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Banking on Intelligence: The Global Sprint to AI Maturity in Finance

Finextra

On the risk and operations side, common uses include fraud detection, anti-money-laundering pattern detection, credit risk scoring and trading optimization. finance leaders cite fraud and risk management as areas in which they use AI. banks face challenges in AI adoption, such as regulatory compliance and risk management.

AI 69
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Are Commercial Credit Bureau Reports Enough?

Trade Credit & Liquidity Management

In this data-driven economy, risk assessment demands more than simply evaluating whether a customer will pay their bills. To truly understand and manage credit risk today, modern companies must look beyond the basics and leverage new technologies, alternative data, and broader information sources.

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AI Becomes the Banker: 21 Case Studies Transforming Digital Banking CX

Finextra

Traditional areas like fraud prevention (65%), credit underwriting (62%) and regulatory compliance (58%) are still heavily prioritized, reflecting that these were some of the first uses of AI in banking and continue to be critical for reducing losses. Upstart’s AI models evaluate credit risk more holistically than FICO scores.

AI 114
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Unlocking Growth: Supply Chain Financing Future with M2P’s Credit Stack

M2P Fintech

Challenges in Supply Chain Financing Manual processes slow down operations and heighten the risk of errors. Additionally, regulatory compliance and the need to adhere to strict financial standards further complicate the landscape.

Finance 52
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Why AI’s Early Adopters Are Laser-Focused On Credit Risk And Payments

PYMNTS

These circumstances have brought to the fore what has long been a central concern for lenders: assessing and managing credit risk. This vital task is complicated even in normal times due to the multitude of financial risk factors in play at any given time. percent expect these systems to improve credit/portfolio risk.

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New Data: Share Of FIs Using AI Has Increased 70 Pct

PYMNTS

Even more significantly, our research shows that FIs are using AI with greater focus than they have in the past, with two areas emerging as key applications: payments fraud and credit risk. Supervised systems like BRMS are simply not capable of responding to the dynamic, constantly shifting nature of these risks.

AI 81
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Understanding Risk Management Strategies as a PayFac

Stax

PayFacs handle risk assessment, underwriting, settling of funds, compliance, and chargebacks which exposes them to greater potential risks. Major risk factors for PayFacs include fraudulent transactions, merchant credit risk, regulatory compliance, and operational risks.