While spreadsheets like Microsoft Excel provide a convenient and well-known interface, using them for reconciliation poses significant risks, including errors, inefficiencies, and potential regulatory and audit issues. Spreadsheets are not designed for reconciliation, especially when handling large datasets, as they are prone to errors and lack the advanced features of dedicated reconciliation tools.

Although firms are aware of the drawbacks of using spreadsheets for reconciliation, the absence of immediate consequences often hinders their motivation to make a change. One critical issue is the lack of controls, as macros and sheets can be modified without monitoring, posing challenges to ensuring data integrity. This lack of controls not only makes error correction time-consuming but also complicates tracking changes, presenting risks from an audit and regulatory perspective.

The audit risk is another concern, as auditors require transparency and repeatability in the reconciliation process. Spreadsheets fall short in providing the necessary level of transparency and auditability, particularly for complex reconciliations like cash proofing, total equity, etc. From a regulatory standpoint, using spreadsheets for data reconciliation is also risky, as regulators demand accurate and complete records. Spreadsheets' deficiency in audit controls and transparency may lead to difficulties in meeting regulatory obligations, potentially resulting in fines, penalties, reputational damage, or legal action.

The risk associated with key person dependence is another issue, as spreadsheets are often complex and understood by only the person who created them. If that person leaves the company, the lack of understanding by others can hinder problem-solving.

Use Limitations:

Spreadsheet templates have limitations in handling large volumes of data or file sharing between incompatible programs, leading to potential problems. For instance, during the UK's 2020 pandemic peak, Microsoft Excel's million-row limit resulted in misplacing nearly 16,000 covid test results, affecting public health efforts.

Human Error:

Relying on manual data entry and maintenance introduces the risk of human errors, potentially causing financial losses and customer service issues. Notable incidents, such as J.P. Morgan's $6 billion loss in 2012 due to an Excel error and the London Olympics ticketing error, highlight the consequences of human mistakes.

Monitoring Weaknesses:

Properly overseeing and checking records is time-consuming but essential to detect anomalies and errors before they become costly. Examples include a spreadsheet error costing TransAlta $24 million and a formatting error leading MI5 to collect unnecessary data in the UK.

Lack of Control:

Decentralized ownership of templates makes it difficult to maintain a single version, leading to devastating outcomes in case of mistakes. The CEO of Mouchel resigned after a spreadsheet error by outsourced actuaries resulted in a wrongly valued pension fund deficit.

Over-Reliance:

Over-reliance on spreadsheets limits capabilities and efficiency, especially if only one person possesses advanced spreadsheet skills. Serious businesses aiming to grow should diversify their technology mix to avoid dependency on manual competence.

Security Issues:

Cyber attackers increasingly use Excel sheets for malware delivery, and spreadsheet security measures are often easily overcome. As remote work becomes more common, ensuring the security of the Wi-Fi networks employees use to access data becomes challenging.

Inferior Insights:

Spreadsheets struggle to provide rich, actionable data as information complexity increases.

Limited Collaboration:

Collaboration using spreadsheets can be challenging due to a lack of central control and real-time updates, hindering progress.

Accountability Gaps:

The lack of version control impacts transparency, creating challenges in regulatory compliance if edits are not traceable.

Undisciplined Workflows:

The ease of use encourages customization of master worksheets, affecting operational accuracy and consistency as the number of users grows. Organisations should be looking for advanced features that can automate and streamline the reconciliation process, minimize errors, and ensure regulatory compliance. Key aspects not to compromise on include advanced matching algorithms, robust audit controls, workflow management tools, native support for buy-side reconciliation processes, and comprehensive reporting and data analytics capabilities.

The automation of reconciliation on a daily or intraday basis can prove invaluable for investment managers. This proactive approach aids in promptly identifying potential issues, effectively mitigating risks associated with financial losses, fraud, or operational challenges.