Augmented Analytics for Customer Sentiment and Risk Signals from Transaction Logs
Author(s): Ravi Kiran Alluri
Publication #: 2508007
Date of Publication: 08.08.2023
Country: United States
Pages: 1-8
Published In: Volume 9 Issue 4 August-2023
DOI: https://doi.org/10.5281/zenodo.16883314
Abstract
With the advent of digital banking and financial services, the amount of detailed transaction logs produced by customers has exploded. These logs are a critical component to auditing and compliance, but are also a rich, untapped well of behavioral insights. Conventional analytics techniques are often insufficient for detecting subtle customer emotions and hidden risk cues, as they have a narrow focus and require manual interpretation. Augmented analytics, which involves the use of machine learning, NLP, and advanced visualization, provides a revolutionary way to solve this problem. Here, we propose an end-to-end architecture for deploying augmented analytics to generate customer sentiment and early-stage risk signals through the analysis of financial transaction logs (also referred to as financial event logs) in near real-time.
The approach utilizes enriched metadata retrieved from transaction narratives, behavioral information, and third-party contextual enrichment, such as merchant type and temporal event clustering. These entities are input into sentiment classification models and anomaly detection pipelines created with explainable models. Additionally, the system incorporates domain knowledge through rule-based signal attribution for known financial stressors, including account deficiency, high-velocity money withdrawals, or irregular spending patterns. The novelty of the framework is in combining automated insight generation with interactive dashboards that enable analysts to validate, explore, and take action on the signals.
Keywords: Augmented Analytics, Customer Sentiment Analysis, Transaction Logs, Risk Signals, Natural Language Processing, Financial Behavior Analysis, Machine Learning, Explainable AI, Financial Risk Detection, Digital Banking Intelligence.
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