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Godrej Capital is utilizing Generative AI (GenAI) to enhance customer service, analyze interactions, and manage creditrisk. Key initiatives include AI-driven quality checks, real-time dataanalysis, and the upcoming launch of the SAKSHAM-AI/ML platform for improved decision-making and service efficiency.
Plati Potom develops post-payment solutions for eCommerce and offline retailers, as well as dataanalysis and creditrisk management tools. One of the main goals of this transaction is to acquire proprietary dataanalysis and scoring technologies, as well as the team working on the products.
This was a year that bent and broke quite a few risk forecasting models, thus all the more reason to bring AI smarts to bear on transaction volumes scaling far beyond a human pace. Circumstances] have underscored the singular importance of artificial intelligence (AI) in managing creditrisk as well as supporting other bank operations.
He added that this expands the scope of the CFOs’ remit, but it also means CFOs need to expand their data sources. "I I think what’s important is to find out what data is out there to augment your dataanalysis," Kesuma added. Moody’s, he noted, is well known for its counterparty creditriskanalysis.
DataAnalysis: They use data analytics tools and techniques to extract insights from financial data, providing valuable information to support strategic decisions. This includes building pro forma financial statements, forecasting cash flows, and scenario analysis.
The Hong Kong Monetary Authority has, as finews.asia reported this past week, amended its creditrisk management guidelines in a way that seeks to boost the embrace of analytics when lending to smaller firms.
For example, it manages borrower’s creditdata and spots early financial signs. This helps lenders proactively tackle creditrisks. Also, AI's predictive analysis forecasts borrower defaults and risk levels using data.
It became apparent very quickly, Lifshitz noted wryly, that it wasn’t a market segment they could address in either a scalable or profitable manner, given the diversity of those micro, zero-employee businesses and the creditrisk associated with them.
It became apparent, very quickly, Lifshitz noted wryly, that it wasn’t a market segment they could address in either a scalable or profitable manner given the diversity of those micro, zero employee businesses and the creditrisk associated with them.
But there are so many tools at your disposal, and let alone how much duration you’re taking, how much interest, how much creditrisk you’re taking, illiquidity, et cetera. And how do you make the decision, I’m not comfortable with this creditrisk relative to the return it’s going to throw off?
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