woven into enterprise applications, from ERP to procurement platforms.

Balancing Technology and Talent

While AI holds immense promise for addressing the accounting shortage, organizations must strike the right balance:

1. Upskill existing staff with data analytics and AI literacy.
2. Preserve ethical oversight to ensure AI-driven decisions remain transparent.
3. Redesign roles to emphasize human judgment, empathy, and strategic influence.

By embracing both automation and human ingenuity, finance leaders can turn the current talent crunch into an opportunity, elevating their teams, enriching the quality of insights, and driving long-term resilience.

For a broader look at what’s next, visit our **Future of Finance** hub.