SBIR Phase I: Predictive and Computational Technologies for the Mortgage Industry

SBIR 第一阶段：抵押贷款行业的预测和计算技术

• 批准号: 2015154
• 负责人: Alex Papanicolaou
• 金额: $ 22.38万
• 依托单位: INTELLIGENT FINANCIAL MACHINES LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2015154&HistoricalAwards=false
• 关键词: SBIR; Phase; Predictive; Computational; Technologies

The broader impact of this Small Business Innovation Research (SBIR) Phase I project will result from providing mortgage market participants such as lenders, servicers, insurers, investors, rating agencies, government sponsored enterprises, and regulators with integrated deep learning systems that offer actionable predictions of borrower, portfolio, security, and market behavior of unprecedentedly high accuracy and low latency at scale. The systems will enable these organizations to identify valuable opportunities, reduce losses, and improve staff utilization while dramatically lowering compute costs in a $2 billion annual mortgage decision and risk analytics market. Wide adoption will boost the performance of the American housing-finance system, benefiting homeowners and the broader population through lowering borrowing costs, expanding access to credit, and reducing the risk of future financial crises. The Phase I project focuses on developing transformative computational algorithms that make comprehensive deep learning predictions available in real time, at a fraction of the cost of existing computational technologies. It yields new insights into how computational algorithms can significantly enhance the benefits of AI prediction systems. This Small Business Innovation Research (SBIR) Phase I project seeks to address the core technical challenge associated with the development of powerful deep learning systems for measuring risk and identifying opportunities in the mortgage industry. This challenge is the construction of novel and transformative asymptotic-approximation algorithms to run in real time, rather than the hours or days prior technology requires. Examples of such applications include measuring the risks of large pools of mortgages over long horizons and the risks of mortgage securities backed by such pools. The dramatic running time gains offered by these algorithms are the key to harnessing the unprecedented predictive accuracy of deep learning models of individual borrower behavior. The key objectives of the proposed project are to develop a large-pool asymptotic approximation approach that offers run-time guarantees for deep learning models, and to construct efficient numerical schemes for implementing the resulting algorithms in a cloud environment.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个小企业创新研究（SBIR）第一阶段项目的更广泛影响将来自于为抵押贷款市场参与者，如贷款人，服务商，保险公司，投资者，评级机构，政府赞助的企业和监管机构提供集成的深度学习系统，提供对借款人，投资组合，证券和市场行为的可操作预测，具有前所未有的高准确性和低延迟。这些系统将使这些组织能够识别有价值的机会，减少损失，提高员工利用率，同时在每年20亿美元的抵押贷款决策和风险分析市场中大幅降低计算成本。广泛采用将提高美国住房金融体系的绩效，通过降低借贷成本、扩大信贷渠道和降低未来金融危机的风险，使房主和更广泛的人群受益。 第一阶段项目的重点是开发变革性的计算算法，以真实的时间提供全面的深度学习预测，而成本仅为现有计算技术的一小部分。它对计算算法如何显着增强AI预测系统的优势产生了新的见解。 小企业创新研究（SBIR）第一阶段项目旨在解决与开发强大的深度学习系统相关的核心技术挑战，以衡量抵押贷款行业的风险和识别机会。这一挑战是构建新颖的和变革性的渐近近似算法，以在真实的时间内运行，而不是之前的技术需要的几个小时或几天。这类应用的例子包括衡量长期大量抵押贷款的风险以及由这类抵押贷款支持的抵押贷款证券的风险。这些算法提供的巨大运行时间增益是利用个人借款人行为深度学习模型前所未有的预测准确性的关键。该项目的主要目标是开发一种大型池渐近近似方法，为深度学习模型提供运行时保证，并构建高效的数值方案，以便在云环境中实现所产生的算法。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。