Tips for Overcoming the Challenges of Developing AI for Financial Services

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Machine learning operations (MLOps) and data operations are now being used to break down silos, much like devOps did in app development. However, the sheer volume of siled data stored by banks complicates consistency and usability

Fremont, California: Artificial intelligence and machine learning are making inroads in the financial services industry as companies recognize the benefits of automating key processes and making better use of existing data. According to Business Insider, 56% of banks have used AI for risk management, and 52% are using these tools to generate revenue from new products and services.

However, difficulties persist. While 74% of bank executives believe these technologies will transform the industry, they worry about barriers to effective implementation, such as growing skills gaps and increasing complexity. To make the transition to AI and ML, finance companies need to understand key benefits, study common challenges, and implement best practices.

Overcoming barriers to deploying AI in banking:

A myriad of processes: Given the many 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.

Partitioned data: Machine learning operations (MLOps) and data operations are now being used to break down silos, much like DevOps did in app development. However, the sheer volume of siled data stored by banks complicates consistency and usability.

Current infrastructure management: Deployment engineers and MLOps are hampered by rigid infrastructure, a lack of uniformity, and changing tools that require constant repackaging and integration into banking 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 often struggle to find a unified approach that works for everyone, given the large number of AI frameworks, tools, and technologies available.

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