The financial sector is a highly regulated marketplace. Deepfakes or artificially-generated synthetic media are associated with political disinformation but have not yet been linked to the financial system. The Carnegie Endowment for International Peace issued a scintillating working paper series titled “Cyber Security and the Financial System” covering a wide range of cutting edge issues from the European framework for Threat Intelligence-Based Ethical Red Teaming (TIBER) to assessing cyber resilience measures for financial organizations to global policies to combat manipulation of financial data. Jon Bateman’s contribution titled “Deepfakes and Synthetic Media in the Financial System: Assessing Threat Scenarios” takes a closer look on how deepfakes can impact the financial system.
tl;dr
Rapid advances in artificial intelligence (AI) are enabling novel forms of deception. AI algorithms can produce realistic “deepfake” videos, as well as authentic-looking fake photos and writing. Collectively called synthetic media, these tools have triggered widespread concern about their potential in spreading political disinformation. Yet the same technology can also facilitate financial harm. Recent months have seen the first publicly documented cases of deepfakes used for fraud and extortion. Today the financial threat from synthetic media is low, so the key policy question is how much this threat will grow over time. Leading industry experts diverge widely in their assessments. Some believe firms and regulators should act now to head off serious risks. Others believe the threat will likely remain minor and the financial system should focus on more pressing technology challenges. A lack of data has stymied the discussion. In the absence of hard data, a close analysis of potential scenarios can help to better gauge the problem. In this paper, ten scenarios illustrate how criminals and other bad actors could abuse synthetic media technology to inflict financial harm on a broad swath of targets. Based on today’s synthetic media technology and the realities of financial crime, the scenarios explore whether and how synthetic media could alter the threat landscape.
Make sure to read the full paper titled Deepfakes and Synthetic Media in the Financial System: Assessing Threat Scenarios by Jon Bateman at https://carnegieendowment.org/2020/07/08/deepfakes-and-synthetic-media-in-financial-system-assessing-threat-scenarios-pub-82237
Deepfakes are a variation of manipulated media. In essence, a successful deepfake requires a sample data set of a original that is used to train a deep learning algorithm. It will learn to alter the training data to a degree that another algorithm is unable to distinguish whether the presented result is altered training data or the original. Think of it as a police sketch artist who will create a facial composite based on eye-witness accounts. The more available data and time the artist has to render a draft, the higher the likelihood of creating a successful mugshot sketch. In this paper, the term deepfake relates to a subset of synthetic media including videos, images and voice created through artificial intelligence.
The financial sector is particularly vulnerable in the know-your-customer space. It’s a unique entry point for malicious actors to submit manipulated identity verification or deploy deepfake technology to fool authenticity mechanisms. While anti-fraud prevention tools are an industry-wide standard to prevent impersonation or identity theft, the onset of cheaper, more readily available deepfake technology marks a turning point for the financial sector. Deepfakes may be used to leverage a blend of false or hacked personal identifiable information (PII) data to gain access or open bank accounts, initiate financial transactions, or redistribute private equity assets. Bateman focused on two categories of synthetic media that are most relevant for the financial sector: (1) narrowcast synthetic media, which encompasses one-off, tailored manipulated data deployed directly to the target via private channels and (2) broadcast synthetic media, which is designed for mass-audiences deployed directly or indirectly via publicly available channels, e.g. social media. An example for the first variation is the story of a cybercrime that took place in 2019. A Chief Executive Officer of a UK-based energy company received a phone call from – what he believed – his boss, the CEO of the parent corporation based in Germany. In the phone call, the voice of the German CEO was an impersonation created by artificial intelligence and publicly available voice recordings (speeches, transcripts etc). The voice directed the UK CEO to immediately initiate a financial transaction to pay a Hungarian supplier. This type of attack is also known as deepfake voice phishing (vishing). These fabricated directions resulted in the fraudulent transfer of $234,000. An example for the second variation is commonly found in widespread pump and dump schemes on social media. These could range from malicious actors creating false, incriminating deepfakes of key-personnel of a stock-listed company to artificially lower the stock price or creating synthetic media that misrepresents product results to manipulate a higher stock price and garner more interest from potential investors. Going off the two categories of synthetic media, Bateman presents ten scenarios that are layered into four stages: (1) Targeting Individuals, e.g. identity theft or impersonation, (2) Targeting Companies, e.g. Payment Fraud or Stock Manipulation, (3) Targeting Financial Markets, e.g. creating malicious flash crashes through state-sponsored hacking or cybercriminals backed a foreign government, and (4) Targeting Central Banks and Financial Regulators, e.g. regulatory astroturfing.
In conclusion, Bateman finds that at this point in time, deepfakes aren’t potent enough to destabilize global financial systems in mature, healthy economies. They are more threatening, however, to individuals and business. To take precautions against malicious actors with deepfake technology, a number of resiliency measures can be implemented: broadcast synthetic media is potent to amplify and prolong already existing crises or scandals. Aside from building trust with key audiences, a potential remedy to deepfakes amplifying false narratives is the readiness to create counter-narratives with evidence. To prevent other companies from potential threats that would decrease the trust in the financial sector, an industry wide sharing of information on cyber attacks is a viable option to mitigate coordinated criminal activity. Lastly, the technology landscape is improving its integrity at a rapid succession rate. A multi-stakeholder response bringing together leaders from the financial sector, the technology sector and experts on consumer behavior with policymakers will help to create more efficient regulations to combat deepfakes in the financial system.
misterfergusson 