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Tips to Overcome Challenges in AI Development for Financial Services
Machine learning operations (MLOps) and data operations are now being used to break down silos, much like devOps did in application development. Nevertheless, the sheer volume of siloed data stored by banks complicates uniformity and usability
Fremont, CA: Artificial intelligence and machine learning are making inroads in the financial services industry as companies recognize the benefits of automating key processes and better utilizing existing data. According to Business Insider, 56 percent of banks have used AI for risk management, and 52 percent use these tools to generate revenue from new products and services.
However, difficulties persist. While 74 percent of banking executives believe these technologies will transform the industry, they are concerned about barriers to effective implementation, such as growing skill gaps and increasing complexity. Making the transition to AI and ML requires financial firms to understand key benefits, investigate common challenges, and implement best practices.
Overcoming Hurdles to AI Deployment in Banking:
• Myriad Processes: Given the numerous processes involved in AI — analysis, data ingestion, transformation and validation, model development, validation and monitoring, and logging and training, to name a few — there is significant pressure on IT to implement a forward-thinking data center infrastructure or hybrid cloud strategy to support scalability for data science users and processes.
• Siloed Data: Machine learning operations (MLOps) and data operations are now being used to break down silos, much like DevOps did in application development. Nevertheless, the sheer volume of siloed data stored by banks complicates uniformity and usability.
• Managing Present Infrastructure: Both deployment and MLOps engineers are hampered by inflexible infrastructure, a lack of uniformity, and changing tools that necessitate constant repackaging and integration across bank IT environments.
• Multiple Stakeholders: Data scientists, software engineers, data engineers, and deployment engineers are among the many players involved in AI projects, each with their own preferences for technology tools and how they work. Banks frequently struggle to find a unified approach that works for everyone, given the vast number of frameworks, tools, and AI technologies available.