Accounting firms have been increasingly acknowledging and adapting AI in their daily pursuit. A report reveals:
- 54% of accountants believe that the value of a firm drops if they do not use AI
- 66% agree that AI can serve as a competitive advantage
- 46% agree that AI can help attract and retain more talent in the accounting field
While the adaptation and acceptance of AI and technology have been rapidly growing, even in the accounting industry, many organizations have been struggling to understand the impact and benefits of AI holistically. This includes the risk profile, attack surface, and compliance. While AI has multiple boons, there are also significant data privacy concerns, including bias in the strategic decision-making process, etc.
Misuse of AI solutions can help raise a significant amount of data privacy concerns, and introduce bias in strategic decision-making, which can lead to non-compliance violations and increased third-party risks. This is where data and data analytics play a significant role.
What is data analytics?
Data analytics is all about using data to gain a better understanding of your data reality. By understanding where the data lives and how it can help bridge the gap between tech and general businesses, firms can leverage data to pave the path to success.
Within an organization, firm leaders will meet commercially like-minded decision-makers who have the subject matter expertise. The data engineers or the data scientists are trained to use data to connect the dots between the technical and commercial aspects that allow them to understand each other.
Data analytics can also help define the metrics that matter. To go beyond throwing numbers into charts, data analytics also equips you to find meaningful patterns in the data and inform decisions across the business that can be articulated within the organization.
The role of analytics in risk assessment:
In most businesses, especially in a critical field like accounting, risk assessment plays a crucial role. The process is aimed at identifying areas where firms of all sizes are vulnerable to fraud, errors, or non-compliance. Data analytics supports risk assessment in the following ways:
- Risk identification through data:
The volume of AI tools available in the market today is humungous. These tools play a key role in analyzing historical data and current data to identify patterns that may indicate risk. These tools play an important role in detecting anomalies or unusual patterns in the volume, frequency, or values that may suggest fraud. By leveraging data analytics, and auditors, businesses can spot high-risk areas more accurately and efficiently.
- Risk score and prioritization:
Today, AI algorithms are everywhere and they are crucial in assigning risk scores to transactions, accounts, or processes that are based on predefined criteria. These scores help auditors prioritize their efforts, focusing on areas that have the highest risks. This is a targeted approach, which helps improve the effectiveness of the audit process, ensuring that critical risks are addressed promptly.
- Scenario analysis:
This is a key way to help businesses stay prepared for all kinds of circumstances and crises. Scenario analysis includes stimulating different risk scenarios that businesses might be affected by. AI tools can help generate multiple scenarios, that are specifically curated as per the business’ uniqueness. These tools use historical data and predictive models, allowing auditors to assess the likelihood and the impact of various risks. This helps organizations prepare a robust risk mitigation plan, that can also reduce the impact of fraud.
- Anomaly detection:
One of the key strengths of AI in risk assessment is its capability to detect anomalies. The algorithms can identify deviations from established patterns. They help flag transactions or activities that warrant further investigation. Anomaly detection can be useful in identifying frauds or errors that don’t meet the eye through traditional audit methods.
Detection of fraud and errors using data
In the age of digitalization, fraud is ever-growing. Data not only helps in decision-making and strategizing. It is a key tool in detecting fraud. Here’s how:
- Pattern recognition:
All organizations, especially the big ones have large datasets. AI algorithms help identify patterns that are indicative of fraudulent activities, such as unusual transaction sequences, repeat entries, or discrepancies between different data sources. Pattern recognition will help auditors identify red flags that lay the ground for further investigation.
- Behavioral analysis:
Fraud has a lot to do with the behavior of individuals within the organization to detect potential fraud. AI can help track and analyze employee activities, such as financial systems, transaction approvals, and changes to financial records. A lot of fraud happens internally within the organization. AI can help flag unusual or suspicious activities, helping auditors identify potential fraud.
- Text analysis:
A lot of financial records and communications often contain unstructured data, such as emails, memos, and notes. AI-powered text analysis can help analyze this unstructured data to identify patterns, and keywords that may help indicate fraudulent activity. Text analysis can also help identify discussion or financial irregularities that have attempted to conceal information.
- Benford’s law analysis:
It is a statistical principle that helps predict the frequency distribution of digits in numerical data. AI can apply Benford’s law to financial data to identify anomalies that can indicate fraud. Deviations from the expected distribution can help signal the potential manipulation of financial records.
Auditing in the Age of AI – Conclusion
Auditing represents a shift in paradigm in how risk assessment and detection are conducted. The integration of AI and advanced analytics offers unprecedented opportunities for auditors to enhance their efficiency, accuracy, and effectiveness. By automating routine tasks, leveraging, data-driven insights, and employing advanced risk assessment and detection techniques, AI empowers auditors to seamlessly navigate the complexities.
As organizations continue to use data to their advantage, the need to be careful with handling, using, and processing them becomes increasingly important. AI in audit not only helps improve the quality of audits but also strengthens the overall integrity and transparency.
Want to know how outsourcing can help leverage AI and data? Write to us at connect@finsmartaccounting.com.
Director Growth Strategy & Alliance
Maanoj Shah is a finance and outsourcing expert with strong Business Strategy and Scaling-up experience. Over the last 20 years, he has incubated multiple businesses and helped build global enterprises in verticals as diversified as hospitality, technology, and healthcare.
