AlphaSense is a search engine for market intelligence that transforms how decisions are made by the world's leading corporations and financial institutions. Leveraging AI and NLP technology, AlphaSense enables knowledge professionals to extract insights in seconds from thousands of disparate data sources, including company filings, event transcripts, news and trade journals, and equity research. Founded in 2011 and backed by investors including Innovation Endeavors and Soros Fund Management, AlphaSense is on a mission to empower organizations to make data-driven decisions with confidence. We are seeking a passionate senior software engineer to join our AI Research team. The AI Research team develops the cutting edge deep learning, NLP and ranking systems that power AlphaSense Search and Recommendations.

AlphaSense's artificial intelligence (AI) tools--which include a combination of machine learning and natural language processing (NLP)--enable …

Ever since British economist John Maynard Keynes first declared that investors are prey to people's urge to act, however irrationally, the financial world has tried to quantify the impact of public sentiment on stock prices. Solving the puzzle would give investors in the know a huge advantage over the competition. Over the past decade, one vibrant corner of that still ongoing research has been data analysis. The goal has been to tease out clues about sentiment that are hidden in news articles, regulatory filings, transcripts, and press releases. With the rise of artificial intelligence, the sophistication of sentiment-measuring technology is increasing.

Evolving needs and investors behaviors, combined with the advancement of technology, have created opportunities for traditional players and new disruptors to reshaping the wealth management industry. In recent years, companies have arrived on the scene offering wealth management solutions leveraging technologies such as artificial intelligence (AI), big data and cloud computing. This breed of fintech companies, referred to as wealthtechs, is on the rise around the world. Switzerland's Sentifi was founded in 2012 with the goal of improving the information people all over the world use to make investment decisions. Sentifi uses AI and machine learning to analyze alternative data, classify it, rank the sources and link the results to over 50,000 global companies, commodities and currencies.

New York-based AlphaSense has created a software-based search engine that uses a unique mix of natural language processing and advanced linguistic tools, to uncover key insights for financial professionals in seconds. In an interview with Globalive Chairman Anthony Lacavera, Jack Kokko, CEO of AlphaSense, explains how their algorithms outperform the rest.

Machine learning is a type of artificial intelligence that provides computers with the ability to learn without being explicitly programmed. The science behind machine learning is interesting and application-oriented. Many startups have disrupted the FinTech ecosystem with machine learning as their key technology. There are various applications of machine learning used by the FinTech companies falling under different subcategories. Let us look at some of the applications of machine learning and companies using such applications.

Artificial intelligence may be all the craze in Silicon Valley, but on Wall Street, well, there's a lot of skepticism. High-powered algorithms are not a new phenomenon in finance, and for this industry, the name of the game is efficiency and precision. Quite frankly, finance executives want systems that, in one way or another, make money. Because of this, new wild and flashy AI systems that just make something "smart" won't fly. The fintech companies that are successfully leveraging AI today are the ones that have found a very concrete way to apply the technology to an existing business problem.

Machine learning is a type of artificial intelligence that provides computers with the ability to learn without being explicitly programmed. The science behind machine learning is interesting and application-oriented. Many startups have disrupted the FinTech ecosystem with machine learning as their key technology. There are various applications of machine learning used by the FinTech companies falling under different subcategories. Let us look at some of the applications of machine learning and companies using such applications.

We know that we can use artificial intelligence (AI) for trading stocks, and some hedge funds are making a killing in this space. In order to feed these algorithms, there are generally two types of financial data you can use; fundamental and market data. If you think about all the data related to a stock that describes the way it trades, you're thinking about market data. Using this data to make trades would be referred to as "technical trading" because it ignores the fundamentals behind stocks. People that do technical trading are often thought of as speculators by investors who invest based on fundamental data, things like profitability, revenues, valuations, etc.

For the casual internet user, a quick Google search is often all it takes to find plenty of information on any particular topic. But for specialized financial research, analysts often find themselves laboriously searching proprietary databases, regulatory filings, and paywalled sources that aren't even indexed by the big search engines, says Jack Kokko, the founder and CEO of financial search engine company AlphaSense. That's why he and cofounder and CTO Raj Neervannan created AlphaSense, which applies natural language processing and machine learning techniques to let users find relevant information in financial documents. "It started from my first job out of college as an analyst at Morgan Stanley, where I was, as every analyst, going through these huge piles of paper on my desk and trying to find information very manually--nights and days spent toiling through that information and still fearing that I'm missing a lot," Kokko says. The San Francisco-based company takes in information from thousands of licensed data sources, as well as public web sources like news reports, and automatically processes them to extract meaning on a sentence-by-sentence level.