Avoiding Machine Learning Pitfalls to Unlock Powerful Insights
Introduction:
The financial services industry has enthusiastically embraced artificial intelligence (AI) and its subfield, machine learning (ML). However, many organizations struggle to maximize the benefits of this technology. This article delves into the four most common mistakes businesses make when using ML and presents effective strategies to avoid them, emphasizing the transformative potential of low-code tools in addressing these challenges.
Common Pitfalls in Machine Learning Implementation:
1. Working with Low-Quality Data:
ML systems heavily rely on the quality of the data they analyze. Poor data leads to inaccurate insights and decision-making, potentially harming a business’s operations and reputation.
Example: American Express’s AI/ML-powered fraud detection system leverages high-quality transaction data to identify suspicious activities, protecting over $1.2 trillion in annual transaction value.
2. Improper Management of ML System Performance:
Negligence in monitoring ML systems can result in processing errors and data inaccuracies, leading to long-term problems such as reputational damage, regulatory non-compliance, and financial losses.
3. Lack of Adequate Documentation:
Detailed and organized documentation is crucial for a successful ML system. It ensures regulatory compliance, knowledge sharing, effective training, and simplified maintenance and troubleshooting.
Example: A leading insurance carrier in California automated its manual documentation process using low-code tools, allowing staff to expedite document processing and provide timely information to customers.
4. Neglecting to Foster a Collaborative Work Environment:
Failure to promote knowledge-sharing and collaboration can create departmental silos, leading to inefficiency, lack of innovation, and redundancy in work.
Example: Piraeus Bank, a Greek multinational financial services company, credits low-code tools for breaking down silos and creating an environment that encourages innovation, idea exchange, and collaboration.
Resolving Pitfalls with Low-Code Tools:
Low-code tools can effectively address the common pitfalls in ML implementation by providing user-friendly interfaces, reducing coding requirements, and enabling non-technical staff to contribute to ML system development and performance.
1. Data Quality:
Low-code tools streamline data collection and storage, facilitate regular data cleansing and validation, and allow continuous monitoring of data quality without coding expertise.
2. ML Performance:
Low-code tools simplify workflow processes, provide flexibility in using foundation models and ML services, and enable automated testing and reporting, improving communication and knowledge-sharing.
Example: A private equity firm integrated ML into investment and research operations using a low-code tool, resulting in improved decision-making, streamlined processes, and a competitive advantage.
3. ML Documentation:
Low-code tools offer built-in templates for capturing project information, tracking documentation updates, and generating detailed reports, easing the documentation process.
4. Collaboration:
Low-code tools facilitate collaboration by creating shared workspaces, data pipelines, and features like role-based permissions and change tracking, fostering innovation and knowledge-sharing.
Example: Deloitte, a leading financial consulting firm, reports that low-code tools have enhanced collaboration, improved efficiency, and increased productivity by 30-40%.
Conclusion:
Low-code tools are gaining popularity as powerful ML aids, reducing coding errors, accelerating app development and deployment, and enabling less tech-savvy employees to contribute to ML system success. By overcoming common roadblocks, low-code tools empower financial services organizations to innovate, drive efficiency, and gain a competitive edge in the rapidly evolving digital landscape.
About the Author:
Yuxin Yang, the practice manager of machine learning at TensorIoT, holds a master’s degree in computer engineering from Stanford University and a bachelor’s degree in electrical and electronics engineering from Columbia University. TensorIoT, an AWS Advanced Tier Services Partner, specializes in digital transformation and sustainability through IoT, AI/ML, data analytics, and app modernization.