AI in Commercial Lending

Jul 21, 2025

Commercial lending is a space that’s quite complex, and its workflow is equally convoluted: application intake, document ingestion, KYC/AML & fraud screening, risk assessment, credit underwriting, loan structuring, approval documentation, loan disbursement, monitoring of early warning signals, and closure.

The entire loan cycle takes significant time and resources. AI could be a saving grace for this workflow, not only saving time and resources but also creating better risk models, early warning systems, and providing decision-makers with tailored insights to make informed calls.

An AI-charged onboarding reduces friction for the application. During the underwriting process, AI models analyze critical pointers such as financial statements and cash flow. For fraud, too, AI can sift through transaction patterns, applicant data, and behavioral indicators to identify if there are any suspicious activities.

AI has the potential to figuratively supercharge every step in the commercial lending workflow. That’s precisely what we aim to unpack in this edition of Fin AI Briefings. Let’s explore what AI has to offer!

An Agentic LOS for Business Banking [Chris Shayan]

This article proposes that an AI-powered, multi-agent loan origination system build on RAG and LangGraph could slash commercial lending decision times from 30–45 days to under 5 minutes, hits 92% accuracy, scales to 1,000+ applications/hour, and drives an estimated $7.2 M in annual ROI, with applicability across trade finance, real estate, treasury and more.

Can AI Predict Who Deserves a Loan? The Future of Credit Decisions [Neil Sahota]

AI-driven lending can compress underwriting to minutes and reveal risk insights that legacy models miss. However, as these systems scale, they bring challenges of bias, explainability, and informed consent. Deploying interpretability tools, human-in-the-loop reviews, and clear data-sharing agreements is crucial to ensure AI not only accelerates decisions but also upholds fairness and accountability.

Need a Loan? The (AI) Agent Will See You Now [Andrea You]

AI agents are now able to handle end-to-end workflows in lending, risk management, compliance, and back-office operations. Although only ~30% of institutions use basic AI today, the shift to “agentic” systems promises seamless underwriting, real-time KYC, fraud detection, and even autonomous payments. But success also hinges on regulatory, ethical, and risk-management requirements.

Three Strategies for Smarter Corporate Lending [BCG]

BCG’s report outlines three strategies for smarter corporate lending: standardizing and simplifying workflows to lift efficiency by up to 30%, integrating advanced analytics, GenAI, and a unified data architecture to shave up to 20% off costs and speed decision-making, and adopting KPI-driven monitoring. This overhauls front-to-back lending for better service and competitiveness.

Commercial Lending: AI-Driven Risk Management [Ari Harrison]

AI-driven risk management in commercial lending enriches credit assessments in real time, boosts fraud detection accuracy through pattern recognition (cutting false positives), and automates AML/KYC compliance via NLP—while demanding robust governance to address data privacy, algorithmic bias, and transparency concerns—ultimately enabling lenders to make faster, more resilient, and better-informed decisions.

Unlocking the Benefits of GenAI for Next-Generation Lending [Deloitte]

GenAI can revolutionize lending by delivering three high-value benefits: Efficiency (handling larger loan volumes with fewer underwriters), Experience (real-time, personalized self-service platforms), and Capabilities (enhanced risk monitoring). The technology’s use cases yield ROI and cut manual documentation, while reducing loan losses by ~20% and freeing up ~10% RWA.

AI in Lending Market: Enhancing Efficiency and Accuracy in Lending [Pramod Pawar]

AI in the lending market is set to reach $58.1 billion by 2033, as lenders deploy algorithms and big-data analytics to speed underwriting, improve accuracy, and reduce risk. Despite the gains, banks must tackle data privacy/security, algorithmic bias, high implementation costs, complex system integration, and shifting regulations.

The Future of Commercial Mortgages: Powered by Generative AI and Machine Learning [Manish Kochar]

GenAI & ML are revolutionizing commercial mortgages with document review automation, AI-driven credit/risk assessments in origination and underwriting, personalized chatbots and closing packages, and continuous post-closing portfolio monitoring with predictive analytics and dynamic AVMs. All of this is helping create a more efficient, transparent, data-driven mortgage ecosystem.

How can AI Improve the Efficiency of Loan Processing in Banks [Rob Tyrie]

AI & ML can slash loan turnaround by automating data gathering, document parsing, credit-risk scoring and underwriting, while delivering faster, more convenient customer experiences. But banks must guard against opaque “black-box” models, biased or drifting algorithms, and ensure strong explainability, compliance, and governance frameworks to manage those risks.