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Roundup Of Machine Learning Forecasts And Market Estimates, 2020

#artificialintelligence

IDC predicts spending on AI systems will reach $97.9B in 2023, more than two and one-half times the ... [ ] $37.5B that will be spent in 2019. Machine learning's growing adoption in business across industries reflects how effective its algorithms, frameworks and techniques are at solving complex problems quickly. Open jobs requiring TensorFlow experience is a useful way to quantify how prevalent machine learning is becoming in business today. There are 4,134 open positions in the U.S. on LinkedIn that require TensorFlow expertise and 12,172 open positions worldwide as of today. Open jobs on LinkedIn requesting machine learning expertise in the U.S. further reflect its growing dominance in all businesses.


Roundup Of Machine Learning Forecasts And Market Estimates, 2020

#artificialintelligence

IDC predicts spending on AI systems will reach $97.9B in 2023, more than two and one-half times the ... [ ] $37.5B that will be spent in 2019. Machine learning's growing adoption in business across industries reflects how effective its algorithms, frameworks and techniques are at solving complex problems quickly. Open jobs requiring TensorFlow experience is a useful way to quantify how prevalent machine learning is becoming in business today. There are 4,134 open positions in the U.S. on LinkedIn that require TensorFlow expertise and 12,172 open positions worldwide as of today. Open jobs on LinkedIn requesting machine learning expertise in the U.S. further reflect its growing dominance in all businesses.


TCI Deploys Industry's First AI-Powered Natural Language Rules Engine

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Teledata Communications, Inc. (TCI), the provider of DecisionLender 4 (DL4), a complete consumer loan origination platform, announced it has developed the industry's first AI-powered, natural language rules engine that leverages machine learning to quickly and easily create and maintain risk-based rules and lending policies. Lenders can now effortlessly create and maintain credit and lending policies; the intuitive process does not require any specialized software development skills or the addition of IT resources or third-party assistance. TCI's DecisionLender 4 implementation of Natural Language Understanding (NLU) utilizes machine learning that enables users to create rules using plain English and then convert the rule into code automatically. Any business user can now add new rules, edit existing rules and maintain risk policies. Read More: How Artificial Intelligence and Blockchain is Revolutionizing Mobile Industry in 2020?


Deep Dive: How Anuj Kacker Of MoneyTap Uses AI/ML For Financial Inclusion

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Lending by Fintech, startups have added new dimensions to the financial intermediation process and has boosted financial inclusion, especially by helping borrowers. The Fintech lending industry is constantly innovating and is set to grow to $100 billion by 2023. Also, a growing number of non-financial startups such as Ola and Mi are trying to become lending players as well. To know more about this industry and for this week's Deep Dive, Analytics India Magazine got in touch with Anuj Kacker, COO and Co-Founder of MoneyTap. Bangalore-based MoneyTap was launched in 2015 and is currently available in 44 cities in India.


Three Ways AI Will Impact The Lending Industry

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Consider the massive size of real estate lending. The Fed's latest report shows mortgage debt topping $9 trillion. When including mortgages from businesses, it tops $15 trillion. Over 10 million homes and commercial properties sell each year. Equally staggering is how much data exists on the borrowers.


Byrider Selects PointPredictive as Machine Learning AI Partner to Prevent Fraud

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PointPredictive Inc., the San Diego-based machine learning company, announced today that Byrider has selected the companys risk scoring solutions to help them better segment high- and low-risk applications and dealers to improve profitability, expand loan availability and enhance the lending experience for both consumers and dealers. As part of the integration, Byrider will use the companys scoring solution " Auto Fraud Manager with Auto Fraud Alert Reporting " to identify misrepresentation and prevent default on high-risk applications while streamlining the approval process of low-risk applications to improve and expedite both the consumer and dealer loan funding experience, ultimately expanding their loan portfolio profitably. Byrider selected PointPredictives machine learning AI scoring after extensive testing of the solution and evaluating retrospective results. In our retrospective test with PointPredictive, we saw a significant lift in identifying defaults tied to misrepresentation and fraud, said Gary Harmon, Chief Risk Officer of Byrider. PointPredictive launched Auto Fraud Manager with Auto Fraud Alert Reporting to help address the $6 billion-dollar annual problem of misrepresentation and fraud that plagues the auto lending industry.


A.I. Could Be The New Play To Increase Minority Homeownership

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Artificial Intelligence and its inherent bias may not be as judgmental as previously thought, at least in the case of home loans. It appears the use of algorithms for online mortgage lending can reduce discrimination against certain groups, including minorities, according to a recent study from the National Bureau of Economic Research. This could end up becoming the main tool in closing the racial wealth gap, especially as banks start using AI for lending decisions. The Breakdown You Need to Know: The study found that in person mortgage lenders typically reject minority applicants at a rate 6% higher than those with comparable economic backgrounds. However, when the application was online and involved an algorithm to make the decision, the acceptance and rejection rates were the same.


10 Ways AI Is Going To Improve Fintech In 2020

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Bottom Line: AI & machine learning will improve Fintech in 2020 by increasing the accuracy and personalization of payment, lending, and insurance services while also helping to discover new borrower pools. Zest.ai's 2020 Predictions For AI In Credit And Lending captures the gradual improvements I've also been seeing across Fintech, especially at the tech stack level. Fintech startups, enterprise software providers, and the investors backing them believe cloud-based payments, lending, and insurance apps are must-haves to drive future growth. Combined with Internet & public cloud infrastructure and mobile apps, Fintech is evolving into a fourth platform that provides embedded financial services to any business needing to subscribe to them, as Matt Harris of Bain Capital Ventures writes in Fintech: The Fourth Platform - Part Two. Embedded Fintech has the potential to deliver $3.6 trillion in market value, according to Bain's estimates, surpassing the $3 trillion in value created by cloud and mobile platforms.


Privacy-Preserving Public Release of Datasets for Support Vector Machine Classification

arXiv.org Machine Learning

We consider the problem of publicly releasing a dataset for support vector machine classification while not infringing on the privacy of data subjects (i.e., individuals whose private information is stored in the dataset). The dataset is systematically obfuscated using an additive noise for privacy protection. Motivated by the Cramer-Rao bound, inverse of the trace of the Fisher information matrix is used as a measure of the privacy. Conditions are established for ensuring that the classifier extracted from the original dataset and the obfuscated one are close to each other (capturing the utility). The optimal noise distribution is determined by maximizing a weighted sum of the measures of privacy and utility. The optimal privacy-preserving noise is proved to achieve local differential privacy. The results are generalized to a broader class of optimization-based supervised machine learning algorithms. Applicability of the methodology is demonstrated on multiple datasets.


Importance of Artificial Intelligence in making lending easier and profitable - CIOL

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Over the year, evolution in global technologies has made people move forward from fixed phones to mobile phones. Today, every sector is readily and rapidly adapting the Artificial Intelligence (AI). With the fast-paced modern industries, AI is becoming an integral part of business operations. AI is not limited trend-based forecasting in marketing but its presence is getting indispensable in every vertical of the company. AI is much more efficient in analysing data patterns, based on these patterns companies acquire in-depth knowledge about their potential customers, their requirements and their behaviour.