Five Common Mistakes in Implementing AI in Investment Operations
OnCorps AI
Fast-Track Financial Services Operations AI with Ready-to-Deploy, Pre-Trained Algorithms.
In our work with financial services firms, we've identified five recurring mistakes organizations make when integrating AI into their investment operations. Addressing these early can prevent setbacks and ensure smoother implementation.
1. Equating Generative AI with All AI
Generative AI, like OpenAI's GPT models, has captivated the industry with its ability to create content from prompts. However, equating generative AI with the entirety of AI overlooks the vast array of other AI techniques that have long been in use, such as reinforcement learning and neural networks embedded in platforms like YouTube and Netflix. While generative AI is accessible and user-friendly, it is only a part of the broader AI spectrum that includes powerful algorithms capable of data processing, anomaly detection, and predictive analytics.
2. Mismatching Algorithms to Problems
Choosing the right AI algorithm for a specific problem is crucial. Financial operations data can be complex, and the algorithm must be tailored to the data's nature and the problem's specifics. For instance, a static table-based algorithm may not suit a dynamic, high-frequency trading environment. AI must be integrated with continuous data streams and production-level workflows to be effective, ensuring algorithms are not only theoretically sound but practically applicable.
3. Favoring Incumbents Over Startups
Large firms often default to incumbent solutions, underestimating the innovative capabilities of startups. This bias can result in missed opportunities for deploying cutting-edge AI solutions that are more flexible and advanced. Startups in the AI space often bring fresh perspectives and specialized innovations that can significantly enhance operational efficiency and accuracy.
4. Managing AI Like Traditional Software
AI systems require a different management approach than traditional software. They need continuous monitoring, retraining, and updates to adapt to new data and evolving conditions. Treating AI systems as static software can lead to reduced efficacy over time. Effective AI management includes regular performance assessments, algorithmic adjustments, and integration of user feedback to refine the models continually.
5. Underestimating AI Support Needs
Implementing AI is not a one-time setup; it involves ongoing support and expertise. Financial firms must invest in skilled personnel who can manage AI systems, understand their outputs, and tweak them as necessary. Additionally, robust support structures are essential to troubleshoot issues and maintain optimal performance. Firms often underestimate the resources needed for this continuous support, leading to challenges in realizing the full potential of AI.
About OnCorps
Avoiding these common pitfalls can make the integration of AI into investment operations more successful. At OnCorps, we emphasize the importance of a comprehensive approach to AI implementation, from selecting the right algorithms to ensuring continuous support and adaptation. Our AI solutions are designed to reduce false positives by 90%, and significantly cut down manual labor time.
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