Why Most SME Lending Transformations Fail, And How to Fix Them
This article is one of several thought leadership pieces we have planned for this year. It explains why many banks struggle with SME lending transformations, highlighting how gaps in operating logic across data, risk appetite, and customer-centric banking, must be addressed before technology or AI can drive real results.
Banks Are Turning to AI to Tame Risk: Join Us on October 30
As banks are turning to AI to handle rising data and compliance demands, Q-Lana and SME Banking Club will host a free session on October 30 exploring how AI can make everyday risk management easier.
Q-Lana to Lead Conversation on AI in SME Lending at CEE25 SME Banking Conference
Q-Lana Inc. will be a key voice at this year’s CEE25 SME Banking Conference, focusing on one of the hottest topics in SME finance today: how artificial intelligence (AI) is changing the way banks lend to small and medium-sized businesses.
The Future of Credit Decisions with AI
AI in Credit Decision-Making helps banks make smarter, faster loan decisions by looking beyond simple credit scores. AI alongside the knowledge of relationship managers lets banks spot real risks, understand clients better, and approve more loans for small businesses that were often overlooked before.
SME Lending: The Role of Local Banks and Investors
Micro, Small, and Medium-sized Enterprises often face one big challenge – Access to finance – yet local banks often lack the capital to support them effectively. This challenge requires bridging of the gap to enable collaboration between banks and investors through an SME Lending Platform, introducing a risk-sharing model that expands lending capacity while safeguarding returns.
Role of Relationship Management in SME Lending
In SME banking, relationships still matter. Behind every loan or restructuring deal is a relationship manager who understands clients beyond the numbers. As banks adopt AI and digital tools, the role of the RM is evolving—not fading—proving that the future of SME lending lies in combining human insight with intelligent technology.eam, but a clear, actionable roadmap that defines where the business is heading and how it will get there.
Credit Risk Series Summary
In this final section of our credit risk series, we bring together the key risk metrics (Expected Loss, Unexpected Loss, and RAROC) and connect them to informed decisions around pricing, capital allocation, and profitability. We explain how to embed these concepts into your daily operations as a credit risk manager, to drive productivity and institutional growth.
RAROC: Risk Adjusted Return on Capital
In this final chapter of our credit risk series, we introduce Risk-Adjusted Return on Capital (RAROC) as a tool you can use to link credit risk metrics to loan profitability. We explain how institutions can use RAROC to guide pricing, optimize capital usage, and align lending decisions with risk-return expectations.
Quantifying Capital Requirements for Individual Loans
This is the fourth chapter of our Credit Risk Series where we explain how credit risk managers can go about quantifying capital requirements for individual loans, using global standards like Basel to match capital to risk.
Unexpected Loss
This is the third chapter of our credit risk series where we build on the previous discussion of expected loss, introducing unexpected loss, which is the more volatile, less predictable side of credit risk. As a credit risk manager, you must ensure the institution plans for average outcomes to prepare for extreme scenarios that require equity capital and robust risk modeling.