Avoid these pitfalls with low-code tools

Traditional ML solutions require the involvement of data scientists and software developers, increasing investments of time, costs, and other resources for financial institutions. Fortunately, low- or no-code platforms break down these barriers and enable businesses to create ML models via user-friendly interfaces.

Low- and no-code tools—such as Appian, Creatio, ZoHo Creator, Retool, Caspio, and Amazon SageMaker Canvas—are designed to reduce hand-coding so that ML models can be delivered faster and easier. These tools boast graphical user interfaces that allow users to drag and drop features instead of creating complex code. Ananya Bhattacharyya, a strategic global product and business leader at Mastercard, says low-code tools not only help financial enterprises innovate faster, they also enable them to navigate data in siloed systems and incompatible formats. “Combining sophisticated data processing along with Automl in a resilient low- or no-code development environment enables enterprises to build end-to-end digital native solutions faster and with minimum technical debt,” Bhattacharyya says. With low- and no-code tools, less technical staff can significantly impact ML system development and performance. 

Here are some of the ways that low- and no-code tools can resolve the four common pitfalls that are preventing many organizations from best utilizing ML:

1. Data quality. Low- and no-code tools allow users to implement a clear and optimized data collection and storage policy, conduct regular data cleansing and validation, and continuously monitor data quality—all without any coding experience. An example of the importance of maintaining good data quality is American Express. The company uses its AI/ML-powered fraud detection system to continuously sift through massive amounts of high-quality transaction data and flag suspicious activity. The company says that due to the system, it can now generate a fraud decision in milliseconds each time an American Express card is used. That fraud system protects over $1.2 trillion in transaction value each year.
2. ML performance. Low- and no-code tools allow users to make changes easily, streamlining the workflow process. These tools can also provide flexibility in using a variety of different foundation models (FMs), such as Claude 2, Amazon Titan, Jurassic-2, and others for generative AI applications. In addition, their flexibility extends to ML services that process tabular data (such as spreadsheets and databases), text, and computer vision images, which allow users to more quickly assess which ready-to-use models are most appropriate or cost-effective. Users can automate testing and reporting and take action to improve communication and knowledge-sharing without having to go through a long and tedious coding process. For example, a private equity firm recently used a low-code tool to integrate ML into its investment and research operations. The company reports that this integration improved decision-making, streamlined processes, and gained an advantage over its competition. 
3. ML documentation. With low- and no-code tools, it is also easier to assemble documentation. Users can utilize built-in templates to capture key project information, track documentation updates, and generate detailed reports. A leading national insurance carrier in California processed thousands of insurance claims through manual data extraction until it switched to ML. The company reports that low-code tool usage has transformed a painstakingly slow, manual documentation process into an automated one that allows staff to quickly process documents and provide valuable information to customers much faster.
4. Collaboration. Low- and no-code tools help pave the way for improved collaboration and knowledge-sharing by enabling the creation of shared workspaces and data pipelines. They may also offer such features as role-based permissions, change tracking, and document version control. Piraeus Bank, a Greek multinational financial services company, reports that its use of low-code tools has allowed it to break down company silos and create a work environment “where innovation, the exchange of ideas, and creativity are supported” and collaboration is fostered. Deloitte, a leading financial consulting and advisory company, says that its use of low-code tools has unlocked greater collaboration and improved efficiency that have enhanced “the speed of development and deployment productivity by 30–40% across client-facing and internal projects.”