Moderated by Niharika Marketing Manager at Hyperbots.
Niharika: Good morning, good afternoon, good evening, everyone, depending on wherever you are. I am Niharika, and I take care of marketing at Hyperbots. Today, we have with us Mr. Ayo Fashina, the CFO of Kobo 360, Africa’s leading integrated logistics solution provider. Ayo brings a wealth of experience and insights, having operated in the domain for a little more than 20 years now. It’s a pleasure to have you on board, Ayo.
Ayo Fashina: Thank you. Happy to be here.
Niharika: Today, we will be discussing a very interesting topic: how AI can not only improve but also revolutionize cash outflow management. To set the stage, can you help us understand what cash flow is and why businesses must manage it effectively?
Ayo Fashina: Thank you, Niharika. Let me start by defining cash outflow. Cash outflow refers to the movement of money out of a business for various needs like expenses, investments, debt repayment, or salary payments. Managing it effectively ensures that a business can meet its obligations while pursuing growth opportunities. Managing both cash outflows and inflows is essential. The timing of money inflows and outflows determines a business’s solvency. If a business does not manage its cash flow properly, it may become insolvent, and unable to meet its obligations, leading to potential closure. Effective cash flow management is crucial for businesses, including banks, which also face challenges in managing cash flows.
Niharika: How does AI fit into this picture, especially for those new to this concept?
Ayo Fashina: AI enhances decision-making and operational efficiency. In finance, AI can process vast amounts of data to forecast and manage cash flows. It can identify savings opportunities and automate transactions, making cash flow management more efficient. AI can also connect with APIs to consolidate all your bank information, eliminating the need for manual data entry and enabling seamless financial analysis from a single interface tools generate detailed reports and perform in-depth financial analyses, providing the insights needed to track financial performance and identify improvement opportunities.
Niharika: How can AI improve the accuracy of forecasting and budgeting compared to traditional methods?
Ayo Fashina: AI algorithms can analyze historical data, and market conditions, and predict future cash flow needs more accurately than humans. AI can synthesize a vast amount of data quickly, creating more realistic budgets, financial plans, and forecasts. It provides real-time comprehensive forecasting, offering complete visibility into cash flows and enabling better-informed financial decisions.
AI also aids in scenario analysis and planning by simulating various what-if scenarios, helping businesses understand potential future changes and their impacts. This capability allows for more accurate financial forecasting and decision-making.
Niharika: Can you explain how AI enhances operational efficiency and expense management?
Ayo Fashina: AI tools streamline expense management by identifying patterns and anomalies in spending, helping businesses cut unnecessary costs and negotiate better terms with suppliers. AI optimizes cash flow by monitoring payment terms, taking advantage of discounts, and delaying payments when appropriate can also increase visibility into procurement data, ensuring that purchase orders and invoices are properly matched. This enhances cash flow forecasting accuracy and enables efficient payment scheduling. Overall, AI significantly reduces the time required for financial tasks, improving operational efficiency.
Niharika: How does AI contribute to risk management and fraud detection?
Ayo Fashina: AI is adept at identifying irregularities and spotting slight changes that humans might overlook. In fraud detection, AI can monitor transactions and flag unusual activity, such as sudden large transactions on a credit card, potentially preventing fraud. By checking trends and identifying irregular transactions, AI enhances risk management and protects company finances.
Niharika: With AI playing such a big role, how do companies ensure compliance and ethical use?
Ayo Fashina: AI providers must adhere to international standards and regulatory requirements, ensuring ethical data handling and management. Compliance involves following regulations around personally identifiable information and confidential data. AI tools should have access rights and data classification to maintain trust and reliability. Ensuring compliance with these standards is crucial for the ethical use of AI in financial management.
Niharika: Absolutely. Thank you for answering that, Ayo. I think we’ve covered fraud detection and risk management well. But are there other examples where AI has successfully optimized cash flows?
Ayo: Certainly. At our organization, we are an e-logistics platform matching transporters with goods owners. We manage payments between transporters and goods owners. Initially, managing these cash flows was manual and prone to errors. To optimize this, we adopted an AI solution. By connecting our systems to banks via APIs, we automated payments, eliminating duplication and ensuring timely payments. AI also optimized our cash outflow reporting, providing automated and accurate financial reports. On the accounts receivable side, AI generates and tracks invoices, sending automated reminders to customers about due payments. This has significantly reduced our cash-to-cash cycle the time between money going out and coming back in. For example, we reduced our cash-to-cash cycle from 45 days to about 10 days. Some customers even make partial advance payments, further improving our cash flow. These improvements allow us to conduct more business with the same amount of cash, demonstrating AI’s impact on financial efficiency.
Niharika: Thank you for that insight, Ayo. It’s wonderful to hear how AI has been implemented successfully. However, I’m sure integrating AI comes with challenges. Could you share your experience with that?
Ayo: The primary challenge with adopting any technology, including AI, is people. There’s natural resistance to change. Convincing staff and even senior management can be tough. The second challenge is ensuring the quality of data and outputs from the AI. It’s crucial to monitor and clean the data used by AI systems to ensure accuracy. Being a startup, our resistance to change wasn’t as pronounced as it might be in larger, more established organizations. In such companies, where processes have been done a certain way for a long time, resistance can be stronger. Building a culture that embraces change is essential for successful AI integration.
