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Profiles of Corrupt Payments

I recently had the opportunity to visit with Vince Walden, founder and CEO of KonaAI, for a podcast series on the uses of data driven compliance. KonaAI is the sponsor of those podcasts. This blog post series will flesh out the podcast show notes. Over the next five blog posts, we will discuss generative AI and ChatGPT in compliance, the profiles of corrupt payments, making the business case for data-driven compliance, what to ask for and how to ask for it, and some success stories. In Part 2, we will consider the profiles of a corrupt payment.

The episode highlighted research by MIT and KonaAI that examined $75 billion in payments from various companies to identify characteristics associated with high-risk payments. For businesses looking to identify and stop improper payments, the MIT and KonaAI research offered useful insights. Key attributes that were found to be associated with high-risk payments included payments made without purchase orders, payments flagged by anti-corruption keywords, and payments that significantly deviated from the norm. These attributes were often relevant in the data that humans tagged as high-risk.

One of the key takeaways from the research is the importance of investigating red flags in sales increases. A case study was presented in the episode, highlighting a suspicious sales increase in a Polish province. Contributions to a charitable organization came with increased sales, which raised questions about potential corruption or bribery. This case study emphasizes that compliance officers and risk professionals must monitor commissions, sales incentives, and margins to identify potential bribery and corruption issues.

Companies are encouraged to leverage data analysis tools like KonaAI to identify high-risk payments and prevent corporate corruption. These tools can help track and identify improper payments, providing transparency and easy access to financial accounting data for compliance professionals. By combining financial accounting data with data analysis capabilities, companies can gain insights into payment patterns and detect anomalies that may indicate potential corruption.

However, it is important to note that tradeoffs are involved in balancing different factors when identifying high-risk payments. Compliance officers and risk professionals must carefully consider the impact of their decisions on the business. The podcast episode highlighted the analogy of brakes on a car, emphasizing that the purpose of brakes is not to slow down but to enable the car to go fast and stop when necessary. Similarly, compliance efforts aim to facilitate business growth while ensuring ethical practices and preventing corruption.

The episode also discussed the challenges of identifying high-risk payments and preventing corporate corruption. One challenge is the need for collaboration among companies in an anonymous way to analyze the profiles of improper payments. The research conducted by MIT and KonaAI demonstrated the potential of such collaboration in identifying common risk triggers and profiles of high-risk payments. However, ensuring data privacy and confidentiality is crucial in such collaborative efforts.

In conclusion, identifying high-risk payments and preventing corporate corruption require a comprehensive approach that combines data analysis, collaboration, and a focus on business growth. The MIT and KonaAI research offers useful insights into the characteristics of high-risk payments. Compliance officers and risk professionals are urged to leverage data analysis tools and closely monitor payment patterns to detect and prevent improper payments. By balancing compliance efforts and business objectives, companies can mitigate corruption risks and foster a culture of transparency and integrity.

Examining data is like peering into a crystal ball that projects the inner workings of a business, but only if you know what to look for. One essential facet is sales performance. Even the tiniest irregularities can be a hint of greater issues at hand, such as improper payments. So, understanding and tracking sales data, be it a sudden sales surge in a particular area or an individual outperforming all expectations, is quite crucial.  Walden emphasized the importance of transparency in analyzing sales data. If figures shoot up in a specific region or uncannily exceptional sales are tied to a particular individual, Vince suggests investigating to find out more. The key here, he describes, is the ability to spot these oddities before they morph into a serious problem. Transparency in financial analysis, Vince implores us, can be a game-changer in tracking down and rectifying improper payments.

Third-party relationships can be as much a source of risk as any other part of a business. Keeping tabs on the financial activities of entities such as distributors, commission sales agents, and joint venture partners is therefore imperative. Monitoring these relationships to minimize the risks of improper payments. Walden suggests that the same strategies used to interpret company data for potential risks can also be utilized for third-party relationships. Compliance officers can pair financial analysis with tools like KonaAI to actively monitor anomalies or suspicious transactions. In this scenario, compliance officers can be armed with the right tools and data to monitor and, if required, mitigate any suspicious financial activities related to third-party relationships.

Extending data analysis to third parties is no longer nice; in today’s compliance and fraud-risk environment, it is a business necessity. Monitoring these outside relationships closely provides another layer of security and reduces the breeding ground for unethical activities like improper payments. By integrating financial data with tools like these, compliance officers can actively keep an eye out for anything unusual. This way, companies are not only ensuring that their internal affairs are in order but are also making sure that their external associations are clean and ethical. It’s an insight into how companies can use strategic data analysis to maintain regulatory compliance.

The bottom line is that compliance officers are the guardrails that keep a company on track. Their role is two-pronged – facilitate business growth and, at the same time, deter the business from veering off into unethical practices. Compliance officers ensure the company is always one step ahead in identifying and addressing compliance risks. A balance between growth enablement and ethical conduct is needed to steer the course towards success.

Finally, as compliance officers, you have the power to make a significant impact by preventing improper payments and preserving your organization’s reputation. By embracing the learnings from this podcast episode, you can confidently navigate the challenges of today’s complex business environment and ensure that your efforts contribute to a culture of transparency and ethical behavior. Together, we can create a stronger, more accountable business world.

Resources:

Connect with Vince Walden on LinkedIn

Check out KonaAI

Connect with Tom Fox on LinkedIn

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Data Driven Compliance

The Uses of Data Driven Compliance: Part 2 – Profiles of a Corrupt Payment

Welcome to Data Driven Compliance. In this podcast, we discuss how to use data to improve and enhance the effectiveness of your compliance program, creating greater business efficiency and leading to a higher return on investment for your compliance regime. Join host Tom Fox as he explores how data will drive your compliance program to the next level. This podcast is sponsored by Kona AI.

I recently had the opportunity to visit with Vince Walden, founder and CEO of KonaAI, for a podcast series on the uses of data driven compliance. Over these five podcasts, we will discuss generative AI and ChatGPT in compliance, the profiles of corrupt payments, making the business case for data-driven compliance, what to ask for and how to ask for it, and some success stories. In Part 2, we explore the profiles of corrupt payments.

Vince Walden is an expert in identifying high-risk payments and preventing corporate corruption. His belief in the ability of data analysis and collaboration to find patterns and warning signs shapes his viewpoint on these issues. He shares his experience from a research project where companies collaborated anonymously to analyze the profiles of improper payments, using risk-scoring transactions and applying anti-corruption tests to identify high-risk attributes. Vince emphasizes the importance of transparency and access to data to proactively investigate suspicious activities, serving as a guardrail to prevent potential corruption. Join Tom Fox and Vince Walden as they delve deeper into this topic on this Data Driven Compliance podcast episode.

Key Highlights:

  • Attributes of High-Risk Payments Analysis
  • Uncovering Suspicious Sales Spikes in Poland
  • Detecting Improper Payments with Data Analysis

Resources:

Connect with Vince Walden on LinkedIn

Check out Kona AI

Connect with Tom Fox on LinkedIn