Mortgages


Ai Ai Oh: Artificial Intelligence in the Mortgage Industry - Rate Zip

#artificialintelligence

This is not a blog about Old MacDonald or his farm. Instead it is about Artificial Intelligence (AI) in the mortgage industry. And we will NOT allow any sarcastic, caustic or offhand remarks about the mortgage industry needing some kind of intelligence. First of all, exactly what is artificial intelligence, at least how it is described of late? One thing it is not is fake intelligence (not related to fake news … and you might like this site that helps YOU create your own fake news … but I digress, and so soon ... sorry).


Big Data Trends in Financial Services

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NEW YORK, NY / ACCESSWIRE / February 7, 2020 / Humans are creating data at an exponential rate. In fact, 90% of the data in the world has been created in the past 2 years according to a 2015 IBM study. In the same study, it was estimated that we create 2.5 exabytes (2.5 quintillion bytes) of data every day. To put it in perspective, there are 18 zeros in a quintillion. As Big Data gets, well, bigger, it becomes even more important for executives and C-suites in financial services to stay ahead of the curve.


Roundup Of Machine Learning Forecasts And Market Estimates, 2020

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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.


How Machine Learning and A.I Will Help you Acquire a Mortgage

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AI is about as big a buzzword that has ever existed in the mortgage industry, on par with automated underwriting, cloud technology, and digital mortgages. Indeed, AI is intrinsically tied to these innovations. AI tools enhance automation, can be delivered through the cloud, and would significantly improve the production of digital mortgages. At the same time, AI is also one of the least understood terms in the mortgage industry. This fact is keeping most mortgage industry participants from realizing its full benefits.


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.


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.


Kyobo Life Insurance rolls out AI-based underwriting system - The Digital Insurer

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In Korea, Kyobo Life has announced the launch of its new AI-based underwriting platform called Best Analysis and Rapid Outcome (BARO). The platform employs machine learning technology with the ability to process large amounts of natural language data. Kyobo life's AI-based underwriting platform employs machine learning technology and has the ability to process large amounts of natural language data The platform provides real-time services to sales staff and customers. The platform leverages Kyobo Life's underwriting manual to facilitate online deliveries by enabling instant communication with its sales staff. BARO's intelligence allows for easy approval or denial of insurance contracts with the help of screening criteria for pre-existing conditions and medical history.


Investigating bankruptcy prediction models in the presence of extreme class imbalance and multiple stages of economy

arXiv.org Machine Learning

In the area of credit risk analytics, current Bankruptcy Prediction Models (BPMs) struggle with (a) the availability of comprehensive and real-world data sets and (b) the presence of extreme class imbalance in the data (i.e., very few samples for the minority class) that degrades the performance of the prediction model. Moreover, little research has compared the relative performance of well-known BPM's on public datasets addressing the class imbalance problem. In this work, we apply eight classes of well-known BPMs, as suggested by a review of decades of literature, on a new public dataset named Freddie Mac Single-Family Loan-Level Dataset with resampling (i.e., adding synthetic minority samples) of the minority class to tackle class imbalance. Additionally, we apply some recent AI techniques (e.g., tree-based ensemble techniques) that demonstrate potentially better results on models trained with resampled data. In addition, from the analysis of 19 years (1999-2017) of data, we discover that models behave differently when presented with sudden changes in the economy (e.g., a global financial crisis) resulting in abrupt fluctuations in the national default rate. In summary, this study should aid practitioners/researchers in determining the appropriate model with respect to data that contains a class imbalance and various economic stages.