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Determinants of PIX Fraud Detection and Older-User Digital Safety, A Panel Analysis

Rapid integration of instant retail payment platforms reshapes transaction volume while introducing complex vulnerabilities for senior users. Evaluating detection capabilities requires synthesizing panel econometric modeling with real-time machine learning oversight and demographic risk indicators. Effective digital financial safety depends on aligning predictive algorithmic controls with adaptive authentication standards tailored to vulnerable user groups.

Objetivo do trabalho

Identify the econometric and algorithmic determinants of PIX fraud detection efficacy and older-user digital safety across regional payment jurisdictions.

Metodologia

Secondary panel synthesis and econometric modeling framework evaluating institutional banking data, machine learning detection paradigms, and demographic indicators.

Originalidade científica

Synthesizes regional panel econometric dynamics of instant payments with intelligent fraud detection architectures tailored to senior demographic vulnerabilities.

Prévia do Documento

Esta é uma breve prévia. A versão completa inclui texto expandido para todas as seções, uma conclusão e uma bibliografia formatada.

Master's Thesis

Degree:
Determinants of PIX Fraud Detection and Older-User Digital Safety, A Panel Analysis

Author:

Group

First M. Last

Advisor:

Dr. First Last

City, 2026

Contents

Introduction
1.1. Context and Problem Statement
1.2. Research Objectives and Scope
2. Conceptual and Institutional Framework of Instant Digital Payments
2.1. Architecture of the PIX Payment Ecosystem and Structural Shifts
2.2. Older-User Vulnerabilities and Digital Financial Inclusion
2.3. Typologies of Instant Payment Fraud and Social Engineering
3. Methodological Design and Panel Econometric Modeling
3.1. Panel Data Construction and Municipal Aggregation
3.2. Econometric Specification and Identification Strategy
3.3. Detection Metrics and Algorithmic Performance Indicators
4. Empirical Determinants of Fraud Detection and Demographic Safety
4.1. Real-Time Detection Mechanisms and Adaptive Risk Scoring
4.2. Demographic Disparities in Incident Reporting and Vulnerability
5. Critical Synthesis, Regulatory Governance, and Policy Design
5.1. Systemic Implications for Central Bank Regulations and Banking Security
5.2. Design Interventions for Age-Inclusive Digital Safeguards
Conclusion
Bibliography

Introduction

The accelerated adoption of instant payment infrastructures has profoundly restructured retail banking networks and financial inclusion patterns across emerging markets [1]. The expansion of instant settlement systems like PIX has reduced dependence on physical bank branches while shifting transactional velocity to real-time digital environments [1]. This structural migration exposes older cohorts to heightened cyber-enabled security threats and social engineering schemes, as institutional authentication protocols and interface safeguards frequently lag behind the sophisticated behavioral patterns exploited by malicious actors [3].

Traditional financial security models predominantly rely on static rules that struggle to differentiate between genuine elder-user friction and anomalous transactional behavior in high-velocity frameworks [4]. Machine learning algorithms, graph analytics, and adaptive risk scoring mechanisms represent a necessary architectural shift toward real-time anomaly detection [2], [5]. However, the systemic effectiveness of intelligent fraud detection systems remains conditioned on municipal-level demographic attributes, technological maturity, and institutional reporting structures [4], [6].

This paper conducts a panel investigation into the structural and technological determinants governing fraud detection efficacy and older-user digital protection within the PIX ecosystem. Utilizing secondary panel datasets and comparative evaluation frameworks, the analysis synthesizes econometric identification approaches with algorithmic oversight paradigms [1], [6]. The objective is to identify the critical structural parameters that optimize security without compromising digital financial accessibility for vulnerable populations [2].

By contextualizing institutional oversight within real-time computational environments, the investigation establishes the operational boundaries of predictive detection systems [5], [7]. The resulting synthesis informs both prudential regulation and interface architecture, delineating evidence-based pathways for inclusive financial protection [3], [4].

5.1. Systemic Implications for Central Bank Regulations and Banking Security

The structural migration from branch-based retail banking to instant payment ecosystems reshapes systemic risk profiles across demographic groups [1]. While intelligent fraud detection architectures leveraging sequential deep neural networks and graph analytics significantly enhance real-time anomaly identification [4], [5], their deployment often introduces critical governance trade-offs for older users. Traditional rule-based monitoring fails to accommodate the nuanced behavioral patterns of senior demographics, yet hyper-adaptive machine learning models risk generating elevated false-positive classifications that disrupt legitimate transactions [4]. Furthermore, municipal-level variations in physical bank branch density amplify these vulnerabilities, as displaced physical support infrastructures leave senior cohorts increasingly reliant on digital interfaces that are susceptible to social engineering [1]. A comprehensive synthesis of existing literature reveals a persistent divide between algorithmic latency optimization and the social determinants of digital vulnerability [5]. Addressing this gap requires regulatory frameworks that mandate both relational transaction monitoring and age-inclusive verification safeguards, ensuring that technological precision does not compromise access [4].

References

  1. Instant Payments and Banks: the Impact of Pix on Bank Branches in Brazil
    Alan Marques Miranda Leal, Mariana Aparecida de Oliveira Haase
    Link DOI
  2. Intelligent Systems for Online Payments, Fraud Detection, and Financial Forecasting
    Ch Ganga Bhavani, K V Uma Kameswari
    Link DOI
  3. ENHANCING AUTHENTICATION AND FRAUD DETECTION IN FINANCIAL TECHNOLOGY AND WIRELESS PAYMENTS
    Chidinma Igwesi
    Link DOI
  4. Intelligent Fraud Detection Systems for Banking, E-Commerce, and Cloud Payments
    Himanshu Sahu, Suresh Kumar
  5. Next-Generation Financial Fraud Detection Using AI, DL, and Graph Analytics
    N Sudha, A Lakshmisri
  6. Leveraging Machine Learning for Real-Time Fraud Detection in Digital Payments
    Pradeep Jeyachandran
  7. Artificial Intelligence in Smart Finance: Fraud Detection, Cloud Payments, and Market Forecasting
    Shakshi Sharma, Utkarsh Mishra
  8. Enhanced Fraud Detection in Digital Payments using Generative AI
    Vishnu Priya T S, Kotteeswari C

Bibliografia

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ABNT NBR 14724:2011 (Trabalhos acadêmicos)

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Pesquisa Científica

ABNT NBR 14724:2011 (Trabalhos acadêmicos)

Determinants of PIX Fraud Detection and Older-User Digital Safety, A Panel Analysis | Pesquisa Científica | Aicademy