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A Data-driven Approach to Audit & Accountancy

A Data-driven Approach to Audit & Accountancy

At this year’s Big Data Belfast, Analytics Engines Head of Sales & Marketing Geoff McGimpsey took part in a panel session titled ‘A Data-driven Approach to Audit & Accountancy’. Geoff was joined by Ross Boyd, Director, Ross Boyd Group and Keith Douglas, Director of Data & Analytics, EY. In this blog post, we share some of the key takeaways from the session and explore how data analytics is helping to unlock new opportunities for organisations in the accountancy sector. 

The use of digital technologies has skyrocketed, particularly over the course of the pandemic. As the pace of change increases, there’s considerable scope for audit and accountancy to exploit emerging technologies to provide higher-value services, improve data quality and drive efficiencies in service delivery.

Organisations we’re speaking to are looking to data analytics, AI and Machine Learning, and want to see the art of the possible. They want to understand: what additional value and strategic advice can we offer our customers; how can AI and ML carry a degree of repetitive tasks (with explainability at the core) to drive efficiencies; how can data analytics tools ensure we can offer more robust, efficient and accelerated services, within audit for example.

A report from First Intuition last year found that 41% of accounting organisations surveyed had begun to adopt data analytics. But many are still at the early stage of their journey, and they want to get the right balance between leveraging new technology and combining this with the trusted and proven traditional methods of service delivery.

Business opportunity

The audit and accountancy practitioners we speak to are eager to transform their data from something that they collect to build reporting around an organisation’s historic performance, into a critical real-time business asset capable of unlocking new business opportunities.

There’s a plethora of powerful accounting software on the market, in many instances practitioners want to combine the data held within those systems to create a holistic single view of the customer. We’re talking to firms about building pipelines and exporting their data into common models capable of ingesting data in various formats and visualising this to unlock the hard-to-find patterns and trends contained within.

Combining data from multiple siloed sources and pooling it together for deeper interrogation can enable practitioners to provide higher value, strategic business advisory services and to offer new products and services.

Augmenting the human in the loop

Not only is data analytics and new developments in AI and machine learning creating new business opportunities but it is also delivering more effective, efficient and robust approaches to servicing customer accounts.

Take the audit process. A data-enabled approach to audit brings together the capturing, extracting, cleansing, formatting, interpreting, analysing and visualising data from a range of complex datasets.

Toolsets are available that can create efficiencies that can be re-invested to other audit tasks; identify insights that are not yet known; provide whole population sampling and improved evidence gathering, and; safeguard quality as risk factors can be customised through user-defined rules.

Established techniques around time series anomaly detection can inform auditors of anomalous spending events or unexpected behaviours within risk assessment processes. And rules-based analytics techniques applied to large volumes of data can identify patterns, uncover unusual relationships and categorise events, whilst complying with audit standards.

Data Quality

But data quality presents a significant obstacle for organisations eager to drive value from their data. In their 2021 CPO Survey, Deloitte found that quality was the biggest challenge that organisations faced. A 2021 report from EY also found that 41% of respondents deemed quality to be the most important characteristic regarding their organisation’s success.

Techniques such as standardised ETL (Extract, Transform, Load), Data Matching and Data Augmentation are capable of helping organisations to overcome their most pressing data quality issues. This ensures that the decisions driving firms forward are based on the most accurate intelligence available.

First steps

Moving down the path towards data analytics does not necessarily imply a major level of investment and does not need to fall within the scope of a large and expansive data project. In many instances, a short-run and narrowly focused Proof of Concept can help firms to see the story contained within their data, build trust and confidence in the technologies and to understand where gains can be leveraged.

Utilising data analytics, AI and Machine Learning, professionals can adopt a data-centric approach that allows them to focus more on the outputs and, ultimately, to tell a deeper, richer story around the data and create new, more valuable conversations with customers.

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by PJ Kirk

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