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Art of Accounting: Telling a client the reality of their business’ value

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A client told me that his business was worth $10 million and he wanted to know how much he would net if he sold it and how it could be invested to provide him with sufficient cash flow in his retirement.

I disagreed and gave him a “ballpark” number off the top of my head and got an angry retort telling me I did not know what I was talking about. Then the problems came.

My client started the business in his garage 27 years ago and now employs 35 people with annual sales of $10 million. He told me that his business is worth the amount of sales he has: “Don’t you know anything about how businesses are valued? Besides, it is growing and in a few years the business will be worth $12 million so it is a bargain at that price.”

There are many ways to value a business and many factors that go into determining the value. My job suddenly became trying to explain this to my client, and to do it in a way that did not upset him more than he already was, without lessening my credibility.

I tried to explain there are many different ways of valuing a private business but to simplify the discussion I would explain two basic ways. I told him that after we go over these, we can get further into values and then apply what we know to his specific company.

The first basic way is based on the earnings with a rate of return applied to the earnings to determine the value. An example is a business with earnings of $300,000 where the investor would want a 20% return. This would value the business at $1.5 million calculated like this: $300,000 ÷ 20%. If the investor wanted a 10% return, the business would be worth $3 million, and if he wanted a 25% return, it would be worth $1.2 million. Explaining this was not easy. Regardless of his or any owner’s attachment, the business is a business whose purpose is to provide an income either to an investor or someone who wants to work in the business and earn their living from it. An investor would want a greater investment return than someone who wants to create a job for themself. However, in either situation the basis for the value is its earnings. In most situations the value is not based on what it would cost to recreate the business, although that is usually the situation when someone starts a business from scratch.

I told the client to set this aside and to let me tell him the other way. And then we’ll get back to what we were talking about. 

The other method is when the buyer has their own motive for wanting to own the company, i.e., what it could do for their present business. That is called a strategic or synergistic buyer. An example is when Amazon.com acquired Pillpack for $1 billion. This instantly gave Amazon.com the ability to ship prescriptions to all 50 states. That $1 billion value was only the value to Amazon.com and likely not to anyone else since Pillpack’s sales were about $100 million with far less profits. Further Amazon.com’s market value increased $20 billion when the announcement was made. No one could consider what Amazon.com paid as a true measure of Pillpack’s value to anyone other than that single buyer. 

Getting back to my client, we discussed whether there might be any strategic value to a potential buyer and whether he could identify a potential situation that would make his company attractive to such a buyer. I also identified some of his business’s value drivers so he could see what might be done to increase its value. I told him to think about our conversation and we would discuss it at a later time.

I then explained that since income was a major factor, we needed to examine what that means. I explained the process of normalizing the earnings to what they would be if someone else owned and ran the business. One example I gave him was that if he had his brother-in-law working for him at a 50% higher salary than that position warranted, we would add that 50% amount back to the profits and get a higher earnings amount that we would work off of. We would do that with every expense item. 

I then suggested a starting capitalization rate, and we came up with a ballpark value for a future starting point for any discussions about the value. To further add salt to his wound, I then told him to expect to net about 60% of any selling price after paying selling costs and taxes. With these types of discussions, I find it much better to get all the negative things out of the way early on so the client knows what to expect.

At that point I was not sure he believed what I said, but it dampened his dream of untold wealth and cooled his thinking of an early retirement. He also became somewhat assured that I understood these situations. 

A takeaway for my colleagues is this is a typical situation and eventually occurs with most of our business clients. A better way of dealing with this is to work this type of discussion into a few regular meetings with your clients to 1) provide a feel or range of what the business might be worth, 2) what the net from a sale would be and the potential cash flow from those proceeds, 3) to identify value drivers, 4) to discuss the possibility of a strategic buyer, and 5) to have your client start thinking about operating the business in a way that could increase its value rather than only increase its earnings.

I co-authored a pretty thorough article on providing a client with a method of valuing their business. If you want a copy of it, email me at [email protected] and just put Valuation Article as the subject. No messages are necessary.

Do not hesitate to contact me at [email protected] with your practice management questions or about engagements you might not be able to perform. 

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Accounting

AI-Driven Automation and Continuous Accounting Frameworks

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The accounting profession is undergoing a fundamental structural transition as enterprise finance departments shift from periodic month-end closes toward automated continuous accounting models. By integrating specialized machine learning algorithms directly into enterprise resource planning (ERP) platforms, chief accounting officers are transforming financial reporting from a retrospective exercise into a real-time operational asset.

The Shift from Periodic Close to Continuous Financial Reporting
Traditional accounting workflows heavily relied on manual data reconciliation, spreadsheet calculations, and multi-week closing cycles at the end of each fiscal period. In contrast, continuous accounting frameworks utilize automated software agents to process, validate, and post transactional data in real time as business activities occur.

Automated bank reconciliation tools cross-reference incoming bank feeds, invoice records, and purchase orders automatically. By resolving transactional variances instantly throughout the month, corporate accounting teams eliminate the traditional workload spikes associated with quarterly and annual closes.

Machine Learning in Audit Trails and Anomaly Detection
Advanced natural language processing (NLP) and machine learning tools are redefining internal audit and financial control environments. Automated systems analyze 100% of general ledger entries, identifying anomalous transactions, duplicate payments, and unauthorized journal entries in real time.

Rather than relying on random statistical sampling, corporate internal auditors can focus their attention on high-risk flags automatically surfaced by algorithmic monitoring platforms. This continuous risk assessment strengthens internal controls over financial reporting (ICFR) and significantly reduces fraud risk.

Evolving Roles for Accounting Professionals
As routine data entry and manual reconciliation tasks become fully automated, the skill set required for accounting professionals is shifting toward data analysis, system design, and strategic business advisory.
– Systems Governance: Accountants are increasingly responsible for monitoring algorithmic accuracy and managing data integration pipelines.
– Business Partnership: Finance professionals leverage real-time financial dashboards to advise operational leaders on margin management and working capital allocation.
– Regulatory Compliance Management: Accounting teams utilize automated platforms to ensure compliance with dynamic tax codes and international accounting standards.

Core Implementation Recommendations
1. Deploy Automated Reconciliation Tools: Integrate continuous transaction processing modules into existing enterprise ERP architectures.
2. Establish Algorithmic Governance Controls: Implement strict internal testing protocols to ensure automated accounting rules comply with GAAP/IFRS standards.
3. Reskill Accounting Teams: Invest in training finance staff on data analytics, workflow automation, and predictive financial modeling.

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Accounting

Global ESG Reporting Standards and Double Materiality Compliance

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Corporate accounting departments face expanding reporting expectations as international sustainability disclosure standards achieve regulatory enforcement across major global jurisdictions. Chief Accounting Officers (CAOs) and corporate controllers are establishing rigorous internal accounting controls to treat Environmental, Social, and Governance (ESG) metrics with the same data precision, auditability, and governance as traditional financial statements.

Regulatory Harmonization Under Global Sustainability Frameworks
The implementation of standardized sustainability reporting frameworks—notably rules established by international sustainability accounting boards—has created unified expectations for public and large private enterprises. Corporations must report standardized metrics covering greenhouse gas emissions (Scope 1, 2, and material Scope 3), energy utilization, workforce demographics, and supply chain governance.

In Europe and other participating international jurisdictions, double materiality principles are mandatory. Under double materiality, organizations must report both how external sustainability risks impact corporate financial performance, and how internal corporate operations affect surrounding environmental and social structures.

Integrating Sustainability Metrics into Core ERP Systems
To provide auditable non-financial data, enterprise organizations are integrating specialized carbon accounting and ESG management platforms directly into core ERP systems. Automated data collectors capture energy utility invoices, logistics fuel consumption metrics, and vendor compliance records in real time.

Establishing automated, traceable data pipelines ensures that non-financial reporting is supported by clear audit trails. This structured approach allows external financial auditors to provide reasonable assurance on sustainability disclosures during annual corporate reporting cycles.

Financial Impacts and Capital Market Disclosure
Accurate ESG reporting directly influences corporate cost of capital and institutional credit ratings. Commercial lenders and institutional asset managers systematically incorporate sustainability metrics into risk pricing models. Companies that demonstrate transparent, verifiable progress in operational energy efficiency and climate risk mitigation benefit from expanded access to green bond markets and lower debt pricing.

Action Steps for Accounting Leadership
1. Implement Double Materiality Frameworks: Conduct comprehensive assessments to identify material financial and operational sustainability metrics.
2. Build Auditable Non-Financial Data Pipelines: Automate ESG data collection within core accounting software to ensure data integrity.
3. Align Sustainability with Annual Financial Filings: Prepare non-financial disclosures concurrently with financial statements to satisfy regulatory audit expectations.

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Accounting

Modernizing Internal Controls: Machine Learning and Continuous Monitoring in Auditing

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Internal audit departments and corporate risk managers are modernizing internal control frameworks by shifting from periodic sampling techniques to continuous monitoring and machine learning analytics. As operational data volumes increase across enterprise organizations, automated control testing ensures financial integrity, prevents corporate fraud, and streamlines annual audit engagements.

The Limitation of Periodic Audit Sampling
Historically, internal and external auditors evaluated internal controls by reviewing random samples of financial transactions—often analyzing less than five percent of total ledger entries. In complex enterprise environments, periodic sampling methods carry inherent risks of overlooking localized financial misstatements, unauthorized disbursements, or operational control breakdowns.

In 2026, progressive internal audit functions are utilizing automated continuous monitoring platforms that evaluate one hundred percent of financial transactions in real time. Continuous control auditing systems continuously monitor general ledger entries, procurement approvals, and expense reimbursements across all operating subsidiaries.

AI-Powered Fraud Detection and Anomaly Identification
Machine learning models trained on historical corporate financial data excel at identifying subtle transactional anomalies that indicate potential fraud or operational error. Automated systems instantly flag duplicate invoice payments, unapproved vendor creation, unusual journal entry timing, and unauthorized override of authority thresholds.

When an anomaly is detected, the automated auditing platform generates an instant risk alert, allowing internal audit teams to investigate root causes immediately. Early detection prevents minor operational errors from escalating into material weaknesses in financial reporting.

Streamlining External Audit Preparation
Continuous internal control monitoring delivers significant benefits during annual external financial audits. External audit firms can review continuous audit logs and automated control testing documentation, reducing the time required for manual field testing.

This integrated approach lowers overall audit compliance fees, reduces administrative burdens on corporate accounting staff, and provides senior management and audit committees with real-time visibility into the organization’s overall risk profile.

Core Implementation Guidelines
1. Transition to 100% Data Testing: Replace legacy sampling methods with automated continuous audit monitoring systems.
2. Deploy Anomaly Detection Algorithms: Implement machine learning models to identify unauthorized transactions and operational control overrides.
3. Align Internal and External Audit Workflows: Coordinate continuous control testing protocols with external auditors to optimize annual compliance cycles.

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