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KDD 2021 Celebrates Winning Teams of 25th Annual KDD Cup

The Association for Computing Machinery (ACM) Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD) recognized the winning teams of this year’s KDD Cup, the annual competition held at KDD 2021, the premier interdisciplinary conference in data science. KDD Cup 2021, which took place virtually Aug. 14-18, 2021, crowdsourced participants who are helping to solve challenges within the knowledge discovery and data mining industry, providing a platform for aspiring and experienced data scientists alike to build their professional profiles and network with leading professionals in the field during KDD 2021. 



How Governing Observability Data is Critical to ESG Success

In this contributed article, Nick Heudecker, Senior Director of Market Strategy at Cribl, discusses how observability data comprises the logs, events, metrics, and traces that make things like security, performance management, and monitoring possible. While often overlooked, governing these data sources is critical in today’s enterprises. The current state of observability data management is, at best, fragmented and ad hoc. By adopting an observability pipeline as a key component in your observability infrastructure, you can centralize your governance efforts while remaining agile in the face of constant change.



Why QIIB trusts IBM Safer Payments for cross-channel fraud prevention

Fraud prevention is about who you can trust. For financial institutions like Qatar International Islamic Bank (QIIB), it’s about understanding the relative risk of a customer, a merchant and/or a transaction, as well as hundreds of different factors including location, amount, device, etc.

But for customers, both actual fraud attacks as well as incorrectly blocked legitimate transactions represent a breach of that trust. In the former instance, the customer trusted the institution to protect them from such attacks, and the fraud took place anyway. Luckily, most jurisdictions have regulations in place to make a customer whole after such an event. But in the latter case, there is an erosion of trust as well.

When a bank or payment processor blocks a legitimate transaction, it creates doubt in the customer that the organization can be counted on to complete their payment. Why would you choose a financial provider that may or may not be able to execute your transaction? In addition to losing share of wallet, the customer may begin to ask, “Why would I bank with a financial institution that doesn’t even know me?”

Why QIIB trusted IBM Safer Payments

QIIB is one of the Qatar’s leading banks, recently selected IBM to deliver cross-channel fraud prevention to its customers. This supports QIIB’s aim to provide its customers the ability to bank securely while delivering an innovative and positive customer experience.

Many conveniences that customers enjoy with today’s modern banking services carry an increased risk of fraud. Legacy fraud prevention systems were designed to see and stop easily recognizable fraud patterns. However today’s anytime, anywhere banking online and on mobile devices has made fraud detection much more challenging. Banks’ time to respond is also shrinking as real-time payments mean there are just milliseconds to detect and prevent theft before it’s too late.

QIIB understands the importance of providing solutions that meet its customers’ expectations for convenience while also ensuring that state-of-the-art security is in place, capable of withstanding increasingly sophisticated cyber-attacks and large-scale fraud breaches.

To do so, QIIB selected IBM Safer Payments, an advanced fraud payment prevention solution that allows banks to intercept fraudulent activity before it happens, while ensuring customers’ genuine transactions are not stopped in error. IBM Safer Payments uses artificial intelligence and machine learning to analyze behavior and fraudulent patterns. It then builds and adapts predictive models of emerging fraud threats and recommends countermeasure responses.

“QIIB was built on three pillars: trust, family and commitment. Protecting our customers’ trust in us, is key to our success and we put security front and center, so they can be sure their money and personal information is well-protected. With IBM Safer Payments, we are stepping up this protection, analyzing every transaction in real-time, but without sacrificing the customer experience,” said Dr. Abdulbasit Al Shaibei, CEO of QIIB.

Understanding the whole customer

IBM Safer Payments uses both financial and non-financial data together with a customer’s transaction history, to perform rigorous authentication and profiling every transaction. Fraudulent transactions are quickly identified – allowing them to be stopped, or put on hold pending further validation.

“IBM Safer Payments is also PCI PA-DSS certified,” notes Dr. Hesham Mohameden, Chief Information Security Officer. “This implies that the highest standard of data security and data consistence of the payment industry are maintained and ensures that all development and quality assurance processes are in line with the highest standards as well.”

“Facilitating safe, secure and fast transactions while eliminating friction through technological advancements is at the core of what we strive to offer our customers,” said Mohamed Elsir Haguo, the Head of the Fraud Management Department. “IBM Safer Payments delivers efficiencies and speed in identifying fraudulent activity.”

Building for the future of financial services

Trust and innovation have always been the corner stone for QIIB’s business model as the bank is keen on reinforcing this value through the innovative digital and electronic banking services. QIIB Mobile Banking and QIIB Internet Banking are premium services that provide efficient transactional banking and at a glance account information. With IBM Safer Payments, QIIB will ensure this innovation and trust is maintained.

To learn more, visit https://www.ibm.com/products/financial-crimes-insight/safer-payments

The post Why QIIB trusts IBM Safer Payments for cross-channel fraud prevention appeared first on Journey to AI Blog.



Is Data a Differentiator for Your Business? If So, Traditional OCR Cannot Be An Answer

In this contributed article, Ankur Goyal, CEO and co-founder of Impira, discusses how to make the most of your OCR investment with AutoML. Automated Machine Learning is a nascent AI technology that exposes the power of machine learning (ML) to a much broader audience than data scientists and technologists.



Data: The Defining Language in Logistics Technologies

In this special guest feature, Marc Meyer, CCO at Transmetrics, discusses AI and just-in-time shipping: how tech models offer a competitive advantage for logistic businesses. Emerging AI innovations are a driving factor in defining the usability of data and will be pivotal to the supply chain and all adjacent processes—from dispatch to last-mile.



The State of Data Innovation 2021

Our friends over at Splunk, along with researchers at the Enterprise Strategy Group, set out to measure data innovation, surveying 1,250 senior IT and business decision-makers worldwide, across industries, at larger organizations to assess their data practices, their innovation infrastructure, and their results. This report, “The State of Data Innovation 2021” summarizes the findings.



Securing the open source software supply chain

Cybersecurity incidents are among the greatest threats facing organizations today. In the wake of recent high-profile software supply chain attacks, the US Federal government has taken bold action to strengthen the country’s cyber resilience. On 12 May 2021, President Biden issued a widely anticipated Executive Order on Improving the Nation’s Cybersecurity, which calls for stringent new security guidelines for software sold to the federal government, and has wide-ranging implications that will ripple across the entire software market.

Despite the troubling frequency of malicious attacks, most organizations still have only a partial view of the make-up of their software applications. This partial knowledge leaves them exposed to unknown software component vulnerabilities and hampers any response efforts.

Anaconda asked about open source security in our 2021 State of Data Science survey, and the results were surprising:

  • 87% of respondents said they use open source software in their organization.
  • 25% are not securing their open source pipeline.
  • 20% did not report any knowledge about open source package security.

We also found that in organizations that aren’t using open source software today, the most common barrier to entry is security concerns, including fear of common vulnerabilities and exposures (CVE), potential exposures, or risks. It’s no secret that open source software is key to accelerating the development of new business ideas—not only by saving time, but by allowing greater collaboration and assembling more minds to solve for some of the world’s toughest challenges.  With the increased visibility and involvement from third parties, however, these benefits come with exposure to potential risk. IT departments need solutions that support innovation but also provide governance to mitigate the damage from any attack or exposure.

Providing security and trust in open source

CVE matching and remediation information enables an organization to build a secure supply chain tailored to their unique needs and policies. For example, one foundational cybersecurity practice is to consult CVE databases and scores regularly to guard against the risk of using vulnerable packages and binaries in applications. Anaconda Repository for IBM Cloud Pak® for Data automates this process by allowing IT security administrators to filter access to packages and files against a curated database of known vulnerabilities. This effort-saving feature frees developers and data science teams to focus on building models.

Collaborating to confront risks head-on

The Executive Order includes many additional steps to improve cybersecurity, such as providing a software bill of materials (SBOM) that enables potential software consumers to know exactly how something is developed. These additional steps are essential for mitigating the many malicious cyber campaigns aimed at gathering critical information and disrupting operations across the nation. As society continues to become more and more technologically driven, vulnerabilities are inevitable. However, a spirit of transparency and collaboration—when combined with the right tools—will help enterprises guard against potential breaches and hacks to their systems, so they can continue to innovate and safely collaborate in the open source ecosystem.

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Anaconda Repository for IBM Cloud Pak for Data helps organizations identify vulnerabilities and enables greater control over open source packages in use by allowing admins to block or safelist packages based on IT policies and CVE scores.

 Learn more about Anaconda Repository for IBM Cloud Pak for Data.

The post Securing the open source software supply chain appeared first on Journey to AI Blog.



Urban Institute and IBM help cities measure gentrification

Cities across the United States are increasingly seeing their local communities impacted because of widespread neighborhood change due to blight or to adverse effects of gentrification. While gentrification can often increase the economic value of a neighborhood, the potential threat it poses to local culture, affordability, and demographics has created significant and time-sensitive concerns for city governments across the country. Many attempts to measure neighborhood change up until now have been backwards-looking and rules-based, which can lead governments and community groups to make decisions based on inaccurate and out-of-date information. That’s why IBM partnered with the Washington D.C.-based nonprofit research organization Urban Institute, which for more than 50 years has led an impressive range of research efforts spanning social, racial, economic and climate issues at the federal, state, and local level.

Measuring neighborhood change as or before it occurs is critical for enabling timely policy action to prevent displacement in gentrifying communities, mitigating depopulation and community decline, and encouraging inclusive growth. The Urban Institute team recognized that many previous efforts to measure neighborhood change relied on nationwide administrative datasets, such as the decennial census or American Community Survey (ACS), which are published at considerable time lags. For that reason, the analysis could only be performed after the change happens, and displacement or blight has already occurred. Last year, the Urban Institute worked with experts in the US Department of Housing and Urban Development’s (HUD) Office of Policy Development and Research on a pilot project to assess whether they could use novel real-time HUD USPS address vacancy and Housing Choice Voucher (HCV) data with machine learning methods to accurately now-cast neighborhood change.

Together, the IBM Data Science and AI Elite and Urban Institute team built on that pilot to develop a new method for predicting local neighborhood change from the latest data across multiple sources, using AI. This new approach began by defining four types of neighborhood change: gentrifying, declining, inclusively growing, and unchanging. IBM and Urban Institute then leveraged data from the US Census, Zillow, and the Housing Choice Voucher program to train individual models across eight different metropolitan core based statistical areas, using model explainability techniques to describe the driving factors for gentrification.

The IBM Data Science and AI Elite team is dedicated to empowering organizations with skills, methods, and tools needed to embrace AI adoption. Their support enabled the teams to surface insights from housing and demographic changes across several metropolitan areas in a collaborative environment, speeding up future analyses in different geographies. The new approach demonstrated a marked improvement over the precision of older rules-based techniques  (from 61% to 74%) as well as the accuracy (from 71% to 74%). The results suggest a strong future for the application of data to improving urban development strategies.

The partnership put an emphasis on developing tools that enabled collaborative work and asset production, so that policymakers and community organizations could leverage the resulting approaches and tailor them to their own communities.

IBM Cloud Pak® for Data as a Service was used to easily share assets, such as Jupyter notebooks, between the IBM and Urban Institute teams. During the engagement with Urban Institute, the teams leveraged AutoAI capabilities in Watson Studio to rapidly establish model performance baselines before moving on to more sophisticated approaches. This capability is especially valuable for smaller data science teams looking to automatically build model pipelines and quickly iterate through feasible models and feature selection, which are highly time-consuming tasks in a typical machine learning lifecycle.

Together, this engagement and collaboration aims to empower the field to use publicly available data to provide a near real-time assessment of communities across the country. In addition to providing insights on existing data, the project can help uncover shortcomings in available data, enabling future field studies to fill the gaps more efficiently.

For more details on the results, check out our assets which provide an overview of how the different pieces fit together and how to use them and. And if you want to dig deeper into the methods, read our white paper.

IBM is committed to advancing tech-for-good efforts, dedicating IBM tools and skills to work on the toughest societal challenges. IBM is pleased to showcase a powerful example of how social sector organizations can harness the power of data and AI to address society’s most critical challenges and create impact for global communities at scale. IBM’s Data and AI team will continue to help nonprofit organizations accelerate their mission and impact by applying data science and machine learning approaches to social impact use cases.

Interested in learning more? Discover how other organizations are using IBM Cloud Pak for Data to drive impact in their business and the world.

The post Urban Institute and IBM help cities measure gentrification appeared first on Journey to AI Blog.



BNZ protects customers (and their experience) with IBM Safer Payments

Bank of New Zealand (BNZ), one of the leading banks in ANZ, announced in 2018 that they have selected IBM Safer Payments to deliver cross-channel fraud protection to its customers. The multi-million-dollar deal supports BNZ’s efforts to provide frictionless and safer payments experience to their customers.

Growing fraud requires new approach

Many conveniences that customers enjoy as a result of modern banking carry an increased risk of fraud. Global card fraud losses are on the rise—from 2016 to 2025, they are projected to nearly double, climbing from US $22.8 billion to nearly $50 billion. Mobile banking is particularly vulnerable as 65% of fraudulent transactions were perpetrated through a mobile browser or mobile app according to a recent study.

Legacy systems were designed to see and stop easily recognizable, repetitive fraud patterns, however modern “anytime, anywhere” banking on mobile devices has made fraud detection much more challenging. Banks’ time to respond is also shrinking as real-time payments mean there are just milliseconds to detect and prevent theft before transactions are done.

Protecting customers and the customer experience

BNZ understands the importance of providing solutions that meet its customers’ expectations for convenience while also ensuring that state-of-the-art security is in place, capable of withstanding increasingly sophisticated cyber-attacks and large-scale fraud breaches. To do so, BNZ selected IBM Safer Payments, a modern real-time fraud detection solution that allows banks to intercept fraudulent activity before it happens, while ensuring customers’ genuine transactions are not stopped in error. IBM Safer Payments uses machine learning and artificial intelligence to analyze behavior and fraud patterns, build and adapt models to deter emerging fraud threats, and recommend countermeasure responses.

“We are ruthlessly vigilant in protecting our customers’ trust in us, and we put security front and center, so they can be sure their money and personal information is well-protected. With IBM Safer Payments, we are stepping up this protection, analyzing every transaction in real time, but without sacrificing the customer experience. Everything we do to protect our customers from fraud and cybercrime also helps us contribute to upholding New Zealand’s excellent e-commerce and trading reputation globally.” – Owen Loeffellechner, Chief Safety and Security Officer, BNZ

BNZ looks beyond just transactions for greater context and accuracy

IBM Safer Payments uses both financial and non-financial data together with a customer’s transaction history, to perform rigorous authentication and profiling on each and every transaction. Potentially fraudulent transactions are quickly identified – allowing them to be stopped or put on hold pending further validation. The solution also complies with all credit card scheme rules. “Banks are facing the challenge of needing to adapt to meet their customers’ evolving expectations for a frictionless transaction, while also ensuring their security,” said Mike Smith, Managing Director of IBM New Zealand. “With financial crime becoming increasingly sophisticated, BNZ partnered with IBM to address the rising threat of crime and fraud while still enabling top quality experiences for customers and allowing for future growth.”

With the implementation of IBM Safer Payments, BNZ’s 1.2 million customers are enjoying heightened security.

Find out more at https://www.ibm.com/products/financial-crimes-insight/safer-payments

The post BNZ protects customers (and their experience) with IBM Safer Payments appeared first on Journey to AI Blog.



insideBIGDATA Latest News – 10/20/2021

In this regular column, we’ll bring you all the latest industry news centered around our main topics of focus: big data, data science, machine learning, AI, and deep learning. Our industry is constantly accelerating with new products and services being announced everyday. Fortunately, we’re in close touch with vendors from this vast ecosystem, so we’re in a unique position to inform you about all that’s new and exciting. Our massive industry database is growing all the time so stay tuned for the latest news items describing technology that may make you and your organization more competitive.



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