fannie mae
Digital Marketing - Data Analyst (Hybrid) at Fannie Mae - Washington, DC, United States
At Fannie Mae, futures are made. The inspiring work we do makes an affordable home a reality and a difference in the lives of Americans. Every day offers compelling opportunities to impact the future of the housing industry while being part of an inclusive team thriving in an energizing environment. Here, you will help lead our industry forward and make your career. As a valued colleague on our team, you will contribute to planning and implementing all aspects of content marketing in the consumer journey, as well as drive brand and business value with great digital experience design and content.
- Marketing (0.91)
- Banking & Finance > Real Estate (0.69)
- Information Technology > Data Science > Data Mining > Big Data (0.43)
- Information Technology > Artificial Intelligence (0.40)
Data Scientist - Finance (Hybrid) at Fannie Mae - Washington, DC, United States
At Fannie Mae, futures are made. The inspiring work we do makes an affordable home a reality and a difference in the lives of Americans. Every day offers compelling opportunities to modernize the nations housing finance system while being part of an inclusive team using new, emerging technologies. Here, you will help lead our industry forward, enhance your technical expertise, and make your career. As a valued colleague on our team, you will work with your team to apply fundamental techniques to support production of insights, new product or change recommendations, process improvement or automation, and predictive modeling.
- Government > Regional Government > North America Government > United States Government (0.66)
- Government > Housing (0.66)
- Banking & Finance > Real Estate (0.66)
- (2 more...)
Finance - Data Science - Associate-101193-TEMPLATE at Fannie Mae - Washington, DC, United States
GENERAL BOILERPLATE At Fannie Mae, futures are made. The inspiring work we do makes an affordable home a reality and a difference in the lives of Americans. Every day offers compelling opportunities to impact the future of the housing industry while being part of an inclusive team thriving in an energizing environment. Here, you will help lead our industry forward and make your career. CORPORATE PROFESSIONAL At Fannie Mae, futures are made.
- Banking & Finance > Real Estate (1.00)
- Government > Regional Government > North America Government > United States Government (0.88)
- Government > Housing (0.88)
- Banking & Finance > Loans > Mortgages (0.88)
Custom DU: A Web-Based Business User-Driven Automated Underwriting System
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.
- Banking & Finance > Real Estate (1.00)
- Banking & Finance > Loans > Mortgages (1.00)
- Banking & Finance > Insurance (1.00)
Paid Program: Infusing Intelligence
From stock trades to credit card purchases, the financial services sector has always been a powerhouse of data generation. Today, many financial institutions are harnessing insights from this data--using artificial intelligence and machine-learning tools to serve customers in new, innovative ways and quickly expand their service offerings. According to a 2020 survey of over 150 financial services firms by the Cambridge Centre for Alternative Finance and the World Economic Forum, artificial intelligence is expected to become an essential business driver across the industry, with 77% of respondents anticipating AI will "possess high or very high overall importance to their businesses within two years." The same survey revealed the range of applications of AI and machine learning, from risk management and product development to customer service and client acquisition. So it's no secret that AI and machine learning are playing a foundational role in financial services.
Reinvented mortgage lending with the new URLA and AI
Financial institutions have a wealth of information available to them from consumers. Due to manual and antiquated models, residential lending processes so far have had several negative experiences for both the lender and the borrower. Banks are plagued with application limitations, transaction complexities and data collection and processing challenges. The'one-size-fits-all' loan application simply does not work anymore. The newly implemented and redesigned URLA (Uniform Residential Loan Application), aims to simplify, organize and streamline the entire consumer journey – from loan request, to the underwriting and approval process.
Custom DU--A Web-Based Business User-Driven Automated Underwriting System
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.
Bitly
This is the third in a series of posts on how to build a Data Science Portfolio. If you like this and want to know when the next post in the series is released, you can subscribe at the bottom of the page. Data science companies are increasingly looking at portfolios when making hiring decisions. One of the reasons for this is that a portfolio is the best way to judge someone's real-world skills. The good news for you is that a portfolio is entirely within your control. If you put some work in, you can make a great portfolio that companies are impressed by. The first step in making a high-quality portfolio is to know what skills to demonstrate. Any good portfolio will be composed of multiple projects, each of which may demonstrate 1-2 of the above points. This is the third post in a series that will cover how to make a well-rounded data science portfolio.
- Banking & Finance > Loans (0.32)
- Banking & Finance > Real Estate (0.31)
Custom DU: A Web-Based Business User-Driven Automated Underwriting System
Krovvidy, Srinivas (Fannie Mae)
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. Once lenders have created their rules, loans are routed to the appropriate rule sets, and customized, but consistent, results are always returned to the lender. Using Custom DU, lenders can create different rule sets for their products and assign them to different channels of the business, allowing for centralized control of underwriting policies and procedures—even if lenders have decentralized operations.
- North America > United States > New York > Suffolk County > Stony Brook (0.04)
- North America > United States > Illinois > Cook County > Chicago (0.04)
- North America > United States > Georgia > Fulton County > Atlanta (0.04)
- (3 more...)
- Banking & Finance > Risk Management (1.00)
- Banking & Finance > Real Estate (1.00)
- Banking & Finance > Loans > Mortgages (1.00)
- Banking & Finance > Insurance (1.00)