Automating Risk Assessment and Loan Cleansing in Retail Lending: A Conceptual Fintech Framework

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@article{1708535,
      author = {Benedict Ifechukwude Ashiedu, Ejielo Ogbuefi, Uloma Stella Nwabekee, Jeffrey Chidera Ogeawuchi, Abraham Ayodeji Abayomi},
      title = {Automating Risk Assessment and Loan Cleansing in Retail Lending: A Conceptual Fintech Framework},
      journal = {Iconic Research And Engineering Journals},
      year = {2022},
      volume = {5},
      number = {9},
      pages = {728-744},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1708535.pdf},
      abstract = {This presents a conceptual fintech framework for automating risk assessment and loan cleansing in retail lending, based on insights drawn from analyzing loan books and proposing system upgrades for institutions like Access Bank. As retail lending grows in emerging markets, financial institutions face increasing pressure to efficiently manage credit risk, reduce non-performing loans (NPLs), and comply with regulatory requirements. Traditional manual processes for assessing borrower risk and cleansing loan books are often slow, prone to errors, and lack real-time monitoring capabilities, creating significant challenges in maintaining portfolio health. The proposed framework leverages advanced technologies such as artificial intelligence (AI), machine learning (ML), and robotic process automation (RPA) to enhance risk prediction and automate loan cleansing activities. By incorporating alternative data sources and predictive analytics, the system can assess loan performance in real-time, allowing for proactive risk management and early identification of potential defaulters. Furthermore, automated tools streamline the process of identifying, flagging, and managing NPLs, improving operational efficiency and reducing manual interventions. This also discusses the integration of these automation tools with existing core banking systems, ensuring seamless data flow across risk, collections, and finance teams. The framework emphasizes real-time data analytics, predictive modeling, and system-wide communication to improve the overall quality of loan books, reduce operational costs, and enhance decision-making. Additionally, it highlights the regulatory benefits of automation, providing accurate reporting and audit trails that meet compliance standards.
Ultimately, the review demonstrates that implementing automated risk assessment and loan cleansing systems can drive significant improvements in financial stability, operational efficiency, and scalability for retail lenders. The framework outlined here is designed to be adaptable, making it suitable for financial institutions in various markets and stages of digital transformation.},
      keywords = {Automating risk, Assessment, Loan cleansing, Retail lending, Conceptual fintech, Framework},
      month = {March},
  }