Role of Data Science Models in Enhancing Revenue Assurance and Compliance in U.S. Financial Enterprises

Abstract

This study addresses revenue leakage and compliance exposure in U.S. financial enterprises where cloud enabled, high volume transaction lifecycles make manual checks and periodic audits insufficient for timely detection, reconciliation, and audit ready evidence. The purpose was to quantify whether Data Science Model Capability (DSMC) strengthens Revenue Assurance Performance (RAP) and Compliance Performance (CP) in an enterprise case setting. A quantitative cross sectional, case-based design surveyed N = 162 professionals across revenue assurance, compliance, risk, internal audit, finance operations, and data analytics roles within a cloud and enterprise systems environment. Key variables were DSMC (10 items), RAP (8 items), and CP (8 items) measured on a 5-point Likert scale. The analysis plan combined descriptive statistics, reliability testing (Cronbach’s alpha), Pearson correlations, and linear regression models predicting RAP and CP from DSMC. Results showed favorable baseline capability and outcomes: DSMC M = 3.84 (SD = 0.61), RAP M = 3.76 (SD = 0.58), and CP M = 3.89 (SD = 0.55), with strong scale reliability (α = 0.88, 0.85, 0.87 respectively). DSMC was strongly associated with RAP (r = 0.62, p < .001) and CP (r = 0.58, p < .001); regression confirmed predictive effects for RAP (β = 0.59, t = 9.41, p < .001, R² = 0.38) and CP (β = 0.55, t = 8.61, p < .001, R² = 0.33). Risk concentration was highest at pricing and fee computation with manual overrides (mean risk = 3.97/5), and high control automation groups outperformed low automation groups (RAP 4.01 vs 3.42; CP 4.12 vs 3.56). Implications indicate that financially material assurance gains come from workflow embedded analytics, expanded automation coverage, and stronger governance and explainability to improve audit defensibility and near real time reporting. Item trends showed exception identification scored highest (M = 4.02) while audit explainability lagged (M = 3.51). Governance readiness averaged 3.63 (SD = 0.69) and related to CP (r = 0.61, p < .001).