The future of AI in finance is here: Reducing the cost of accuracy - Deepstash
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What drives adoption ?

Adoption of AI in finance is being spurred by digital natives (professionals who grew up in a connected world ), with tech solutions finally delivering on the promise of AI/ML. Finance professionals are no longer willing to endure painstaking manual reviews and the threat of inaccurate data in their forecasts and plans.

Outside of finance, Most sales forecasts are automated, allowing sales leaders to focus on closing deals instead of crunching numbers. Further, AI has automated marketing outreach to a significant degree, enabling marketing teams.


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Finding signals in the noise

Cloud adoption is driving AI adoption and vice versa, and that trend accelerated drastically in 2020, Sectors that were once slow to adopt cloud and AI solutions, like health care and finance, are now either catching up or falling behind.

Predict: Signals was built from the ground up to deliver a compelling and seamless user experience for finance. Finance professionals needed a product that could address the variances between predicted vs. actual numbers at the point of use, and that’s exactly what Predict: Signals delivers.


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Accuracy, but at what cost?

Predict: Signals is built for finance to ensure more accurate forecasts and address finance professionals’ chief concern of data accuracy by detecting anomalies. It eliminates the need for the painstaking manual reviews that consume so much time at each monthly or quarterly close or board meeting. It works all day, every day to reduce risk and allow finance professionals to present plans with confidence and accuracy.


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#engineering, #machinelearning and #crypto


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