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Chapter 6: Intelligent Automation in Accounting and Finance—From Accounts Payable and Accounts Receivable to Forecasting and Fraud Detection

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6-1. The Three Pressures Facing CFOs and the Growing Expectation for AI

CFOs around the world, including in Japan, are facing three major pressures.The first is “labor shortages and increasing workload.” Requirements such as financial reporting, disclosure, taxation, and ESG compliance continue to expand, while back-office talent is difficult to scale.The second is the need to balance “speed and accuracy.” As exchange rates, interest rates, and supply chain risks fluctuate, management demands faster and more precise scenario analysis.The third is “compliance and governance.” Organizations must pursue automation and efficiency without weakening internal controls or audit readiness.

Against this backdrop, a survey shows that 56% of finance leaders are already using AI as of 2026—double the level in 2023. AI is no longer an experimental tool for advanced companies; it is rapidly becoming a standard capability in finance functions.



6-2. AP (Accounts Payable) Automation — Reducing Processing Time and Cost

One of the most tangible applications of AI in finance is accounts payable (AP) automation.

AI-powered AP solutions can automate the entire process:

  • Extracting invoice data using OCR and machine learning

  • Matching invoices with purchase orders and contracts (2-way / 3-way matching)

  • Recommending GL codes and cost centers

  • Routing approvals automatically

  • Detecting duplicate invoices and potential fraud

For example, the AI-enabled invoice platform Stampli uses an AI assistant called “Billy” to automate invoice capture, data entry, GL coding, PO matching, and approval workflows. Companies such as Coca-Cola and McDonald’s have reported up to an 80% reduction in invoice processing time, allowing AP teams to focus more on strategic analysis and cost optimization.

AP automation goes beyond reducing manual work. By analyzing payment patterns in real time, AI can improve cash flow forecasting accuracy, maximize early payment discounts, and detect anomalies or errors at an early stage.



6-3. AR (Accounts Receivable) and Cash Application — Automating Payment Matching

AI adoption is also advancing on the accounts receivable (AR) side. Cash application—the process of matching incoming payments to invoices—has traditionally been highly manual, especially when payments cover multiple invoices or contain incomplete remittance information.

AI can:

  • Automatically infer invoice matches even from incomplete references

  • Allocate payments across multiple invoices

  • Detect irregularities or discrepancies early

As a result, processing time is significantly reduced. Some reports indicate that around 90% of payments can be automatically matched, with only exceptions requiring human intervention.

This leads to shorter days sales outstanding (DSO), improved receivables management, and greater stability in cash flow—one of the most critical KPIs for CFOs.



6-4. Forecasting and Scenario Planning — The “Brain” of the AI Finance OS

If AP/AR automation represents the “hands and feet” of finance, forecasting and scenario planning represent the “brain.”

Based on analyses cited by PwC, applying AI to financial planning can improve both the speed and accuracy of forecasting by up to 40%.

AI can process multiple variables simultaneously, including:

  • Historical revenue, cost, and cash flow data

  • Exchange rates, interest rates, and commodity prices

  • Order backlogs and supply chain data

It can then simulate multiple scenarios—baseline, optimistic, and pessimistic—in real time.

With these insights, CFOs can make data-driven decisions on:

  • Investments, hiring, and inventory adjustments

  • Dividend policies, share buybacks, and financing strategies

  • Risk tolerance and financial buffers

In Japan, surveys show that financial institutions increasingly view AI’s primary value as enabling advanced analytics and decision support, rather than just improving operational efficiency.



6-5. Fraud Detection, Compliance, and Audit Support

AI also plays a critical role in fraud detection and compliance.

AI-enabled AP/AR systems can:

  • Identify anomalies that deviate from historical transaction patterns

  • Flag suspicious invoices (duplicates, inconsistent amounts, abnormal vendor data)

  • Check deviations from contractual terms and payment conditions

Industry analyses consistently identify invoice processing, anomaly detection, and knowledge management as key AI use cases in finance.

In addition, generative AI is being used to support reporting processes, including drafting financial statements, disclosures, and ESG reports. While ultimate accountability remains with CFOs and finance leaders, a workflow in which AI generates initial drafts and humans review and refine them is becoming increasingly standard.



6-6. Back-Office Automation in Japan — The Case of LayerX

A representative example of back-office automation in Japan is the AI SaaS startup LayerX. The company offers “Bakuraku,” a platform that automates expense management, invoice processing, and corporate card operations, and has been adopted by over 15,000 companies.

Following the introduction of Japan’s invoice system and updates to electronic bookkeeping laws, the digitization of invoices and receipts has accelerated. At the same time, the workload of back-office functions has increased.

LayerX addresses this challenge by providing AI-driven solutions that streamline workflows across finance, tax, procurement, and HR. In 2025, the company raised $100 million in funding, reflecting strong demand for such solutions.

This case highlights how Japan is entering a phase where companies are redesigning their entire back-office operations with AI. While local regulations and business practices create barriers to entry, they also present significant opportunities for vendors that can adapt effectively.



6-7. AI Adoption in Japan’s Financial Sector — A Cautious but Steady Approach

In Japan’s banking, securities, and asset management sectors, AI adoption is progressing primarily in analytics and back-office optimization.

A 2025 survey shows that:

  • About half of financial institutions are investing in AI for research and analytics

  • Data management, operational efficiency, and risk/fraud management are key priorities

  • Large-scale AI adoption in front-office trading remains limited

At the same time, new solutions such as AI-powered post-trade platforms and operational agents are enabling a shift from reactive processes to predictive operations—such as anticipating settlement failures or optimizing inventory.

Regulators in Japan have also begun emphasizing not only the importance of responsible AI use but also the risks of falling behind, suggesting that adoption will accelerate in the coming years.



6-8. Is AI a Threat or a Lever for Finance Professionals?

Will AI replace finance jobs in Japan? Current analyses suggest that it will transform roles rather than eliminate them.

Many organizations are still in the early stages of AI adoption, and the primary focus is on improving analytical capabilities and back-office efficiency rather than reducing headcount.

In practice, AI is increasingly replacing:

  • Manual data entry and reconciliation

  • Routine reporting tasks

  • Standard compliance checks

Meanwhile, human professionals are shifting toward:

  • Handling exceptions and making judgment-based decisions

  • Acting as business partners to operational teams

  • Supporting scenario planning and investment decisions

Given Japan’s workplace culture, which emphasizes collaboration and mutual respect, AI is more likely to be adopted as a “co-worker” that supports human work rather than a replacement.



6-9. The Meaning of “AI Finance Agents” for SMEs

For many small and medium-sized enterprises (SMEs) in Japan, maintaining a full-time CFO or advanced finance team is difficult. As a result, financial visibility, forecasting, and risk management remain structural challenges.

In this context, the concept of AI agents acting as “virtual CFO assistants” is gaining traction.

Such AI agents can provide:

  • Real-time cash flow monitoring and alerts

  • Detection of unusual expenses or transactions

  • Forecasting of upcoming spending spikes

  • Automated invoice matching

  • Budget planning support

This enables business owners to make data-driven decisions without waiting for monthly or annual closing cycles.

For Japanese SMEs facing labor shortages and rising costs, AI finance agents are likely to become a critical infrastructure for survival and growth.



6-10. Bridge to the Next Chapter — Research and Knowledge Management Automation

As we have seen, AI is transforming finance across AP/AR, forecasting, fraud detection, and reporting. However, these capabilities depend on high-quality data and well-organized knowledge.

The next chapter will focus on research and knowledge management, exploring how enterprise search AI and RAG (Retrieval-Augmented Generation) are enabling organizations to query internal information, generate insights, and make decisions in real time.



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Japan E-Commerce Association

Japan Academic Society for E-Commerce

 

Shoji NISHIMURA Lab., Faculty of Human Sciences, Waseda Univ.
2-579-15 Mikajima, Tokorozawa, Saitama 359-1192, Japan

info@jasec.or.jp +81-4-2947-6717

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