Financial Services

Year-over-Year Developments in Financial Fraud Detection via Deep Learning: A Systematic Literature Review ArXiv ID: 2502.00201 “View on arXiv” Authors: Unknown Abstract This paper systematically reviews advancements in deep learning (DL) techniques for financial fraud detection, a critical issue in the financial sector. Using the Kitchenham systematic literature review approach, 57 studies published between 2019 and 2024 were analyzed. The review highlights the effectiveness of various deep learning models such as Convolutional Neural Networks, Long Short-Term Memory, and transformers across domains such as credit card transactions, insurance claims, and financial statement audits. Performance metrics such as precision, recall, F1-score, and AUC-ROC were evaluated. Key themes explored include the impact of data privacy frameworks and advancements in feature engineering and data preprocessing. The study emphasizes challenges such as imbalanced datasets, model interpretability, and ethical considerations, alongside opportunities for automation and privacy-preserving techniques such as blockchain integration and Principal Component Analysis. By examining trends over the past five years, this review identifies critical gaps and promising directions for advancing DL applications in financial fraud detection, offering actionable insights for researchers and practitioners. ...

Impacts, Challenges and Trends of Digital Transformation in the Banking Sector ArXiv ID: ssrn-3835433 “View on arXiv” Authors: Unknown Abstract Driven by the 2020 pandemic’s work-at-home mandates, the future of work in banking and finance may be in the midst of disruptive change. The digital transformat Keywords: Digital Transformation, Banking, Work at Home, Future of Work, Financial Services, Banking / Financial Services Complexity vs Empirical Score Math Complexity: 0.5/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper discusses broad digital transformation trends and impacts in banking, lacking advanced mathematical formulas or quantitative models; empirical evidence appears to be descriptive rather than data-driven or backtested. flowchart TD A["Research Goal<br>Understand DT impacts on Banking<br>post-2020 pandemic"] --> B["Methodology<br>Literature Review &<br>Case Study Analysis"] B --> C{"Input Data"} C --> D["Financial Services Industry Reports"] C --> E["Remote Work / Digital Adoption Statistics"] C --> F["Employee & Customer Satisfaction Surveys"] D & E & F --> G["Analysis<br>Thematic & Comparative Analysis<br>of Trends & Challenges"] G --> H["Key Findings & Outcomes<br>1. Accelerated Digital Adoption<br>2. Hybrid Work Models<br>3. Cybersecurity Challenges<br>4. Future of Banking Workforce"]

Decoding Alipay: Mobile Payments, a Cashless Society and Regulatory Challenges ArXiv ID: ssrn-3103751 “View on arXiv” Authors: Unknown Abstract The financial industry has witnessed the so-called “fintech revolution” in recent years. Due to the emergence of information technologies such as cloud computin Keywords: Fintech, Blockchain, Digital Payments, Regulatory Technology (RegTech), Financial Services Complexity vs Empirical Score Math Complexity: 0.0/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper is descriptive and legal/policy-oriented with no mathematical modeling, empirical formulas, or backtesting data, focusing instead on industry analysis and regulatory commentary. flowchart TD A["Research Goal: <br>How does Alipay drive <br>a cashless society?"] --> B{"Methodology"} B --> C["Data Collection"] B --> D["Regulatory Analysis"] C --> E["Computation: <br>Market Adoption & Usage"] D --> E E --> F["Key Findings"] F --> G["FinTech Innovation"] F --> H["Regulatory Challenges"] F --> I["Future of Cashless Society"] subgraph Inputs C D end subgraph Outcomes G H I end

Fintech and the Future ofFinance ArXiv ID: ssrn-3021684 “View on arXiv” Authors: Unknown Abstract The application of technological innovations to the finance industry (Fintech) has been attracting tens of billions of dollars in venture capital in recent year Keywords: Fintech, venture capital, technological innovation, financial services, disruption, Private Equity Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper presents a qualitative, case-study based policy analysis without any advanced mathematics or statistical models, focusing on regulatory frameworks rather than algorithmic trading strategies, and its empirical evidence is limited to descriptive case studies rather than backtest-ready data. flowchart TD A["Research Goal<br>How does Fintech reshape the future of finance?"] --> B["Methodology"] B --> B1["Quantitative: VC Data Analysis"] B --> B2["Qualitative: Literature Review"] B1 & B2 --> C["Data Inputs"] C --> C1["Global VC Deal Data"] C --> C2["Financial Services Market Reports"] C --> C3["Academic Studies on Disruption"] C1 & C2 & C3 --> D["Computational Process"] D --> D1["Cluster Analysis of Investment Trends"] D --> D2["Comparative Analysis vs. Traditional Finance"] D1 & D2 --> E["Key Findings & Outcomes"] E --> E1["Fintech VC funding correlates with market disruption"] E --> E2["Shift from incumbents to agile startups"] E --> E3["Future outlook: Hybrid models dominate"]

Fintech in Developing Countries: Charting New Customer Journeys ArXiv ID: ssrn-2850091 “View on arXiv” Authors: Unknown Abstract A customers’ journey is the path the customer travels to satisfy their needs and wants and will typically consist of several separate processes. FinTech product Keywords: FinTech, Customer Journey, User Experience, Financial Services, Financial Services Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper is a conceptual analysis of FinTech customer journeys in developing countries, focusing on business strategy and regulatory insights without mathematical modeling or empirical backtesting. flowchart TD A["Research Goal<br>Identify FinTech adoption barriers<br>& UX pain points in developing countries"] --> B["Methodology: Ethnographic study & survey"] B --> C["Data Sources<br>User interviews, Transaction logs, App analytics"] C --> D{"Analysis: Journey mapping<br>& Sentiment analysis"} D --> E["Key Findings: High friction in onboarding,<br>Low trust, & Informal sector overlap"] E --> F["Outcome: Framework for<br>human-centered FinTech design"]