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AI Brings Power of Analytics to All Auditors

By Paul Maplesden

Excessive workloads have always been an issue for accountants and auditors. When you’re juggling multiple audits and expectations, it’s time-consuming to dig that little bit deeper. Artificial Intelligence (AI) is starting to change that—giving you the auditing analytics you need to identify issues, analyze audit risks and optimize client engagements.

Until now, AI-powered auditing has been out of reach for many accounting firms and corporations. Previously, AI analytics required specialized talent—data scientists and power users—that have been in short supply. 

That’s changing. The new generation of AI auditing and analytics software is putting that power and rich insights into the hands of client-facing accountants and auditors. Now, AI gives you the right tools to streamline your auditing and save you time and effort.

Rather than threatening the role of the auditor, AI is set to make your job much easier. We’ll explore what artificial intelligence can do for your corporation or internal audit function to help you manage financial risks, add client value and optimize how you work.

Take Advantage of Data-Driven, AI Auditing

AI auditing, analytics and risk analysis will drive significant benefits for accountants and auditors:

  • Save time through offloading mandatory, repetitive, low-value tasks to an AI auditing tool.
  • Get a complete picture of a client’s data so you have a full understanding of their information.
  • Use a simple, intuitive interface to collect, sort, filter and analyze data.
  • Compare data and transaction patterns and trends highlighted by artificial intelligence.
  • Review AI outputs and prioritize your auditing expertise on exceptions and risks.
  • Develop a deeper context and understanding of your audits.
  • Simplify risk analysis and prioritize high-risk areas for deeper investigation.
  • Build better client engagements through delivering high-value, specialized insights.

Let’s explore some of these areas further.

Identify and Investigate Risks through Automated AI Testing and Risk Scoring

AI auditing tools will gather information from multiple sources, analyze that data, test it against risk-scoring rules, and highlight potential issues. Here’s how that can work:

  1. Define the data that you want to capture and analyze as part of your auditing plan.
  2. Work with your client to identify high-quality data sources—these could be databases, Excel spreadsheets, financial information systems, accounting software and other data repositories.
  3. Use the AI tool to develop audit risk scoring rules—AI auditing tools will have multiple risk scoring frameworks out-of-the-box, and you can customize your own rules based on your audit plan.  
  4. Run your AI auditing analytics tool, review the results and refine as necessary. You may want to iterate on this process several times to ensure you’ve locked in the rules you need.
  5. Use the tool to categorize risks into relevant areas, including their likelihood of occurring and their potential impact.
  6. Prioritize risks for further automated and manual investigation.
  7. Carry out a thorough risk analysis using your experience and insight to report on major risks.

Your AI auditing tool will do most of the heavy lifting here. Although it can take a little tweaking to set up the tool in the first place, you can easily develop a risk analysis approach that gives you a broad and deep understanding of higher-priority risks.

Compare Transactions to Identify Patterns, Trends, Problems and Insights

AI analytics excels at finding patterns in data. AI auditing tools use sophisticated machine learning algorithms for trend and pattern analysis. Here’s how that can work:

  1. Define, identify and integrate with historic data repositories that capture a client’s past and current transactions and information.
  2. Run a general, high-level AI analysis to understand major trends and changes in data. AI auditing tools will have much of this functionality built in.
  3. Use the tool to highlight unusual ratios, patterns or trends outside the norm.
  4. Drill down into those specific datasets and parameters.
  5. Continue with your AI data analysis to explain patterns and shifts in the data and identify root causes.

This type of AI pattern recognition will rapidly surface the data you need to focus on. You can compare different periods, adjust for expected growth and other scenarios, and troubleshoot the results for risks and problems.

Audit the Client’s Entire Dataset to Ensure Good Inputs, Analysis and Findings

Time-starved accountants and auditors need to prioritize the data they analyze. Often, this means focusing on a small subset of information, in the hope it’s a true and representative sample of the client’s overall financial position. AI analytics changes the game—now you can analyze the entire dataset of the business, giving you a much better understanding.

AI analytics tools will integrate across multiple business systems, data warehouses and other information repositories. You can easily set up data connectors to collect, compare and cleanse data from diverse sources. The speed of AI audit processing lets you analyze large quantities of data quickly and accurately.

Comparing and rationalizing information across multiple sources gives you confidence in the integrity of your inputs. You can run your risk analysis, transaction comparisons and other auditing functions against a set of verified data. This directly drives the quality of your findings, ensuring you’re able to deliver a complete report on your client’s information.

Add Context and Value for Your Auditing Client

AI auditing tools aren’t limited to the data in your business. You can connect to external data sources like news articles, marketplace trends, regulatory requirements and best practice frameworks. This adds some extremely useful context.

AI in auditing also saves you time, especially resources you’d be using on low-value tasks. You can redirect your attention to the factors that really matter to the client. This includes:

  • Comparing audit results to the sector and marketplace as a whole and identifying gaps.
  • Benchmarking your audit results against industry best practices and suggesting better management approaches.
  • Highlighting unusual trends and patterns that may be indicative of underlying problems.
  • Considering all internal and external factors that may influence audit results.
  • Discovering risks early and contributing to mitigating actions. 

Ultimately, this helps to build trust—both in the veracity of your findings and the value you provide to your client.

Auditing analytics and risk analysis tools mean you’ll spend less effort sourcing, formatting and cleansing data and more time on developing insights and understanding the implications. This type of streamlining is good for everyone, and ensures your time is well spent, your clients are happy, and your auditing business builds its reputation and revenue.

Discover how CaseWare’s AI Analytics tools can optimize your client engagements.

Paul Maplesden is a professional writer who creates extensively researched, expert, in-depth guides across business, finance, and technology. He loves the challenge of taking complex subjects and breaking them down so they are easy to understand. He can quote 'The Princess Bride' and believes the secret to good writing is Earl Grey tea.

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