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The audit profession has undergone numerous iterations in the last few decades with the onset of greater regulatory requirements, stakeholder needs and technological changes.
There is also a greater level of concern being expressed across all quarters about the quality of audits. For instance, half of all audits monitored by global regulators had deficiencies according to the International Forum of Independent Audit Regulators (IFIAR).
In a world where trust is eroding, it will be important to understand what the introduction of blockchain technology coupled with AI powered by stable algorithms supported by machine learning could mean for the world of audit and assurance in a broader sense.
Without going too much into the description as to what a Blockchain is (essentially a distributed ledger that can only append and not delete transactions posted by different parties or peers in a network), I will attempt to provide a view of what the vision of the future could look like.
It will also be important to note that there are different types of Blockchains. You have on one hand a public or permissionless Blockchain which allows anyone to participate in it – Bitcoin runs on a public Blockchain. You also have a private or permissioned Blockchain which is only open to a defined group of individuals or entities (and where the consensus approach to proof of work may not be required).
It is the year 2022 and the world’s largest FMCG company, Duopivot, has a permissioned Blockchain where all of its financial and operational transactions are recorded across all of its numerous entities globally.
Duopivot’s auditors, MonTian, gets its partner to run an algorithm, with a series of conditions, assumptions and principles, agreed mutually by Duopivot and Montian, on the permissioned Blockchain. Rather than a sample of tests, a complete test of all transactions is made by the algorithm resulting in queries or anomalies which are raised automatically by the AI built within the algorithm to Duopivot’s finance team
The finance team respond and make the necessary adjustments and the financial statements are declared true and fair and the audit is complete. Within 3 days. With no other interventions from auditors MonTian other than the audit partner running the code on the Blockchain.
In addition to a test of balances and figures provided by Duopivot, the algorithm also tests the stability of the Blockchain and detects any abnormal transactions raised across any of Duopivot’s entities by cross-referencing them to external datasets (such as consumer behaviour, sentiment, overall sales figures for the industry and GDP growth of the multiple markets Duopivot operates in). The AI is able to apply the principles of scepticism to the data provided by Duopivot using its own massive datasets and get a high level of assurance on whether the transactions recorded on the permissioned Blockchain is valid or not.
Following the review and sign-off by MonTian, Duopivot’s largest institutional investor WhitePebble, decide to run their own algorithm on Duopivot’s permissioned Blockchain to test the figures themselves and to also assess future investment potential in order to aid them for points to highlight during the AGM which takes place within 30 days of year-end.
MonTian’s algorithm and AI also are able to assess based on Duopivot’s data whether they have utilised the optimal tax channels to achieve greater tax efficiency, as the AI has access to the datasets of every single tax regulations globally and can therefore chart the route to ideal tax positions.
This is the world we could face where audit teams are reduced to individuals running code and managing code rather than managing audit team members and delivering results near instantaneously. It also allows for institutional investors to test the financial statements on their own without relying solely on auditors and also using the information for their investment decisions and approach.
The AI built within the algorithm could also provide recommendations for business decisions on areas such as game theory and provide a probabilistic approach to decision making that will be more attuned to market needs and conditions.
Changes in legislation or reporting standards can also be applied almost instantaneously (even where judgment is required) and be tested to a very high degree of certainty.
What is the likelihood of this?
We already see how PwC’s GL.ai is an innovation made of algorithms. It analyses billions of data points in milliseconds and applies judgment to detect anomalies in the general ledger.
Northern Trust (Nasdaq: NTRS) also currently do this in a similar vein where they launched their Blockchain technology for private equity and allowing for audit firms to carry out audits through access on their own blockchain node, providing access to relevant fund data. Northern Trust currently allows for audit firms to obtain the master records to their own systems or to complete the audit on their blockchain itself. You can find out more here.
The ability of algorithms supported by machine learning , particularly augmented data learning and deep learning models that account for uncertainty (such as utilising the Bayesian approach to machine learning), the role, shape and future of audit and assurance as we know it will change dramatically.