Wolters Kluwer Compliance Solutions and NYDIG have reached an agreement where Wolters Kluwer will serve as an exclusive preferred vendor to interested financial institutions seeking assistance in meeting their regulatory and compliance obligations while exploring and implementing services for bitcoin programs. Wolters Kluwer is positioned to provide regulatory compliance solutions and services that range from a deposit disclosure program to regulatory risk assessments for institutions looking to offer their customers access to bitcoin services provided by NYDIG. This collaboration grants Wolters Kluwer rights as the exclusive provider of Bitcoin Deposit Disclosure Program-NYDIG Accounts of those financial institutions seeking deposit documents when their clients open a NYDIG account. The Bitcoin Deposit Disclosure Program delivers compliant content necessary for U.S. banks and credit unions to compliantly offer NYDIG’s bitcoin program. By leveraging the NYDIG platform with Wolters Kluwer compliant content, U.S. banks and credit unions gain confidence to meet the growing mainstream interest in bitcoin to retain and grow their customer base and increase non-interest income opportunities. Wolters Kluwer Compliance Solutions, which sits within Wolters Kluwer’s Governance, Risk & Compliance (GRC) division, helps financial institutions efficiently manage risk and regulatory compliance obligations, and gain the insights needed to focus on better serving their customers and growing their business. Wolters Kluwer’s GRC division provides an array of expert solutions to help financial institutions manage regulatory and risk obligations. Wolters Kluwer Compliance Solutions’ iLien Motor Vehicle provides for the processing and management of motor vehicle titles and liens, helping solve the most unique and complicated challenges in title perfection. Its OneSumX® for Regulatory Change Management tracks regulatory changes and organizes them to create structured, value-added content through a single data feed that is paired with an easy-to-use software solution.
IBM Environmental Intelligence Suite is a SaaS solution designed to help organizations:
- Monitor for disruptive environmental conditions such as severe weather, wildfires, flooding and air quality and send alerts when detected;
- Predict potential impacts of climate change and weather across the business using climate risk analytics;
- Gain insights into potential operational disruptions and prioritize mitigation and response efforts;
- Measure and report on environmental initiatives and operationalize carbon accounting, while reducing the burden of this reporting on procurement and operations teams.
- The suite delivers environmental insights via APIs, dashboards, maps and alerts that can help companies address both immediate operational challenges as well as longer term planning and strategies. For instance, the suite could be used to help retailers prepare for severe weather-related shipping and inventory disruptions, or factor environmental risks into future warehouse locations; energy and utility companies to determine where to trim vegetation around power lines or which of their critical assets may soon be at greater risk from wildfires due to climate change. Or the suite could be used to help supermarkets get a clearer picture of how refrigeration systems are contributing to their overall greenhouse gas emissions and prioritize locations for improvement.
- Companies around the world are already using many of the core weather and AI technologies found within IBM’s Environmental Intelligence Suite.
Fraud becoming prevalent on BNPL platforms is going to contribute to the ‘too good to be true’ reaction that some people have when hearing about the service, and could drive down the flow of new users until it becomes less viable. As with so many other industries in a digitised, highly connected world, BNPL need to be fast and frictionless, allowing as many people as possible to be approved and move through the sign-up process as quickly as possible. Balancing speed and security is difficult for any industry, but for BNPL it will be crucial. This is why real-time data enrichment solutions are particularly suited to the BNPL space. It can carry out checks on every customer, assembling hundreds of data points from anything from whether they use a VPN to how old their Facebook profile is, building a complete picture of every customer to be able to apply adaptive friction – giving customers who exhibit common signs of fraud more security checks than those who show clear evidence of being a real person. If BNPL companies don’t address problems like fraud then the companies who do what they do in a safer, more customer-friendly way will take their market share, and if no companies can manage to find a way to offer BNPL services in a way that keeps customers safe then the industry will sink into obscurity as payday lending did. AI-powered fraud prevention could be a key part of stopping this from happening.
Embedded lending platform Lending Works, has announced a new multi-year partnership with Experian, the global information services provider.
The deal will provide Lending Works with access to Experian’s credit and affordability data to help underpin its credit decisioning engine and fraud prevention strategies. Experian’s Trended Data solution will help Lending Works understand whether consumers are on an upwards or downwards trajectory in their capacity to afford credit. Lending Works will also use Experian’s Affordability IQ service to identify if a customer has experienced a recent reduction in or loss of income as a result of the pandemic. Access to this data will help Lending Works to understand a customer’s affordability. It will also help Lending Works to proactively manage its customers’ financial wellbeing, ensuring they are treated fairly and responsibly throughout the life of a loan. The partnership will allow Lending Works to accelerate its growth whilst maintaining high credit standards.
- Startup Aware has built a framework aimed at organizations’ internal messaging boards. Aware plans to use the funding to continue expanding its technology, which is focused mainly on monitoring text-based conversations on company messaging platforms like Slack, Teams and Workplace (Facebook’s enterprise-focused service) for things like legal compliance, confidential information sharing, sentiment, toxic behavior and harassment. The plan is both to extend this to other mediums like video — Zoom and other videoconferencing tools being so critical these days in the workplace — as well as to continue enhancing the natural-language and other tools that it has to improve detection and responsiveness.
- A typical organization of 10,000 might send 180 million messages per year on Slack today, and the largest sends 1 billion — up between 300% to 1,000% on pre-COVID levels. This means that the only way to track whether anything illegal or toxic or otherwise is being passed around is by using software like Aware’s. Goldman Sachs, believe in the transformative power of technology and see potential in Aware’s ability to connect fragmented data that exists within organizations across many sectors.
- The financial sector risks inflicting significant damage on itself and companies across the world if it fails to use its “great power” to stop actions that harm the planet, a new report by EY, Microsoft and Earth Knowledge has said. The report urges financial companies to take four immediate actions to tackle biodiversity loss.
- First, they must publicly commit to playing an active role in delivering nature positive outcomes and embed it into their strategies and governance alongside climate change. They must also use their influence to engage with companies on priority biodiversity issues and leverage stewardship and engagement mechanisms established through efforts on climate change; understand the biodiversity risks in lending, insurance and investment portfolios and work with stakeholders to prioritize and overcome these; add new biodiversity expertise to existing best practices for carbon and climate change to accelerate execution; and use global biodiversity frameworks and targets to determine where red lines should be drawn if improvements cannot be evidenced.
- They must collaborate and engage on biodiversity at the policy level to accelerate the evolution of regulation that protects ecosystems and design communication plans that feed information learned into the organisation. Finally, they must measure, manage and report on their progress, while considering how to augment existing climate risk models with new biodiversity data sets, and new tools such as land mapping and planning tools.
Broadridge has launched Broadridge Anti-Money Laundering Solution (AMLS), that delivers an end-to-end machine learning (ML) powered Anti-Money Laundering platform covering transaction monitoring, name screening, alert prioritization, and customer risk scoring. The Broadridge AMLS is for firms and institutions looking to enhance their anti-money laundering surveillance to detect illicit money flows and bad actors. Broadridge’s product, powered by Tookitaki, is an ML-based AML platform that global financial institutions can use to substantially decrease false alerts and realize productivity gains while improving risk mitigation. It leverages innovative machine-learning techniques like AutoML, federated learning and network science to accurately detect complex money laundering activities and triage legacy system alerts through a Smart Alert Management system to accurately prioritize alerts for either rapid disposition or increased due diligence.
One large US bank is developing a prototype model for its annual Comprehensive Capital Analysis and Review (CCAR) as well as for the Current Expected Credit Loss (CECL) accounting standard, both of which require forecasting losses based on macroeconomic scenarios. The model will use machine learning to link economic variables with actual loss forecasts. The bank hopes this will generate new insights that are manually intensive using traditional modelling techniques. A US global systemically important bank (G-Sib) has been applying neural networks as a challenger model for its primary credit risk models in coming up with its CCAR forecasts. The machine learning model is capable of analysing non-linearities – changes in importance of variables – in data much faster than a traditional model. In retail credit, for example, during the first six months of the 39-month CCAR horizon, variables such as income and delinquency history are the primary determinants of default. Further out along the horizon, the importance of such variables decreases, and macroeconomic variables become the primary determinants. Using traditional methods, it would take data scientists months to instruct the model to capture non-linearities.
- NICE Actimize has augmented its AML solutions with real-time entity resolution capabilities from Senzing, an innovator in the entity resolution space. NICE Actimize will integrate the capability into its AML suite of solutions including Suspicious Activity Monitoring (SAM) and Customer Due Diligence (CDD-X). With integrated entity resolution now at the core of NICE Actimize AML solutions, financial services organizations will identify entities, find elusive as well as non-obvious relationships, and more accurately detect suspicious activity. To accomplish this, NICE Actimize is adopting entity resolution technology from Senzing.
- Senzing technology supports entity resolution despite data quality issues and better detects entity obfuscation while providing full data lineage for transparency, explainability and audit. The entity resolution engine identifies hidden connections between organizations and individuals, thereby enhancing risk management. The combination of NICE Actimize’s market-leading AML and Case Management solutions with the proven entity resolution software from Senzing, will allow organizations to focus on their customers and their customers’ networks, providing the context needed for more effective AML and risk management processes.
Consumer Fusion, Inc., a leading software company specializing in online reputation management, announced the launch of its N.E.W. (Negative Evaluation Widget) AI Reputation Management Tool, which helps businesses identify illegitimate reviews and predict the likelihood of their removal. Consumer Fusion utilized years of data from their CRM system to train the deep learning model to predict the probability of a review getting removed through a review dispute process. The company used 80,000 records for the first round of training and is continuously loading more review data, so the model may continue to learn to improve performance and accuracy. In the near future, Consumer Fusion will be adding the new tool to the backend team to utilize during their dispute process,. “The next step will be adding the tool to its real-time review tab so that clients may see the percentage probability of a negative review being removed.”