How AI Helps Assess Credit Risk, Navigate Complex Processes

Credit risk is a persistent challenge for financial institutions, particularly in business lending, where complex data analysis is crucial. Credit losses at Asia-Pacific banks are projected to reach $528 billion in 2024, according to an S&P Global Ratings report. Ivan Perić, head of global artificial intelligence R&D at Synechron, said organizations should adopt smarter credit assessment models that can parse huge volumes of data in real time.

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“Today, we are witnessing an interesting interplay of challenges and risks intertwined with the digital revolution that started a few years ago,” Perić said. “The biggest challenge is the complexity of processes in financial services that require a lot of information.”

To make forecasts, financial analysts need to process vast amounts of information from diverse sources, including investment trends, market indicators, news about mergers and acquisitions, regulatory compliances and geopolitical news. Security leaders need to not only “balance between allocating resources” to ensure regulatory compliance but also mitigate operational risks and perform their core business activities, Perić said.

“All of this is time-consuming, and that’s why we put AI into these processes,” he said.

In this video interview with Information Security Media Group, Perić also discussed:

• How generative AI is used to mitigate supply chain disruptions;
• How AI/ML technologies assist humans in credit risk management and lending decisions;
• R&D investments in Synechron’s FinLabs and Nexus.

Perić leads innovative projects in machine learning, computer vision and natural language processing. In Synechron’s AI practice, he is responsible for conducting advanced AI research to orchestrate, streamline and optimize internal processes.