Shield helps people and organizations turn compliance into a competitive edge; moreover, it has revolutionized financial compliance. "Shield is a modern, agile, hybrid-cloud solution that enables organizations across highly regulated industries to'read between the lines'. It allows compliance teams to easily monitor all communication channels, such as Teams, WhatsApp and Zoom, from a single platform. Our end-to-end platform, equipped with extensive protection and transparency, offers greater flexibility, visibility, and control of alerts or triggers, especially when they could contain sensitive and confidential data." Center-stage startups may soon become game-changers.

The financial compliance world always seems to be perpetually speeding up. But with the last three years precipitating a digital transformation push like the world has never seen, the perpetually influx compliance industry has become even more hectic than usual. Financial businesses today simply do not function the way that they used to. And while the compliance field has done an admirable job of making incremental shifts to plug the gaps that inevitably open up as change comes to pass, the field has reached a watershed moment in its approach to modernization, especially when it comes to risk. The modern business world consists of a vast network of priorities and emerging challenges that simply can not be managed using traditional research methods and legwork alone.

Shield, the world's leading communication compliance platform, launched impressive surveillance capabilities that enable banks and finance firms to bring communication compliance into the control room and protect against data leaks. InfoBarriers, the company's newest AI-model, is included in the latest version of Shield (3.2), which introduces additional new and substantial capabilities unmatched by existing legacy vendors and emerging startups. Also included in Shield 3.2 are enhanced search analytics, case workspaces for more visibility and traceability into eDiscovery, and further refinements to user interface (UI). Shield's data scientists, with deep expertise in trade and securities compliance, have developed InfoBarriers to detect information barrier leaks hiding throughout electronic communication channels. Through control room protocols, InfoBarriers enables organizations to secure material non-public information (MNPI) behind deal and research lists.

Now more than ever, every company is a data company. By 2025, individuals and companies around the world will produce an estimated 463 exabytes of data each day, 1 1. Jeff Desjardins, "How much data is generated each day?" World Economic Forum, April 17, 2019. With that in mind, most businesses have begun to address the operational aspects of data management--for instance, determining how to build and maintain a data lake or how to integrate data scientists and other technology experts into existing teams. Fewer companies have systematically considered and started to address the ethical aspects of data management, which could have broad ramifications and responsibilities. If algorithms are trained with biased data sets or data sets are breached, sold without consent, or otherwise mishandled, for instance, companies can incur significant reputational and financial costs. Board members could even be held personally liable.

There is a key topic that banks spanning the world are asking right now: how are they able to make AI explainable? This was the opinion of Wolfgang Berner, the CTO of RegTech firm Hawk: AI, who recently presented a keynote speech on the above topic. Berner remarked, "In heavily regulated areas such as combating money laundering, considerations as to how transparent and comprehensible the use of artificial intelligence is are entirely appropriate. Classic concerns about such a "black box AI" arise in particular when the decisions of the AI are too disconnected from the original data and when there is no transparency about the way the algorithms work." Hawk AI sees the key to trust and acceptance in the compliance industry in the high level of transparency.

Implementing AI can introduce disruptive change and disfranchise staff and employees. When members are reluctant to adopt a new technology, they might hesitate to use it, push back against its deployment, or use it in limited capacity — which affects the benefits an organization gains from using it in the first place. Organizations often don’t see the problems coming, and rollout a new tool too quickly only for it to run into major barriers. To navigate this process, we propose a three-step approach: 1) assess the impact of an AI solution, 2) identify barriers to adoption, and 3) identify the appropriate pace.

2021 was another bumper year for fines slapped against financial institutions (FIs) for failures in anti-money laundering (AML) compliance. AML shortcomings in transaction monitoring are a global problem. Countries whose banks were hit with fines include the United States, Germany, the Netherlands, Norway, Latvia, France, the UAE, India, Malaysia, and South Africa.  Fines imposed on FIs by regulators could reach as high as $2 billion for a second year running when the final figures come in, according to estimates. The continuous vigilance of regulators should serve as a wake-up call for financial institutions worldwide to take stock in failures and take action to change the trend in 2022. Some guilty parties lacked an AML compliance culture or even engaged in outright fraud and corruption. Others turned a blind eye. For FIs investing in large and costly compliance teams and tools, it’s surely frustrating to be hit with fines of tens

When developing models for regulated decision making, sensitive features like age, race and gender cannot be used and must be obscured from model developers to prevent bias. However, the remaining features still need to be tested for correlation with sensitive features, which can only be done with the knowledge of those features. We resolve this dilemma using a fully homomorphic encryption scheme, allowing model developers to train linear regression and logistic regression models and test them for possible bias without ever revealing the sensitive features in the clear. We demonstrate how it can be applied to leave-one-out regression testing, and show using the adult income data set that our method is practical to run.

Financial regulators around the world are cracking down on banks. With Anti-Money Laundering (AML) and Know-Your-Customer (KYC) procedures being put under the microscope, huge fines are being levied against institutions which are found to be in breach. In fact, recent study discovered that over the past ten years, banks across the globe have been slapped with a total of US$26 billion in monetary penalties for AML and sanctions violations. As banks and financial institutions embark on digital transformation initiatives to streamline and simplify the customer onboarding process and reduce risk associated with fraud, many are eyeing the potential of emerging technologies. This enables financial institutions to simplify the process of identifying illicit client relationships, beneficiaries and links to criminal or terrorist activity during the onboarding phase.

As governments, businesses, organisations and workers figure out how to operate in the new normal brought on by COVID-19, technology, big data, and artificial intelligence are playing an important role. Some governments are deploying contact-tracing technologies, including app-based tracking and facial recognition, to identify those who may be at risk of infection and to keep others at a distance.1 To increase workplace safety and create a sense of security among staff, many organisations may follow the lead of those governments and launch contact-tracing capabilities in the office. Technologies that protect workplace safety will be instrumental in helping employees feel secure enough to go back to the office -- and back to a semblance of normalcy. According to PwC's CFO Pulse, 41 percent of surveyed chief financial officers consider the pandemic's effects on their workforce to be a top-three concern.