Journal of Advances in Developmental Research

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Predictive Delivery Intelligence for Payments and Core Banking: Reducing Change Risk at Scale

Author(s) Amol Diwakar Agade, Samta Balpande
Country United States
Abstract Payments and core banking platforms keeps facing strict demands for high availability, audibility and latency. All these banking products need to continuously keep evolving to meet products regulatory and security requirements. In any environment, biggest operational risk is change to adopt. Change Releases often deals with complex dependencies, uneven incident, uneven test coverages and impacts. This paper introduces the idea of Predictive Delivery Intelligence (PDI), a flexible and risk aware release framework. It gathers signals from code changes, CI/CD pipelines health, and SRE telemetry to form a change risk score and evidence bundle for traceability. These evidences of information can be used for automated checks, progressive delivery and traceability that supports audits. Because data from production environment datasets in financial services are frequently confidential and sensitive, we will evaluate Predictive Delivery Intelligence via an offline replay study on a synthetic data delivery telemetry generator. These value parameters are strengthened in prior empirical findings and DevOps/SRE benchmark foundation. Our study involved 100 replay runs which were run over a 26-week simulation involving 240 different services applications which had 6,864 change controls. Predictive Delivery Intelligence cut down CI compute usage by 29.3% and decreased the pipeline rerun rate by 17.7%. This result happened through early failure prediction, while also lowering the average regression time per change control by 29.3%. Using stable modeling for outcomes, Predictive Delivery Intelligence shifts 24% of the baseline change failure rate toward elite performance targets. It also improves the average time to restore by 33.0% through earlier detection and standardized evidence artifacts. Lastly, Predictive Delivery Intelligence lowers the modeled governance review time per release by 36.0% by generating machine-produced release evidences. In this paper, We also included computation steps, confidence intervals, an ablation study, and a minimal artifact package to help with reproduction and use across different regulated organizations like utility, nuclear and healthcare.
Keywords DevOps, Site Reliability Engineering, continuous integration, continuous delivery, change risk, payments platforms, core banking, failure triage, test flakiness, progressive delivery, operational resilience.
Field Engineering
Published In Volume 15, Issue 1, January-June 2024
Published On 2024-05-08
Cite This Predictive Delivery Intelligence for Payments and Core Banking: Reducing Change Risk at Scale - Amol Diwakar Agade, Samta Balpande - IJAIDR Volume 15, Issue 1, January-June 2024. DOI 10.71097/IJAIDR.v15.i1.1780
DOI https://doi.org/10.71097/IJAIDR.v15.i1.1780
Short DOI https://doi.org/hbtv3s
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