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Founded Year



Series C | Alive

Total Raised


Last Raised

$100M | 3 yrs ago

About Behavox

Behavox is an enterprise compliance software company that provides employee surveillance solutions. The company's solutions allow senior management and risk and compliance officers to detect cases of market abuse, insider threat, collusion and reckless behavior in real time.

Behavox Headquarters Location

29 Albert Embankment Lambeth

London, England, SE1 7GR,

United Kingdom

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Expert Collections containing Behavox

Expert Collections are analyst-curated lists that highlight the companies you need to know in the most important technology spaces.

Behavox is included in 9 Expert Collections, including Digital Health.


Digital Health

8,838 items

Startups recreating how healthcare is delivered



1,157 items



1,341 items

Technology that addresses regulatory challenges and facilitates the delivery of compliance requirements in FIs. Regulatory technology helps FIs and regulators address challenges ranging from traditional compliance and risk management to data reporting and transmission.


SMB Fintech

1,230 items


Fintech 250

500 items

250 of the most promising private companies applying a mix of software and technology to transform the financial services industry.


AI 100

100 items

Behavox Patents

Behavox has filed 1 patent.

patents chart

Application Date

Grant Date


Related Topics




Natural language processing, Computational linguistics, Email, Tasks of natural language processing, Statistical natural language processing


Application Date


Grant Date



Related Topics

Natural language processing, Computational linguistics, Email, Tasks of natural language processing, Statistical natural language processing



Latest Behavox News

Enhancing Trade Compliance with Artificial Intelligence (AI)

Aug 5, 2022

External | what does this mean? This content is provided by an external author without editing by Finextra. It expresses the views and opinions of the author. Enhancing Trade Compliance with Artificial Intelligence (AI) 03 August 2022 0 Physicists may say otherwise, but it is trade that makes the world go round -- at least financially. From supply chain issues to volatility in prices across asset classes, from stocks to crude oil, trade defines much of the movement in the international economy. With trillions of dollars moving daily across the financial system, the temptation to indulge in surreptitious behaviour is great. Regulators, compliance officers and banking leaders have long sought effective tools to combat the increasing sophistication of bad actors, whose wrongdoing frequently leads to billions of dollars in financial losses. Compliance officers and regulators are looking to identify criminal actions such as insider trading, market manipulation, money laundering, violations of sanctions/export controls and trading in others' accounts more accurately and quickly. A recent example is the Libor scandal, where traders from banks colluded to set interest rates favourable to the traders rather than the clients. Other examples include front-running trades ahead of client trades, where the fiduciary duty to clients is not followed. Identifying suspicious activities requires the appropriate monitoring capabilities, especially given the significant communications and transactions trail that must be evaluated. For example, the Financial Times reported an analysis by Behavox, which showed that just 0.0024% of voice-based communications listened to and 0.0002% of texts analysed by Behavox were flagged as "concerning" in 2021. Despite their low frequency, the consequences for banks in terms of fines for regulatory and compliance actions are steep. Banks were fined $15 billion worldwide for such violations in 2020 alone. Artificial intelligence joins the fight for trade compliance Artificial intelligence (AI) is increasingly being used to fight financial transgressions. AI and associated machine learning (ML) or deep learning models provide regulators and compliance officers with new capabilities. These models can handle various data types, run a suite of advanced analytics and contribute an array of outputs to help remove fraud from international trade. Deep learning language models create a generational leap forward Some types of fraud cannot be uncovered using transaction ledgers, financial records and other tabular data alone. As an example of scale, Citi processed 9.4 million transactions in 2018, for about $1 trillion in trade, giving the bank a massive dataset of 25 million pages. In many cases, fraud happens outside these systems in processes involving unstructured data communications such as audio, images and chats. Here, the final source of record data contains minimal markers for identification, so advanced analytics are required to uncover discrepancies. No team can effectively read, interpret and flag potential wrongdoing within a data set that size in a realistic timeframe. Hence the need for deep learning models and the accelerated computing infrastructure that enables computers to support trade compliance. Before accelerated computing, training language and unstructured/semi-structured models would take weeks or months. Now, language and vision models can be trained in hours or days, and their outputs can be delivered in seconds. Given the real-time movement of money, models must be able to execute in milliseconds to prevent financial crimes. Real-time fraud prevention requires an understanding of spoken language. Not just one language, but multiple, in real-time, with the ability to understand context, describe sentiment, identify entities (businesses, people, etc.) and incorporate all of these complex inputs into a fraud-scoring algorithm. As data sizes increase exponentially, more sophisticated models are trained, requiring more advanced accelerated computing infrastructures to manage trade compliance effectively. In addition to unstructured data, tabular data can be analysed for activities such as front-running trades, insider trading and collusion. As international trade expands, the use of AI must follow The level of international trade will continue to grow in the long term as supply chains strengthen and the pandemic eases. As money flows increase, so will the number of bad actors looking to defraud the system for their own financial gain. Financial regulators, compliance leaders and bank officers must prioritise investment in AI, the premier tool capable of analysing all of the data (structured and unstructured) that powers financial markets. Where fraudulent activities are detected, compliance must ensure that a fine balance is maintained and that business units can continue to perform trading functions that generate legitimate profits. AI techniques can screen large amounts of data and identify activities/data with advanced algorithms that require further analysis. Existing systems are often ineffective or unduly flag large amounts of data from legacy systems, referred to as excess false positives. The reduction in false positives alone will create significant efficiencies and cost savings. It will enable financial entities to analyse reams of communications, trade data, and millions of inputs from thousands of sources in real-time. In the meantime, businesses can continue to trade and make profits, leveraging genuine arbitrage opportunities — a win-win for all market participants. 2403

Behavox Web Traffic

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Behavox Rank

  • When was Behavox founded?

    Behavox was founded in 2014.

  • Where is Behavox's headquarters?

    Behavox's headquarters is located at 29 Albert Embankment Lambeth, London.

  • What is Behavox's latest funding round?

    Behavox's latest funding round is Series C.

  • How much did Behavox raise?

    Behavox raised a total of $121.4M.

  • Who are the investors of Behavox?

    Investors of Behavox include SoftBank Group, Index Ventures, Citigroup, Promus Ventures and Hoxton Ventures.

  • Who are Behavox's competitors?

    Competitors of Behavox include Armorblox, KnowBe4, SteelEye, Clausematch, Mimecast, Material Security, Abnormal Security, BehavioSec, Area 1 Security, Shield and 28 more.

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