It has never been a more opportune and uncertain time to be in business. Amidst the growing wave of technological breakthroughs, digital revolutions, and overnight trends, the risks of refusing to adapt are just as relevant as the risks of adopting new tools and approaches without having a solid foundation.

KPMG in Canada has repeatedly observed that deviations between business requirements and system calculations have caused large financial and operational impacts on organizations. That is why, before charging ahead, it pays to look within to uncover hidden assets and liabilities in the organization.

Organizations are increasingly reliant on sophisticated systems to accurately and quickly process data and complex calculations. Changes in personnel, systems and business processes, and the increasing complexity of calculation formulas and data inputs can cause calculation errors in two ways: (1) system anomalies/glitches, and (2) deviations in business requirements and coded system logic.

These system errors and deviations in business requirements can result in hidden assets and liabilities that go unnoticed, resulting in various risks and opportunities for an organization.

• Hidden Liabilities - The impact of calculation errors can grow and ripple outside an organization over many years. If unaddressed, these errors can produce negative financial consequences and increase the risk of liability, especially from regulators and customers who have an increasing expectation that calculations are performed in accordance with established polices and agreements.
• Hidden Assets - Companies may also benefit from uncovering calculation errors being processed to the company's detriment and correct them such that they are either able to recover monies, or at least prevent the error from perpetuating in the future.

Through our experience, we know how common it is for system discrepancies to operate undetected, often for long periods of time, resulting in material financial, operational, and reputational impacts.
 

Discrepancies in system calculations occur across industries and company sizes. Failure to identify said discrepancies can also lead to consequences in all company functions such as:

• Sales/Operations: Under/overcharging customers (e.g., fees or prices) or inaccurate reporting.
• Board of Directors, Internal Audit and Risk Committees: Preventing stakeholders from accurately identifying, measuring, and mitigating business and reputation risks.
• HR/Payroll: Over/underpaying of wages, commissions, pensions, and taxes.
• CFO/Accounting: Producing materially inaccurate or incomplete reports of revenues/expenses.

The advance of data modelling software and the use of artificial intelligence (AI) makes it quicker and more cost effective to do system 'deep dives', resulting in more breadth and accuracy in findings.

Organizations can mitigate risks and identify opportunities at their source by using the following steps:

• Clarifying business requirements and stated policies;
• Performing re-calculations on all, or a large subset of representative data;
• Assessing the root cause and impact of those errors; and
• Working with all relevant stakeholders to remedy the errors and prevent future errors.
   

Conducting a 'deep dive' for hidden assets and liabilities is key to avoiding unwelcome surprises down the road. In recent years, KPMG has helped numerous clients navigate complex business rules and IT systems to uncover errors and quantify, manage, and limit their liability exposure during litigation.

In addition to liability management during litigation, these 'deep dive' investigations have proven to be effective in a wide range of other sectors. For example, KPMG helped financial institutions gain clarity over their sophisticated transaction processing calculations in order to:

• Identify revenue leakages resulting from unknowingly applying generous fees and discounts to customers allowing the business to capitalize on the goodwill;
• Reduce the risk of liability by proactively identifying instances where customers were harmed by erroneous calculations (e.g., overcharging fees) and make remediation payments;
• Allow their Board of Directors and Risk Committee to gain comfort in the organization's system calculations and reduce inherent risks; and
• Decrease the risk of knowledge loss from key personnel departures, and reduce knowledge gaps between business functions and information systems developers.

KPMG also worked with a telecommunications firm to identify the cause of an apparent revenue leakage that resulted from non-billing in a network area. By analyzing all available quarterly data for the given time period, including all billing and financial calculations, we were able to:

• Identify that the cause of the apparent revenue leakage was a mismatch in internal reporting;
• Verify calculation integrity for over 600 million bills and allow the company to achieve granular insight into the revenue earned per activity; and
• Identify plans that were set up by exploiting internal system control deficiencies, and allow them to close loopholes.
   

It is through engagements such as those described in this article that KPMG has noted, in both large and small organizations, the prevalence of system errors and the magnitude of the financial, operational and reputational consequences that resulted from the discrepancies hidden below the surface. We have demonstrated that it is possible to rebuild complex calculation systems in a robust and agile manner, as well as process massive amounts of data over a short period of time.

Time and time again, we have observed that proactively planning to find hidden assets and liabilities is much more effective and is much less disruptive to business operations.

While AI, automation, and machine learning can enable tremendous innovation and growth, their outputs are not immune to error. Decisions made by AI algorithms (e.g., to approve a loan application) will be challenged. The onus will be on the company to ensure they are not inadvertently breaking competition, consumer protection, or other laws and requirements through AI automation.

As such, internal assessments will become even more critical to ensuring intelligent machines are doing what they are supposed to do, and complying with contractual/business rules and regulations.

There are significant benefits to be gained from proactively identifying hidden assets and liabilities. It is key to carry out these investigations in advance of disputes, regulatory challenges, or irrevocable damage. Companies reacting to liabilities that surface will focus solely on addressing the liabilities to limit the disruption to their business, and will often miss the opportunity to identify hidden assets.

By planning ahead and investing in processes to uncover the hidden assets and liabilities within, organizations can mitigate the impact an error can have on its reputation and liabilities, minimize the disruption to the business, and improve their financial results.