Fundbox Selects Superwise.ai for AI Assurance

February 18, 2021
February 18, 2021
February 21, 2021

Fundbox, a leading financial platform for small businesses, has selected superwise's AI Assurance solution to increase the efficiency of their AI-driven operations. With a single solution to measure, analyze, troubleshoot and alert the health of their machine learning models in production, Fundbox is further emphasizing its innovative edge and ability to offer highly personalized experiences to their customers.

“At Fundbox, our mission is to power the small business economy, and through Machine Learning, we unlock growth for businesses by providing access to capital in their time of need,” said Aviad Klein, Director of Data Science at Fundbox. “By selecting superwise.ai, we can ensure accuracy and efficiency of our data science efforts, while lowering risks related to the hyperdynamic data environments in which underwriting decisions are made, and better serve our customers.” 

The use of AI in underwriting creates opportunities to differentiate offerings and reduce risk, primarily by leveraging new sources of data previously unavailable in traditional credit scoring, and by identifying hidden patterns. In such a rich ecosystem, ML efforts require impeccable performance and a high level of granularity to assure differentiation. 

Superwise’s AI Assurance solution integrates seamlessly with any models and platforms to ensure that models behave in production the way they were designed to, avoiding bad decisions, drifts or biases.

“Our customers in the financial sector require real-time insights into the health of their models, without falling into the traps of ‘alert fatigue’,” says Oren Razon, co-founder, superwise.ai. “By empowering data science and operational teams with the ability to have tangible and automatic insights about their data and predictions in production, our AI assurance approach is geared towards delivering more efficiency to scale the use of AI.”

As more organizations use AI for their core business activities, the necessity to achieve full observability for their models in production grows, and becomes a prerequisite for any ML implementation. 



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February 21, 2021

Fundbox, a leading financial platform for small businesses, has selected superwise's AI Assurance solution to increase the efficiency of their AI-driven operations. With a single solution to measure, analyze, troubleshoot and alert the health of their machine learning models in production, Fundbox is further emphasizing its innovative edge and ability to offer highly personalized experiences to their customers.

“At Fundbox, our mission is to power the small business economy, and through Machine Learning, we unlock growth for businesses by providing access to capital in their time of need,” said Aviad Klein, Director of Data Science at Fundbox. “By selecting superwise.ai, we can ensure accuracy and efficiency of our data science efforts, while lowering risks related to the hyperdynamic data environments in which underwriting decisions are made, and better serve our customers.” 

The use of AI in underwriting creates opportunities to differentiate offerings and reduce risk, primarily by leveraging new sources of data previously unavailable in traditional credit scoring, and by identifying hidden patterns. In such a rich ecosystem, ML efforts require impeccable performance and a high level of granularity to assure differentiation. 

Superwise’s AI Assurance solution integrates seamlessly with any models and platforms to ensure that models behave in production the way they were designed to, avoiding bad decisions, drifts or biases.

“Our customers in the financial sector require real-time insights into the health of their models, without falling into the traps of ‘alert fatigue’,” says Oren Razon, co-founder, superwise.ai. “By empowering data science and operational teams with the ability to have tangible and automatic insights about their data and predictions in production, our AI assurance approach is geared towards delivering more efficiency to scale the use of AI.”

As more organizations use AI for their core business activities, the necessity to achieve full observability for their models in production grows, and becomes a prerequisite for any ML implementation.