Smart Mortgage Lending
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1 Whitepaper Smart Mortgage Lending Advanced Business Intelligence for the Residential Mortgage Industry Contents 1 Introduction 1 Analytics Today 2 From Data Warehouse to Data Lake 2 Machine Learning and Next-Generation Analytics 3 Data Warehouse 4 Ellie Mae Data Lake 5 The Advent of Smart Mortgage Applications Ellie Mae is changing the future of mortgage lending by enabling our customers to leverage machine learning and real-time data analytics to make better business decisions. 6 Business Intelligence for the Future 6 About Ellie Mae
2 In this paper we will examine the current and future state of business intelligence solutions, including recent advances in data analytics. Ellie Mae is a leading provider of innovative on-demand software solutions for the residential mortgage industry. More than 3,000 companies depend on our Encompass all-in-one mortgage management solution to automate the mortgage process and provide one system of record to improve compliance, loan quality and efficiency. Encompass helps automate the entire mortgage process, from marketing and customer relationship management (CRM) to point-of-sale and processing, all the way through closing and investor delivery. This enables banks, mortgage banks, credit unions and community banks of all sizes to keep data accurate, streamline workflow, and increase profitability. Today, Ellie Mae is building the next generation of mortgage management solutions and services on Amazon Web Services. As we move our solutions into the cloud and invest in a data lake infrastructure, we will be able to take advantage of machine learning to build next-generation business intelligence. In this paper we will examine the current and future state of business intelligence solutions, including recent advances in data analytics. We will also describe how Ellie Mae is changing the future of mortgage lending by enabling our customers to leverage machine learning and real-time data analytics to make better business decisions. Analytics today For more than a quarter century, the keystone of business intelligence has been the data warehouse. A data warehouse is a central data repository drawn from disparate sources for use in creating analytical reports. The data warehousing concept provides an architectural model for the flow of data from operational systems to decision support environments. Most enterprises today rely on data warehousing to store operational data, build dashboards and reports and measure key performance indicators (KPIs). Data warehousing has enabled enterprises to improve business intelligence by using operational data from different applications for cross-enterprise decision support. But as data sources become more varied and demand for real-time information grows, data warehousing strategy is hitting limitations. Data models in a warehouse are predefined, and the rigidity of the structure often requires IT to build dimensional models, or schema, and deploy data transformation tools for loading data from operational systems. Moreover, batch-based input and output processes make for prolonged lag times, making them a suboptimal choice for real-time analytics. 1
3 SMART MORTGAGE LENDING From data warehouse to data lake Three macro trends are disrupting the traditional approach to data analytics. First, as both enterprise and consumer companies invest in machine learning, intelligent applications will become the order of the day. Second, accustomed to personalized interaction from Netflix, Amazon and Facebook, an increasingly computer-savvy workforce will start demanding real-time information and greater software automation, including streaming data and fast data analysis. Third, continued data growth will force companies to adopt a more scalable, flexible infrastructure for managing their data. In response to this disruption, companies are shifting their business intelligence strategies from a batch-based business warehouse to a streaming data lake. The data lake is an enterprise-wide data management platform for analyzing data from disparate sources in native format. Instead of traditional batch processing, data streams in and out of the lake, thus enabling real-time storage and access. Since the data lake contains both structured and unstructured data, it enables the use of algorithms built on machine learning methods such as neural network modeling to create real-time reports based on massive volumes of data. Machine learning and next-generation analytics In the rapidly evolving world of big data, today s top enterprise and consumer technology companies, from SAP, IBM, Microsoft and Oracle to Amazon, Uber, Baidu, Google and Facebook, are investing heavily in machine learning. Machine learning is a field of computer science that gives computers the ability to learn from large amounts of data in a supervised environment. Evolved from the study of artificial intelligence, machine learning involves the construction of algorithms that can learn from data and make decisions using predictive models based on sample inputs. Using regression analysis or neural feedback, computers can determine dependencies and correlations across a wide range of variants to build predictive models. For example, using algorithms built on machine learning methods such as neural network models, the Stanford Artificial Intelligence Lab led by Fei-Fei Li has created software that can recognize scenes in still photographs and accurately describe them using natural language. 1 The data lake eliminates the need to define schema and transform data before writing it into the data warehouse. The data obtains structure only when it is read out of the lake using search index and columnar data store technology built into intelligent applications. Such intelligent applications are the wave of the future. Any technology company not investing in intelligent applications today is already late to the game. 1 Fei-Fei Li, How We re Teaching Computers to Understand Pictures, TED, March
4 Data Warehouse Dashboard Accounting LOS Reports HR ETL Business Warehouse Analytics Commissions 3
5 SMART MORTGAGE LENDING Ellie Mae Data Lake, AWS Machine Learning Customer Interface Real time analytics on Data Platform Other Data Sources Global Data Lake Repository LOS Data Ingestion based on change events Private Data Lakes per customer 4
6 The advent of smart mortgage applications To take advantage of data streaming and machine learning capabilities, Ellie Mae is building the next generation of mortgage management solutions on Amazon Web Services. Using data lake infrastructure for storage and access, machine learning models allow us to automate much of the loan origination process. Such automation affords lenders an opportunity to save time and money on a daily basis. For example, automated workflow can allow lenders to reduce the home appraisal process from days to seconds by taking advantage of Automated Valuation Models in those cases where an on-site appraisal may not be required. In addition, exception-based workflow allows for faster loan processing, as steps in the origination process are automated such that notifications take place on an exception basis only. For example, if an underwriter raises a condition incorrectly, the application can identify the error based on past trends and automatically notify the underwriter, thus eliminating unnecessary review cycles between processors and underwriters. Another key differentiator for Ellie Mae is our personabased approach. Unlike vendors who serve up large volumes of undifferentiated KPIs, forcing users to sort through piles of random metrics, Ellie Mae takes a persona-based approach to deliver analytics relevant to each user. This approach enables us to build intelligent applications, rather than treating analytics as a separate activity. No one goes to work saying, I m going to do analytics today, says Manish Garg, director of product management at Ellie Mae. Analysis is an important step in any number of specific business processes. That s why Ellie Mae delivers analytics within the context of specific tasks and provides notifications to prompt users when relevant new data impacts ongoing decision-making. Using data lake infrastructure for storage and access, machine learning models allow us to automate much of the loan origination process. 5
7 SMART MORTGAGE LENDING Business intelligence for the future About Ellie Mae Ellie Mae is building the next generation of mortgage management solutions to take advantage of big data, machine learning and data lake capabilities. By incorporating the latest developments in business intelligence technology, Ellie Mae solutions will enable our customers to make better business decisions on a daily basis. Ellie Mae (NYSE:ELLI) is a leading provider of innovative on-demand software solutions and services for the residential mortgage industry. Mortgage lenders of all sizes use Ellie Mae s Encompass all-in-one mortgage management solution, Mavent Compliance Service, and AllRegs research, reference and education resources to improve compliance, loan quality and efficiency across the entire mortgage lifecycle. Encompass helps automate the entire mortgage process, from marketing and customer relationship management (CRM) to point-of-sale and processing, all the way through closing and investor delivery. To learn more about Ellie Mae s business intelligence vision, call us at or sales@elliemae.com. 6
8 Ellie Mae, Inc Rosewood Drive, Suite 500 Pleasanton CA FAX elliemae.com 2017 Ellie Mae, Inc. Ellie Mae, Encompass, AllRegs, the Ellie Mae logo and other trademarks or service marks of Ellie Mae, Inc. appearing herein are the property of Ellie Mae, Inc. or its subsidiaries. All rights reserved. Other company and product names may be trademarks or copyrights of their respective owners MW
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