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Model Risk Management: Regulatory Priorities and Best Practices

Abrigo

Meet Model Risk Management Expectations Updates to the FDIC Risk Management Manual should steer institutions toward a model that manages risk and drives growth. Takeaway 1 Aside from meeting examiner expectations, proper model risk management can protect your institution from unnecessary risk. .

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Best practices for credit risk management in uncertain times

Abrigo

Fortify your credit risk management framework How to prepare your organization for scrutiny of its credit risk management practices during your next exam or review. . You might also like this whitepaper, "Stress Testing: Managing Capital Levels and Credit Risk." Have a playbook.

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How Mastercard Uses AI To Fight Fraud And Make Better Credit Decisions

PYMNTS

He and Nitendra Rajput , Mastercard’s vice president of product development and head of the company’s “AI Garage,” said that in many cases, AI is the only way to scale up sufficiently to meet the challenges the company faces with fraud and other business issues. “It Fighting Fraud in a Post-Pandemic World.

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Cyber Complications for Vendor Risk Management

Abrigo

Cybersecurity | 4 minute read Key Takeaways Third-party/vendor risk management is becoming increasingly challenging with more cloud-based providers. On top of initial vendor due diligence, there are ongoing, systematic approaches to managing third-party relationships. . Fraud Prevention. Credit Risk. Learn More.

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Introducing Our Partnership with Kyriba

Perficient

Specializing in cloud-based business transformation, Perficient’s new Treasury Technology practice will improve client’s global banking footprints through bank connectivity and address concerns for payment fraud. Payment fraud is now a high-priority concern. Bank connectivity is one of the most complicated aspects of an ERP project.

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Cyber Complications for Vendor Risk Management

Abrigo

In a marketplace where data is shared and distributed at record speeds, third-party or vendor risk management is a challenge for most businesses. Regardless of whether banks manage it internally or outsource it, the regulatory burden lies with the institution. The banking industry is no stranger to this.

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Bank Regulators Seeking Comments on the Use of AI and ML in the Industry

Perficient

fraud detection and financial crime monitoring). email, text, audio data), with the aim of identifying fraud or anomalous transactions. Risk Management. AI may be used to augment risk management and control practices. It involves using software to analyze both structured and unstructured data (i.e.,