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New MIT Paper: Cut Finance Oversight Time by 40%: From Compliance to Budgeting

New MIT Paper: Cut Finance Oversight Time by 40%: From Compliance to Budgeting

Financial leaders today face a difficult balance. On one side is the familiar world of compliance with annual audits, quarterly reports, and reconciliations. On the other is a fast-moving reality of complex supply chains, volatile markets, and rapid capital flows. A recent academic paper highlights the growing tension between these two worlds. Traditional audit and planning processes are no longer enough to keep up.

The Core Challenge: Oversight Is Falling Behind

The paper highlights three major risks for finance teams:

  • Information Overload. The volume of financial and operational data overwhelms review processes. Risks are buried until it is too late.

  • Lagging Oversight. Audits and compliance checks remain backward-looking. Anomalies often appear only after they have distorted results.

  • Systemic Fragility. Complex reporting systems create space for both errors and manipulation. These issues are often uncovered slowly.

For Controllers and FP&A teams, these risks are not academic. They are daily challenges that undermine trust with executives, investors, and regulators. Markets punish uncertainty, and delays in oversight can quickly damage valuation.

Why This Matters for Controllers and FP&A

Controllers must ensure accuracy and compliance. FP&A must guide strategy with forecasts and plans. Both functions face the same obstacle: delayed insight.

  • Deloitte found that 70% of finance leaders rank manual reconciliations as their top time drain.

  • Gartner estimates that finance teams spend up to 40% of their time collecting and validating data instead of analyzing it.

  • Hackett Group benchmarks show that inaccurate or stale forecasts cost companies 6–8% of annual revenue.

Controllers often uncover irregularities after the fact. FP&A teams frequently base forecasts on incomplete data. Together, this widens the gap between compliance and foresight.

A Shift Toward Continuous Oversight

The paper calls for a new model of oversight. Finance must adopt what can be called dynamic assurance. Instead of static point-in-time reviews, oversight must be continuous.

  • Proactive Anomaly Detection. Reduce the time to uncover irregularities from weeks or months to hours or days.

  • Continuous AI-driven Forecasting. Stress-test assumptions quickly. Accenture reports this can cut planning cycles by 30 to 50 percent.

  • Integrated Intelligence. Connect oversight with operational data in real time. McKinsey finds this can increase forecast accuracy by 20 to 25 percent.

This evolution does not replace audits or compliance. It strengthens them with always-on intelligence and gives finance leaders more time to analyze and guide strategy.

Technology’s Role in Closing the Gap

Technology now delivers measurable improvements.

  • For Controllers, machine learning tools can reduce false positives in anomaly detection by up to 60 percent. This frees staff to focus on critical issues.

  • For FP&A, predictive analytics and rolling forecasts can shrink planning cycles from six weeks to two. At the same time, accuracy improves in volatile markets.

The payoff is clear. Faster detection means fewer surprises. More accurate forecasts mean better allocation of resources. Finance teams that move to continuous oversight earn credibility with executives, boards, and investors.


AuditFlow helps Controllers surface anomalies faster and with higher accuracy, turning weeks of review into hours of detection.
BudgetFlow gives FP&A leaders AI-powered forecasting and scenario analysis, cutting planning cycles and improving accuracy. Both tools support the shift the paper calls for. Finance can move beyond compliance and into foresight.


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Reports & Whitepapers

AuditFlow Whitepaper

The Hidden Cost of Correcting Historical Accounting Errors

Why hospitals can no longer afford spreadsheet-driven account remediation and how AI is helping mid-sized finance teams cut audit prep by 85%.

Healthcare finance teams spend too much time on manual tasks like correcting miscodings, reconciling entries, and preparing for audits with outdated tools. This white paper highlights the hidden costs:

  • 30% of finance hours spent on remediation

  • 40% of analyst time spent gathering, not analyzing data

  • Major remediations cost $0.5–3M and reduce net income by ~$16M

It also shows how AI tools like AuditFlow™ can:

  • Detect anomalies in GL data in minutes

  • Cut remediation time by up to 85%

  • Accelerate the monthly close by up to 7 days

This paper explains how automation provides strategic leverage for lean finance teams.

 

Want to see what AuditFlow finds in your own data?
Contact Us 

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Corporate Finance Blog

How Should FP&A Be Using AI?

How Should FP&A Be Using AI?

Key takeaways

  • AI gives FP&A teams more time to think, not just calculate

  • Small wins like faster variance analysis build confidence without major disruption

  • FP&A shouldn’t fear AI—like Excel before it, it’s a tool to amplify expertise

  • Augmentation, not automation, is the right entry point for AI in finance

  • Adoption starts with time savings, not transformation

Why FP&A Is Naturally Cautious About AI

FP&A teams are often the most trusted thinkers in any organization. They’ve built their credibility on precision, self-reliance, and a deep command of financial models. And they’ve done it largely with Excel, ERP exports, and old-school logic.

So when AI shows up and suggests doing the thinking for them, there’s bound to be hesitation.

Unlike calculators or spreadsheets—where every formula can be inspected—AI can feel like handing over the wheel. Even if the destination is the same, not knowing exactly how you got there can be unsettling.

Start Small, Prove Value Early

AI adoption in finance shouldn’t start with a vision of mass automation. Instead, it should start like Excel once did: as a productivity tool. The best AI deployments begin with narrow, measurable wins that make an analyst’s life easier.

Examples:

  • Auto-clean ERP data to eliminate rework
  • Suggest variance drivers without digging through a dozen pivot tables
  • Generate base-case forecast scenarios with one click
  • Allow CFOs to ask questions like “What’s driving margin compression?” and get answers in seconds

 

These aren’t revolutionary. But they’re time-saving, trust-building, and momentum-generating.

A Practical Path for FP&A Teams

  1. Pick a visible pain point—think recurring manual work like monthly forecast updates
  2. Implement a narrow AI tool that complements your workflow (not overhauls it)
  3. Compare results side by side with the old method
  4. Measure time saved, not just accuracy improved
  5. Share wins across the team to shift the mindset from threat to value

The goal isn’t to turn finance into data scientists. It’s to free up time so smart people can do smarter work.

What Changes – and What Doesn’t

AI doesn’t change the fundamental value of FP&A: being trusted advisors to leadership. What it changes is how quickly and confidently they can deliver insight. The best analysts won’t be the ones who write the best macros. They’ll be the ones who can explain the story the AI is telling—and challenge it when necessary.

In short, AI isn’t the driver. It’s cruise control. FP&A is still at the wheel.

FAQs

Q1: Will AI replace FP&A roles?
No. It replaces repetitive tasks—data prep, reforecasting, reconciliations—not the thinking, interpretation, or business judgment.

Q2: What’s the first thing we should automate?
Look for low-risk, high-friction tasks: data cleanup, variance flagging, or baseline forecast generation.

Q3: Do we have to change platforms or workflows?
Not at all. Good AI tools integrate directly into your current environment—Excel, Power BI, or ERP dashboards.

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Corporate Finance Blog

What Are the Best AI Tools to Detect Financial Anomalies During Audits?

What Are the Best AI Tools to Detect Financial Anomalies During Audits?

In a nutshell

  • AI detects 90%+ of high-risk transactions humans miss

  • Real-time flagging means continuous auditing, not annual sampling

  • Leading platforms: AuditFlow, MindBridge, AppZen, HighRadius

  • Typical time saved per audit: hundreds staff-hours on a $1B revenue company

  • Integrates with SAP, Oracle, Microsoft Dynamics out of the box

The anomaly-detection landscape (2025)

VendorPrimary strengthIdeal user
AuditFlowDeep ERP, SCM, CRM analysis & explainable MLSmall, mid-market & large enterprises
MindBridgeRisk scoring on GL & sub-ledgers GartnerAudit/insurance firms
AppZenAI spend monitoring & T&E complianceGlobal shared-service centers
HighRadiusCash-flow & AR anomaly alerts Stack AITreasury & AR teams

How anomaly-detection AI works

  1. Data ingestion: Pulls millions of GL lines or AP invoices.

  2. Feature engineering: Creates thousands of statistical & relational features (e.g., Benford scores, employee-vendor matches).

  3. ML & rules engine: Unsupervised clustering plus business-rule overlays catch both novel and known risks.

  4. Risk scoring & workflow: Items above threshold route to accountants for review.

Implementation tips

  • Start small by importing the last two to four years of accounts data.

  • Embed in workflow so accountants review in the same UI.
  • Iterate monthly; models self-learn as new risks emerge.

  • [Optional] Tune thresholds with historical anomaly or outlier cases.

Value metrics

  • % high-risk items auto-cleared vs. false positives

  • Manual testing hours eliminated

  • Detected dollar value and number of accounts of misstatements

  • Reduced internal audit costs

FAQs

  1. Will AI replace auditors?
    No; it augments them by prioritizing risky items.

  2. How long to deploy?
    Most teams see first results within two weeks after data connection.

  3. Can I customize rules?
    Yes. AuditFlow’s rule builder supports custom thresholds and regex check