SBIR Phase II: Assessing Private Company Health Using Advanced Language Computing Techniques

这个小型企业创新研究(SBIR)第二阶段项目将开发一个针对金融机构(贷款人和投资者)的软件系统，为他们提供可操作的实时情报，了解私营公司的健康状况。这项正在开发的技术将扫描和解析数百万个结构化、半结构化和非结构化信息源，以寻找私人公司健康状况的信号。然后，基于背景，它将对这些不同信号的情绪和强度进行算法处理、分类和评估，以提供关于私营公司健康状况、其可能的融资需求以及金融机构最佳融资解决方案的全面、连贯和实时的视图-S产品组合。使用该公司的产品线，金融机构将能够以一种根本不同的、更智能、更具可扩展性和数据驱动的方式看待私营公司，使它们能够高效、智能地做出关键的融资和资本分配决策。具体地说，他们将有能力实时识别合适的私营公司，并将拥有他们可以用来为他们提供适当融资解决方案的情报。该系统处理各种结构化、半结构化和非结构化信息源的能力，以及以编程方式得出公司健康状况的衡量标准的能力，将对机构贷款和对私营公司的投资的精确度、严谨性和可扩展性产生深远的积极影响。今天，私营公司融资市场建立在高度不精确和不完美的试探法之上，这些试探法导致企业贷款违约率很高，或者在最糟糕的情况下导致银行倒闭，就像2009年发生的那样。这种情况的下游影响是，小企业得不到所需的融资，这一点在2009年就得到了证明，根据美联储的数据，当时寻求银行融资的私营小企业中，只有40%真正获得了所需的资金。根据美国小企业管理局(Small Business Administration)的数据，雇员少于500人的企业提供了全国一半以上的就业机会，占GDP的近一半。因此，至关重要的是，作为经济催化剂的健康私营企业能够获得融资。不幸的是，如果没有可信的、可操作的、可扩展的和实时的信息来区分健康和不健康的私营企业，金融机构仍然处于信息劣势。这增加了他们的风险，进而阻碍了成长、健康的私营公司获得所需的融资。如果部署成功，该提案所支持的技术有可能在市场上产生重大影响。

This Small Business Innovation Research (SBIR) Phase II project will develop an software system directed at financial institutions (lenders and investors) that will provide them with actionable, realtime intelligence into the health of private companies. The technology being developed will scan and parse millions of structured, semi-structured and unstructured information sources searching for signals of a private company's health. Then, based on context, it will algorithmically process, categorize and assess the sentiment and strength of these disparate signals to offer a comprehensive, coherent and real-time view of a private company's health, its likely financing needs and best fit financing solutions from a financial institution?s product portfolio. Using the company's line of products, financial institutions will be able to look at private companies in a fundamentally different, smarter, more scalable and data-driven way that empowers them to efficiently and intelligently make critical financing and capital allocation decisions. Specifically, they will have the potential to able to identify the right private companies in real-time and will be armed with intelligence they can use to offer them appropriate financing solutions.The system's ability to process a diversity of structured, semi-structured and unstructured information sources and programmatically derive measures of company health would have profound positive effects on the precision, rigor and scalability of institutional lending and investment into private companies. Today, the private company financing market is built on highly imprecise and imperfect heuristics that result in high business loan default rates, or at its worst, bank failures as occurred in 2009. The downstream impact of this is that small businesses do not get the financing they need as evidenced in 2009 when, according to the Federal Reserve, only 40% of private small businesses that sought bank financing actually received the funding they needed. Per the Small Business Administration, businesses with fewer than 500 employees account for more than half the nation's employment and nearly half of GDP. As a result, it is critical that healthy private companies which are an economic catalyst have access to financing. Unfortunately, without credible, actionable, scalable and real-time information which distinguishes between healthy and unhealthy private businesses, financial institutions remain at an informational disadvantage. This increases their risk, which in turn hinders growing, healthy private companies from receiving the financing they need. If successfully deployed, the technology being supported by this proposal has the potential to make a significant impact in the marketplace.