CRI at Singapore FinTech Festival 2025 — Advancing Interpretable AI for MSME Credit and Smart Credit Reporting

By CRI Team
Published on: Wednesday, November 19, 2025 Last updated: Wednesday, November 19, 2025

The Credit Research Initiative (CRI) at the National University of Singapore’s Asian Institute of Digital Finance (AIDF) marked a strong presence at Singapore FinTech Festival (SFF) 2025, reinforcing our commitment to transparent, high-performance credit risk analytics and practical solutions for the financial ecosystem.

Event highlights

  1. AIDF presence across three booths: This year, AIDF engaged regulators, banks, fintechs, and technology partners across three booths, showcasing applied research and tools in trustworthy AI for finance, model governance, and data-driven credit decisioning.
  2. Training partnership with the State of Odisha: AIDF announced a training partnership focused on capacity building in applied AI for finance and modern credit analytics, tailored to public-sector agencies and financial institutions.

Research spotlight: Preview of MSME credit whitepaper

  1. Two preview sessions of our latest MSME credit risk assessment whitepaper were conducted at AIDF’s booths.
  2. The whitepaper, titled “Seeing Through the Black Box: An Interpretable, High-Performance MSME Credit Risk Assessment Framework,” introduces a framework that delivers both interpretability and strong predictive performance for MSME credit assessment—enabling lenders to scale access to finance responsibly in data-constrained environments where transparent, audit-ready models are essential.
  3. The whitepaper was exclusively previewed at SFF and is scheduled for publication this month.

Showcased solutions aligned with CRI’s mission

CAESARS by AIDF — Smart financial report generation

  1. Built on CRI’s Probability of Default (PD) dataset, CAESARS automates the creation of consistent, decision-ready financial reports.
  2. It streamlines financial analysis workflows by standardizing narratives, surfacing key risk drivers, and supporting governance with documentation that connects data to insight.

PraetorUQ by AIDF — Trustworthy AI for LLMs

  1. The team highlighted PraetorUQ, our latest research on trustworthy AI: an easy-to-use LLM proxy layer designed to detect and flag hallucinations in LLM-generated content.

Access to CRI data for research and teaching

  1. In line with CRI’s ongoing commitment to the academic community, our corporate credit risk datasets continue to be available for non-commercial use, including learning, teaching, and research purposes.
  2. The latest public data release includes information up to June 2024, ensuring researchers, students, and academics have access to a continuous time series for timely studies of corporate credit risk and market dynamics.
  3. The updated dataset is available on the Data Download page. We encourage our community to incorporate this information into ongoing and future projects.

What’s next

  1. Publication of the MSME credit whitepaper: Watch the CRI and AIDF websites for the final release.
  2. Collaboration and pilots: We are expanding engagements with financial institutions, development finance organizations, and public-sector entities to operationalize interpretable MSME credit analytics and credit reporting solutions.
  3. Capacity building: Following the Odisha announcement, AIDF will continue to develop training tracks that translate research into deployable capabilities for practitioners, especially in the field of credit risk.

The CRI team is pleased to continue supporting the global research community and the broader financial ecosystem with transparent, reliable, and forward-looking credit risk analytics. We thank everyone who connected with us at SFF 2025 and look forward to the insights and impact that will follow.


No content (including ratings, credit-related analyses and data, valuations, model, software, or other application or output therefrom) or any part thereof (Content) may be modified, reverse engineered, reproduced, or distributed in any form by any means, or stored in a database or retrieval system, without the prior written permission of National University of Singapore or its affiliates (collectively, NUS). The Content shall not be used for any unlawful or unauthorized purposes. NUS and any third-party providers, as well as their directors, officers, shareholders, employees, or agents (collectively NUS Parties) do not guarantee the accuracy, completeness, timeliness, or availability of the Content. NUS Parties are not responsible for any errors or omissions (negligent or otherwise), regardless of the cause, for the results obtained from the use of the Content, or for the security or maintenance of any data input by the user. The Content is provided on an “as is” basis. NUS PARTIES DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, ANY WARRANTIES OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE OR USE, FREEDOM FROM BUGS, SOFTWARE ERRORS OR DEFECTS, THAT THE CONTENT’S FUNCTIONING WILL BE UNINTERRUPTED, OR THAT THE CONTENT WILL OPERATE WITH ANY SOFTWARE OR HARDWARE CONFIGURATION. In no event shall NUS Parties be liable to any party for any direct, indirect, incidental, exemplary, compensatory, punitive, special or consequential damages, costs, expenses, legal fees, or losses (including, without limitation, lost income or lost profits and opportunity costs or losses caused by negligence) in connection with any use of the Content even if advised of the possibility of such damages.