Google has announced the launch of Lending DocAI, a dedicated artificial intelligence (AI) service for the mortgage industry. On Monday, Google Product Manager Sudheera Vanguri said the new solution, now in preview, has been designed to transform unstructured datasets into accurate models able to speed up loan applications by accurately assessing a borrower's income and assets. To streamline the loan application process, dubbed "notoriously slow and complex" by Vanguri, Lending DocAI has been built with AI models that specialize in document types related to loans and is able to automate "routine" document reviews so mortgage providers don't have to. The executive says that in turn, this will speed up the mortgage and loan application workflows, including the processing of loan sources and mortgage services. Lending DocAI can be applied to a range of documents including tax, income, and asset statements, capturing valuable data and potentially reducing the complexity of applying for a mortgage -- both for providers and hopeful borrowers.
Real estate tech company Snapdocs today closed a $60 million funding round. According to a spokesperson, the proceeds will be put toward product development and expanding the company's workforce. Mortgage lending is a complex process due to regulatory constraints, yet banks in many markets have managed to digitize parts of the mortgage journey, according to McKinsey. While hardly flawless, automated processing can give customers a degree of confidence they can afford the property they're interested in, and some lenders are able to complete the ordeal within minutes as opposed to the weeks it once took. Snapdoc's toolset aims to cut down on manual labor by streamlining processes wherever possible.
Custom DU is an automated underwriting system that enables mortgage lenders to build their own business rules that facilitate assessing borrower eligibility for different mortgage products. Developed by Fannie Mae, Custom DU has been used since 2004 by several lenders to automate the underwriting of numerous mortgage products. Custom DU uses rule specification language techniques and a web-based, user-friendly interface for implementing business rules that represent business policy. By means of the user interface, lenders can also customize their underwriting findings reports, test the rules that they have defined, and publish changes to business rules on a real-time basis, all without any software modifications. The user interface enforces structure and consistency, enabling business users to focus on their underwriting guidelines when converting their business policy to rules.
Life insurance provides trillions of dollars of financial security for hundreds of millions of individuals and fami lies worldwide. To simultaneously offer affordable products while managing this financial ecosystem, life-insurance companies use an underwriting process to assess the mortality risk posed by individual applicants. Traditional underwriting is largely based on examining an applicant's health and behavioral profile. This manual process is incompatible with expectations of a rapid customer experience through digital capabilities. Fortunately, the availability of large historical data sets and the emergence of new data sources provide an unprecedented opportunity for artificial intelligence to transform under writing in the life-insurance industry with standard measures of mortality risk.
Over the last few months, COVID-19 has taken over our lives and presented unprecedented threats to human life and the global economy. The mortgage industry has been severely affected as well. Life as we knew it is a distant reality and is expected to remain so for an extended period. COVID-19 has forced every industry to reassess its future. There is a compulsion to adapt swiftly and respond quickly to the new needs, opportunities, and challenges posed by our "new normal."
TrackStar.ai, a company led by credit industry veterans that specializes in predictive credit technology, today announced the launch of a new proprietary, predictive API designed to help lending institutions determine consumer lending potential. By utilizing this first-of-its-kind API, lenders are able to make better decisions about qualifying current and prior loan applicants. The result is lower acquisition costs and churn, all while reducing lender's reliance on outside partnerships for leads. TrackStar's API is designed for enterprise level banking institutions and lenders to help them optimize the customer acquisition and retention process. TrackStar's predictive AI layer determines which negative credit items could be removed from a customer's credit history, allowing lenders to extend offers to customers who might normally get declined or not even considered as qualifying loan applicants.
Rapid evolution in artificial intelligence (AI) applications, as well as improvements in computing power and the increasing availability of data, have led to significant growth in AI across most industries, write Leanne Mostert, a Partner and Wendy Tembedza, a Senior Associate at Webber Wentzel. The key developments in AI over the past few years have been driven by machine learning which, in turn, is fuelled by data. As more and more data is being gathered, so AI enables more sophisticated analysis of large data volumes. As the importance of data rises, so do the associated legal issues. In some cases businesses are free to use the data they hold for whatever purpose they want, including developing AI algorithms.
The fintech industry has been revolutionized by the computational arms race of the last two decades. Technologies like AI, Machine Learning, Neural Networks, evolutionary algorithms, Big Data Analytics, and more have enabled computers to crunch more varied, diverse, and deep datasets than ever before. Fremont, CA: Artificial Intelligence (AI) has become the buzzword for every industry, and in the last few years has transformed every aspect of the business. The fintech industry is no different. AI technology has improved precision levels, enhanced customer engagement levels, and quickened the query resolution period.
Until recently (through the end of 2018), LendingClub published a public dataset of all loans issued since the company's launch in 2007. With 2,260,701 loans to look at and 151 potential variables, my goal is to create a neural network model to predict the fraction of an expected loan return that a prospective borrower will pay back. Afterward, I'll create a public API to serve that model. Also, as you may have guessed from the preceding code block, this post is adapted from a Jupyter Notebook. If you'd like to follow along in your own notebook, go ahead and fork mine on Kaggle or GitHub.