A leading global corporate, fiduciary and fund administrator has selected Predict360's AI-powered Regulatory Change Management solution to track, evaluate and manage regulatory requirements and changes, 360factors announced today. With its unique mapping abilities and Artificial Intelligence (AI) with Natural Language Processing (NLP) technology, Predict360 streamlines the process of regulatory change management (RCM) by first documenting regulatory changes and updates via external sources such as news feeds, then providing a mechanism to document applicability. An audit trail that associates users with change assessments provides an accountability layer to the entire organization. If a regulatory change is determined to be applicable to the organization, the solution will automatically trigger a regulatory change workflow process that enables stakeholders to manage project plans across business units and product lines along with related artifacts such as, documents, policies and procedures, assessment checklists and more. The solution provides proactive notifications to all relevant parties across the organization that are associated with that regulation and/or change, including due dates, effective dates, action items, and supporting documentation.

Hot off the press today is a FICO-commissioned research study on artificial intelligence and how Chief Analytics Officers (CAOs) and Chief Data Officers (CDOs) are responding to the current pandemic, economic uncertainty, and renewed focus on social justice. In additional to a survey, in-depth interviews with the top AI leaders at HSBC, AXA PPP, Banorte, and Chubb provides additional perspective and commentary. The entire 24-page report is available for download; however I wanted to share some highlights from the research that I found to be particularly impactful, or perhaps even surprising given the amount of hype around AI in the market today. The pandemic has caused a drastic shift in consumer behavior as individuals stay at home and adjust their daily routines. Many travel, hospitality, and restaurant workers are out of work, and those fortunate to still be employed have shifted their spending patterns.

You cannot learn to play the piano by going to concerts. A compass [will] point you True North from where you're standing, but it's got no advice about the swamps and deserts and chasms that you'll encounter along the way. If in pursuit of your destination, you plunge ahead, heedless of obstacles, and achieve nothing more than to sink in a swamp... What's the use of knowing True North? The practice of surgery often forces unique ad hoc decisions based on contextual intricacies in the moment, which are not typically captured in broad, top-down, or committee-approved guidelines. Surgical ethics are principled, of course, but also pragmatic. They are also replete with moral contradictions and uncertainties; the introduction of novel technology into this environment can potentially increase those challenges. The essential element that distinguishes an ethical problem from a tragic situation is the element of choice." Moreover, choosing between options often involves identifying factors by which those options are not exactly equal, and the method one uses to weigh these factors can draw upon a set of ethical frameworks that, themselves, can be somewhat incongruous. At their core, artificial intelligence (AI) systems - and machine learning (ML) more specifically - are also designed to make choices, often by categorizing some input among a set of nominal categories. In the past, the choices these systems made could only be evaluated by their correctness - their accuracy in applying the same categorical labels that a human would to previously unseen inputs, like whether an image contains a tumour, or not.

It took a global pandemic and the death of George Floyd to put deep-seated social inequities, especially systemic racism, front and center for intense public debate. We may or may not be on the cusp of a redressing social injustice by reordering our legacy political and economic systems. Either way, a singular piece of technology – artificial intelligence (AI) -- is destined to profoundly influence which way we go from here. This is not just my casual observation. Those in power fully recognize how AI can be leveraged to preserve status-quo political and economic systems, with all of its built-in flaws, more or less intact.

An online app called Amica is now using artificial intelligence to help separating couples make parenting arrangements and divide their assets. For many people, the coronavirus pandemic has put even the strongest of relationships to the test. A May survey conducted by Relationships Australia found 42 percent of 739 respondents experienced a negative change in their relationship with their partner under lockdown restrictions. There has also been a surge in the number of couples seeking separation advice. The Australian government has backed the use of Amica for those in such circumstances.

The world has a new AI toy, and it's called GPT-3. The latest iteration of OpenAI's text generating model has left many starstruck by its abilities – although its hype may be too much. GPT-3 is a machine learning system that has been fed 45TB of text data, an unprecedented amount. All that training allows it to generate sorts of written content: stories, code, legal jargon, all based on just a few input words or sentences. And the beta test has already produced some jaw-dropping results.

Assortment planning, an important seasonal activity for any retailer, involves choosing the right subset of products to stock in each store.While existing approaches only maximize the expected revenue, we propose including the environmental impact too, through the Higg Material Sustainability Index. The trade-off between revenue and environmental impact is balanced through a multi-objective optimization approach, that yields a Pareto-front of optimal assortments for merchandisers to choose from. Using the proposed approach on a few product categories of a leading fashion retailer shows that choosing assortments with lower environmental impact with a minimal impact on revenue is possible.

Social media platforms now serve billions of users by providing convenient means of communication, content sharing and even payment between different users. Due to such convenient and anarchic nature, they have also been used rampantly to promote and conduct business activities between unregistered market participants without paying taxes. Tax authorities worldwide face difficulties in regulating these hidden economy activities by traditional regulatory means. This paper presents a machine learning based Regtech tool for international tax authorities to detect transaction-based tax evasion activities on social media platforms. To build such a tool, we collected a dataset of 58,660 Instagram posts and manually labelled 2,081 sampled posts with multiple properties related to transaction-based tax evasion activities. Based on the dataset, we developed a multi-modal deep neural network to automatically detect suspicious posts. The proposed model combines comments, hashtags and image modalities to produce the final output. As shown by our experiments, the combined model achieved an AUC of 0.808 and F1 score of 0.762, outperforming any single modality models. This tool could help tax authorities to identify audit targets in an efficient and effective manner, and combat social e-commerce tax evasion in scale.

Left alone, algorithms will count a black defendant's race as a strike against them; yet, several data scientists in the community are supporting calls to turn off the safeguards and unleash the hells of computerized prejudice. Put yourself in the computer's "shoes" for a second; imagine yourself sitting across a person being evaluated for a loan or a job. When they ask you how you make your decision, you inform them, "Well for one, we docked you because you're black." In what logical sense should this sort of comment be tolerated. If humans are reprimanded for making such ignorant comments, why should a computer be allowed to? This simple understanding does not exist amongst a significant percentage of the larger data science, machine learning, and even political community.

Legal professionals and aspiring lawyers may soon start facing competition from artificial intelligence (AI) which could take over day-to-day tasks in the next three to five years. A survey by BML Munjal University (BMU) School of Law and legal search/consulting firm Vahura showed that 90 percent of the respondents (lawyers) foresee use of digitisation and technology in the sector. The survey titled'Decoding the Next - Gen Legal Professional' sought to capture the practitioners' perspective of the practice of law and to identify the relevant skills required of lawyers in the rapidly transforming legal environment in India. In an interaction with Moneycontrol, Nigam Nuggehalli, professor and dean of the BMU School of Law said that tasks like due diligence that is traditionally done by legal professionals could be taken over by AI. "In areas like due diligence which requires detailed inspection, maybe AI can do it better," he added. According to the survey, technology solutions in the legal space may replace some human roles at the entry-level by way of automating repetitive and standardized work but are expected to augment others such as reviewing documents more efficiently.