Predictive Analytics Frameworks Strengthening Credit Risk Assessments and Lending Decisions in Financial Technology Ecosystems

• Author(s): Olaolu Samuel Adesanya; Akindamola Samuel Akinola; Lawrence Damilare Oyeniyi
• Paper ID: 1710638
• Page: 526-551
• Published Date: 30-04-2020
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 3 Issue 10 April-2020

Abstract

The rapid growth of financial technology (FinTech) has transformed traditional lending systems by introducing innovative models that expand financial inclusion and improve service delivery. However, this expansion has heightened the need for robust credit risk assessment frameworks that can balance accessibility with stability. This paper explores predictive analytics frameworks as a strategic tool for strengthening credit risk assessments and lending decisions within FinTech ecosystems. Unlike conventional credit scoring methods that rely heavily on historical financial data, predictive analytics incorporates diverse data sources including transaction histories, social media activity, mobile usage, and alternative digital footprints to create more dynamic and inclusive credit profiles. The study examines how machine learning algorithms, data mining techniques, and artificial intelligence are applied to identify patterns and predict borrower behavior with higher accuracy. Predictive analytics frameworks enable real-time risk modeling, adaptive scoring, and early-warning systems that help lenders anticipate defaults, optimize loan portfolios, and reduce non-performing loans. By integrating big data analytics into credit decision-making, FinTech firms can better manage risks while extending credit to underserved populations traditionally excluded from formal financial systems. In addition, the paper highlights the broader benefits of predictive analytics, including enhanced transparency, improved regulatory compliance, and greater investor confidence in digital lending platforms. Case studies illustrate how FinTech companies across emerging and developed markets have leveraged predictive models to achieve significant improvements in lending efficiency, customer acquisition, and repayment performance. Challenges such as data privacy concerns, algorithmic bias, and regulatory constraints are critically assessed, with recommendations for ensuring ethical and responsible use of predictive tools in credit markets. Ultimately, the study concludes that predictive analytics frameworks represent a paradigm shift in credit risk management, offering FinTech ecosystems the ability to strengthen lending decisions, foster financial inclusion, and build resilient digital financial systems. By bridging traditional financial analysis with advanced data-driven methodologies, these frameworks position FinTech as a catalyst for sustainable growth in the global financial sector.

Keywords

Predictive Analytics, Credit Risk Assessment, Lending Decisions, Fintech Ecosystems, Machine Learning, Big Data, Financial Inclusion, Non-Performing Loans, Algorithmic Modeling, Digital Lending.

Citations

IRE Journals:
Olaolu Samuel Adesanya, Akindamola Samuel Akinola, Lawrence Damilare Oyeniyi "Predictive Analytics Frameworks Strengthening Credit Risk Assessments and Lending Decisions in Financial Technology Ecosystems" Iconic Research And Engineering Journals Volume 3 Issue 10 2020 Page 526-551

IEEE:
Olaolu Samuel Adesanya, Akindamola Samuel Akinola, Lawrence Damilare Oyeniyi "Predictive Analytics Frameworks Strengthening Credit Risk Assessments and Lending Decisions in Financial Technology Ecosystems" Iconic Research And Engineering Journals, 3(10)