Operationalizing AI-Ready Data Pipelines: Preparing Financial Data for Real-Time Machine Learning Systems

Pavan Kumar Mantha

doi:10.36948/ijfmr.2023.v05i03.68684

Operationalizing AI-Ready Data Pipelines: Preparing Financial Data for Real-Time Machine Learning Systems

Author(s)	Pavan Kumar Mantha
Country	United States
Abstract	The pace of AI and machine learning (ML) uptake in the financial services sector has fundamentally transformed how organisations identify fraud, credit risk, customize and streamline customer engagement and make decisions related to operations. Algorithms and model architectures have been developed with significant literature and industry focus but data engineering backgrounds to make the models scalable exist relatively understudied. Industrial production systems where real-time or close-to-real-time decisioning is necessary, the result of an ML system depends not so much on its complexity but on the predictability, stability and control of upstream data pipelines. Financial information is also a special concern because it is fast, variegated, sensitive and must comply with regulations. Conventional data pipelines are batch-based tools that were first created with business intelligence and offline analysis in mind, and which are not well suited to low-latency, high-quality, and auditable data demands of current ML systems. Consequently, training-serving skew, loss of data quality, fragility, and governance blind spots are common phenomena taking place in organizations that adversely affect model performance and trustworthiness in production. This paper focuses on how AI-ready data pipelines can be operationalized by financial institutions by modifying their prior concepts of batch-centric design to streaming-centric, metadata-conducted, and governance-aware concepts. We clarify the main features of AI-prepared pipelines and examine architectural designs that facilitate real-time feature calculation, real-time inference, or closed-loop feedback. Some of the important topics are data ingestion strategies, pipelines of feature engineering, automated data quality controls, metadata orchestration, privacy preserving design, and end-to-end observability. The paper discusses data engineering practices creating the essential foundation of dependable, conformable, and scalable real-time ML systems in financial services via domain-specific use cases of fraud detection and credit decisioning.
Keywords	AI-ready data pipelines, real-time machine learning, financial data engineering, streaming architectures, feature engineering, data governance, observability, regulatory compliance
Field	Engineering
Published In	Volume 5, Issue 3, May-June 2023
Published On	2023-06-09
DOI	https://doi.org/10.36948/ijfmr.2023.v05i03.68684

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Operationalizing AI-Ready Data Pipelines: Preparing Financial Data for Real-Time Machine Learning Systems

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