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AI-driven anomaly detection, risk analysis & financial insights: webinar summary

Unlock the power of AI-driven anomaly and risk detection. Explore ensemble AI, segment, & disaggregated revenue analysis for financial insight


Welcome to our webinar recap, where we delve into MindBridge’s innovative approach to AI-driven financial analysis and anomaly and risk detection. In our recent webinar, “Slice and dice with ensemble AI: segment and disaggregated revenue analytics,” we explored how MindBridge’s AI and machine learning capabilities enable the identification of risky transactions, optimize segment analysis, and provide comprehensive revenue analytics.

In this blog post, we provide a summary of some key takeaways from the webinar, including insights on anomaly detection, risk analysis, and financial insights. Keep reading for more details, or click below to view the recording.

Session host

Michael Bottala
Michael Bottala
Director of Strategic Insights

Michael has been with MindBridge for over four years. During his tenure, he has worked closely with clients to help them understand the value of MindBridge’s innovative platform for their organizations. In addition, Michael’s expertise in financial analysis and risk management has earned him recognition as a Top 100 Accountant by the Los Angeles Business Journal.

How AI transforms financial analysis: a medical industry parallel

The webinar began by drawing parallels between advancements in the medical industry’s heart monitoring and the transformation of financial professionals’ focus through artificial intelligence. 

The evolution of heart monitoring offers valuable insights into how financial professionals can gain focus through AI. Initially, doctors used stethoscopes to listen to patients’ heartbeats, but this method was unreliable and didn’t record data consistently. To address this, the medical industry introduced the EKG, providing reliable and consistent data capture for heart arrhythmias. However, interpreting this data required specialized training, and there was still a challenge in identifying what constituted a good or abnormal heartbeat.

Enter artificial intelligence. AI was used to analyze EKG data, enabling the medical industry to focus on the most abnormal patterns and effectively diagnose and address heart issues. This led to early adopters of the technology catching more heart disease cases.

This progression mirrors the transformation in the financial industry. Initially, the industry relied on qualitative approaches and discussions with management. This gave way to more traditional data analytics approaches with Excel and rules-based tests. Now, tools like MindBridge are revolutionizing the field by pinpointing the riskiest transactions and focusing on what matters most.

In the same way that the EKG captures crucial information about a patient’s heart, the General Ledger can be seen as the heartbeat of an organization. By utilizing AI to identify weak areas within the General Ledger, financial professionals can bring more focus to their work and diagnose potential issues more effectively.

MindBridge’s Ensemble AI: identifying risky transactions with machine learning

MindBridge’s ensemble AI approach combines statistical methods, traditional rules, and machine learning to quickly and efficiently identify risky transactions. 

Machine learning is crucial in reducing noise around datasets, as it is more about pattern recognition and behavioral elements of the underlying ledger than just passing specific tests. It helps to establish what constitutes a normal business process and what is abnormal or atypical. Control points like workflows help track the frequency of transactions, enabling professionals to use their judgment and focus on outliers.

One significant advantage of machine learning over traditional data analytics is its ability to analyze complex relationships. While a rules-based approach may create more noise by drawing a straight line through data, machine learning can distinguish patterns more efficiently, leading to a cleaner separation of the data.

This has been validated through real-life scenarios, where machine learning’s fluid nature allows for better distinguishing relationships, timing, frequencies, and amounts, ultimately creating more focus. In addition, by identifying and addressing issues such as fraud, misstatements, or miscoding, machine learning enhances the efficiency of financial professionals’ work.

Enhancing segment analysis with MindBridge’s AI-powered solutions

The ensemble AI approach creates a greater focus on segment analysis. Segment analysis involves breaking down a company’s financial data into different areas or aspects, supporting a deeper understanding of financial statements, risk drivers, and overall business. By optimizing resources to focus on the areas with the most risk, segment analysis proves to be valuable.

A key consideration for segment analysis is the data included. MindBridge offers standard columns, but users can also incorporate ancillary fields relevant to their dataset. Examples include company code, job number, region, and user ID, which can provide insights into the segregation of duties or high-risk regions to develop an audit plan.

It’s crucial to remember that other fields can be incorporated, such as cost centers, profit centers, vendors, customers, or even programs for non-profit organizations. Therefore, when using MindBridge for segment analysis, it’s essential to consider the organization being examined and the data that would be valuable in analyzing its specific segments.

Demo: MindBridge platform for comprehensive AI-driven financial analysis

During the webinar, Michael demonstrated the MindBridge platform, showcasing how it creates focus and efficiencies in segment analysis. He highlighted the Risk Segmentation tab, which breaks down the underlying risk related to various areas, assigns risk scores, and provides insights into the monetary values associated with those scores.

In the next part of the demo, Michael explored additional ways to use the MindBridge platform, such as:

  1. Profiling users for segregation of duties and training issues: The platform can be used to analyze the risk profiles of different temp workers by seeing which transactions they recorded and identifying any unusual activities.
  2. Segregation of duties: The platform allows users to view the types of transactions recorded by specific users, ensuring they are recording transactions appropriate for their job descriptions.
  3. Focusing on specific processes: Users can select specific types of transactions, such as journal entries, to analyze accounts impacted by manual entries and verify if they meet expectations.
  4. Prior period comparison: The platform allows users to compare regions or transaction types year-over-year to identify changes in activity and risk profiles. New or discontinued activities can be easily spotted, providing a useful indication of changes within a particular segment.
  5. Transaction structure: Unique to MindBridge, the platform offers a transaction structure feature that focuses on the monetary structure and flow of transactions. By identifying transaction structures in a specific region that aren’t consistent with other regions, users can potentially pinpoint process issues within the organization.

Throughout the demo, Michael emphasized the importance of leveraging auditor and accountant knowledge of the organization to supplement and augment the insights gained through the MindBridge platform. Additionally, the platform’s flexibility, out-of-the-box nature, and various features enable users to efficiently analyze their data and focus on areas of concern.

Mastering disaggregated revenue analysis with MindBridge’s AI

Disaggregated revenue analysis helps financial professionals understand risk drivers and their impact on revenue by examining various factors such as geographical regions, types of goods or services, market or customer types, and contract duration. MindBridge, a powerful tool for financial analysis, offers several ways to evaluate and analyze disaggregated revenue data, such as:

  1. Risk by customer/vendor or project: By filtering data using the name function, you can identify customers linked to revenue and their associated risk scores. This helps in determining which customers pose higher risks and require further attention.
  2. Account codes: Breaking down revenue by unique account codes helps track different products or services, making it easier to understand patterns and identify risk profiles.
  3. Trends: By identifying a particular name or project that poses a risk, you can efficiently track their activity and revenue, recording trends over time.
  4. Dashboard elements: MindBridge provides multiple views and visualizations that can be exported as clean Excel files. This includes scatterplots, color visualizations, and trend lines to comprehensively understand risk drivers.
  5. Region-based analysis: By breaking down revenue by regions, you can identify areas with higher risk elements and focus on understanding their unique characteristics.
  6. Financial statement analysis: Performing variance analysis on financial statements can help identify changes in revenue across different segments or regions, allowing for a deeper understanding of underlying patterns.

Expanding financial insights: beyond the general ledger with MindBridge’s AI

While the general ledger has been the primary focus of the webinar due to its flexibility in analyzing various datasets, MindBridge offers a wide range of templates and control points for deeper insights into other financial areas. These include:

  1. Revenue Analysis: MindBridge provides tools for revenue recognition, revenue cycle segmentation, and identifying unusual patterns in revenue and customers. This helps financial professionals gain a more in-depth understanding of revenue generation and related risks.
  2. Payroll Analysis: By examining payroll data, financial professionals can detect anomalies, uncover patterns, and gain insights into the organization’s payroll processes and controls.
  3. Payables and Vendor Analysis: MindBridge extends its analytical capabilities to accounts payable and vendor data, allowing users to identify risk drivers, evaluate vendor relationships, and better understand the organization’s payables processes.

Although the general ledger serves as an excellent starting point for financial analysis, MindBridge’s expanded functionality allows users to delve deeper into specific financial areas for a comprehensive understanding of an organization’s financial performance. By leveraging these additional modules, financial professionals can enhance their risk management efforts and uncover hidden patterns and trends across various financial datasets.

Watch the full recording to learn more about how artificial intelligence and machine learning can transform your financial analysis processes. Then, don’t hesitate to contact our team to discuss how MindBridge can revolutionize your organization’s risk management efforts. Our experts will gladly guide you through the platform and demonstrate its potential to enhance your financial decision-making capabilities.