Businesses should start small and fail fast with machine learning (ML) projects to get the best ROI. Some of the most common use cases for small- to medium-sized businesses (SMBs) include fraud detection, sales optimization, marketing, and document analysis. But the benefits of implementing ML goes even further. This ebook, based on the latest ZDNet / TechRepublic special feature, helps small and medium-sized businesses build a technology stack that promotes innovation and enables growth. Steve Tycast, director of data and analytics at AIM Consulting, said ML efforts focused on operational analytics can reduce costs, drive efficiencies, and increase speed to market.
Founded in 2010, RAVN has developed an AI platform that can organize, discover and summarize relevant information from large volumes of documents and unstructured data. Businesses are using RAVN's unique technology to analyze legal documents like contracts and leases, identify information which is privileged or subject to compliance and automate document classification for easier search and governance. "We have been using RAVN over the past seven months to analyze global supplier agreements," said Horia Selegean, Head of Revenue & Margin Assurance, BT. "We anticipate cost savings of tens of millions of pounds every year through discovering contract optimizations and synergies." The Serious Fraud Office, a UK-governmental department in charge of prosecuting complex cases of fraud and corruption, recently used RAVN to help a team of investigators sift through 30 million documents. RAVN processed 600,000 documents per day -- a target that would be nearly impossible for a human work staff -- allowing investigators to save many months of work.
There's no doubt that the finance industry is undergoing a transformational change. The recent years have seen a rapid acceleration in the pace of disruptive technologies such as AI and Machine Learning in Finance due to improved software and hardware. The finance sector, specifically, has seen a steep rise in the use cases of machine learning applications to advance better outcomes for both consumers and businesses. Until recently, only the hedge funds were the primary users of AI and ML in Finance, but the last few years have seen the applications of ML spreading to various other areas, including banks, fintech, regulators, and insurance firms, to name a few. Right from speeding up the underwriting process, portfolio composition and optimization, model validation, Robo-advising, market impact analysis, to offering alternative credit reporting methods, the different use cases of AI and Machine Learning In Finance are having a significant impact on this sector.