What if you could use just one platform to detect all types of major financial crimes?
One platform to handle the analytical tasks of fraud detection, including:
Data processing and aggregation
Statistical/mathematical/machine learning modeling
One platform that could successfully reduce complex and time-consuming fraud investigations by combining extremely different domains of knowledge including Business, Economics, Finance, and Law. A platform that can cover payments, credit card transactions, and know your customer (KYC) processes, as well as similar use cases like anti-money laundering (AML), trade surveillance, and crimes such as insurance claims fraud.
Learn more about TIBCO's comprehensive software capabilities behind tackling all these types of fraud in this in depth whitepaper.
Mountains of data promise valuable insights and innovation for businesses that rethink and redesign their system architectures. But companies that don’t re-architect might find themselves scrambling just to keep from being buried in the avalanche of data.
The problem is not just in storing raw data, though. For businesses to stay competitive, they need to quickly and cost-effectively access and process all that data for business insights, research, artificial intelligence (AI), and other uses. Both memory and storage are required to enable this level of processing, and companies struggle to balance high costs against limited capacities and performance constraints.
The challenge is even more daunting because different types of memory and storage are required for different workloads. Furthermore, multiple technologies might be used together to achieve the optimal tradeoff in cost versus performance.
Intel is addressing these challenges with new memory and storage technologies that emp
Component Content Management: A New Paradigm in Intelligent Content Services
While technology has changed the world, the way that companies manage information has inherently stayed the same. The advent of near-ubiquitous connectivity among applications and machines has resulted in a data deluge that will fundamentally alter the landscape of content management. From mobile devices to intelligent machines, the volume and sophistication of data have surpassed the ability of humans to manage it with outdated methods of collection, processing, storage, and analysis. The opportunity afforded by the advent of artificial intelligence (AI) has stimulated the market to search for a better way to capture, classify, and analyze this data in its journey to digital transformation (DX). The paradigm of document-based information management has proven to be a challenge in finding, reusing, protecting, and extracting value from data in real time. Legacy systems may struggle with fragmented information
Published By: CheckMarx
Published Date: Sep 12, 2019
Financial services organizations operate under a host of regulatory standards. This makes sense, as the assets and information managed by these firms are valuable, sensitive, and targeted by sophisticated cyber attackers daily.
Compounding these challenges is the large volume of personally identifiable information (PII) that financial organizations handle regularly. PII is subject to many compliance regulations, notably the General Data Protection Regulation (GDPR), which regulates not only the processing of personal data, including PII, relating to individuals in the EU, for also any organization that processes personal data of EU residents.
For US banking consumers, Section 5 (Unfair or Deceptive Acts or Practices) of the Federal Trade Commission Act and numerous state regulations enforce basic consumer protections, which financial organizations must also uphold.
Continuous member service is an important deliverable for credit unions, and. the continued growth in assets and members means that the impact of downtime is affecting a larger base and is therefore potentially much more costly. Learn how new data protection and recovery technologies are making a huge impact on downtime for credit unions that depend on AIX-hosted applications.
The spatial analytics features of the SAP HANA platform can help you supercharge your business with location-specific data. By analyzing geospatial information, much of which is already present in your enterprise data, SAP HANA helps you pinpoint events, resolve boundaries locate customers and visualize routing. Spatial processing functionality is standard with your full-use SAP HANA licenses.
Your business is changing. As a finance leader, you know that accounting is a labour-intensive, costly process where
systems often don’t allow for expedient exception handling and many days are fraught with difficulty in matching
invoices to other databases for reconciliation. Like most companies, you know where you want to go but may not have
infrastructure or internal expertise to handle electronic fund transfers, credit card payments or cheque processing— all
the pieces required to make your vision for an efficient, integrated operation a reality.
Although data and analytics are highlighted throughout the popular press as well as in trade publications, too many managers think the value of this data processing is limited to a few numerically intensive fields such as science and finance. In fact, big data and the insights that emerge from analyzing it will transform every industry, from “precision farming” to manufacturing and construction. Governments must also be alert to the value of data and analytics as the enabler for smart cities. Institutions that master available data will leap ahead of their less statistically adept competitors through many advantages: finding hidden opportunities for efficiency, using data to become more responsive to clients, and developing entirely new and unanticipated product lines. The average time spent by most companies on the S&P 500 Index has decreased from an average of 60 to 70 years to only 22 years. There are winners and losers in the changes that come with the evolution of both technology
Published By: Cisco EMEA
Published Date: Nov 13, 2017
Big data and analytics is a rapidly expanding field of information technology. Big data incorporates technologies and practices designed to support the collection, storage, and management of a wide variety of data types that are produced at ever increasing rates. Analytics combine statistics, machine learning, and data preprocessing in order to extract valuable information and insights from big data.
In-memory technology—in which entire datasets are pre-loaded into a computer’s random access memory, alleviating the need for shuttling data between memory and disk storage every time a query is initiated—has actually been around for a number of years. However, with the onset of big data, as well as an insatiable thirst for analytics, the industry is taking a second look at this promising approach to speeding up data processing.
Data from The Hackett Group’s most recent Purchase-to-Pay Performance Study shows that organizations with high levels of AP automation save 43% on invoice processing costs. However, top-performing organizations don’t focus solely on process automation.
In midsize and large organizations, critical business processing continues to depend on relational databases including Microsoft® SQL Server. While new tools like Hadoop help businesses analyze oceans of Big Data, conventional relational-database management systems (RDBMS) remain the backbone for online transaction processing (OLTP), online analytic processing (OLAP), and mixed OLTP/OLAP workloads.
What if you could reduce the cost of running Oracle databases and improve database performance at the same time? What would it mean to your enterprise and your IT operations?
Oracle databases play a critical role in many enterprises. They’re the engines that drive critical online transaction (OLTP) and online analytical (OLAP) processing applications, the lifeblood of the business. These databases also create a unique challenge for IT leaders charged with improving productivity and driving new revenue opportunities while simultaneously reducing costs.
The Cisco® Hyperlocation Solution is the industry’s first Wi-Fi network-based location system that can help businesses and users pinpoint a user’s location to within one to three meters, depending on the deployment. Combining innovative RF antenna and module design, faster and more frequent data processing, and a powerful platform for customer engagement, it can help businesses create more personalized and profitable customer experiences.
Amazon Redshift Spectrum—a single service that can be used in conjunction with other Amazon services and products, as well as external tools—is revolutionizing the way data is stored and queried, allowing for more complex analyses and better decision making.
Spectrum allows users to query very large datasets on S3 without having to load them into Amazon Redshift. This helps address the Scalability Dilemma—with Spectrum, data storage can keep growing on S3 and still be processed. By utilizing its own compute power and memory, Spectrum handles the hard work that would normally be done by Amazon Redshift. With this service, users can now scale to accommodate larger amounts of data than the cluster would have been capable of processing with its own resources.
Published By: Oracle CX
Published Date: Oct 20, 2017
With the growing size and importance of information stored in today’s
databases, accessing and using the right information at the right time has
become increasingly critical. Real-time access and analysis of operational
data is key to making faster and better business decisions, providing
enterprises with unique competitive advantages. Running analytics on
operational data has been difficult because operational data is stored in row
format, which is best for online transaction processing (OLTP) databases,
while storing data in column format is much better for analytics processing.
Therefore, companies normally have both an operational database with data
in row format and a separate data warehouse with data in column format,
which leads to reliance on “stale data” for business decisions. With Oracle’s
Database In-Memory and Oracle servers based on the SPARC S7 and
SPARC M7 processors companies can now store data in memory in both
row and data formats, and run analytics on their operatio
Published By: IBM APAC
Published Date: Apr 27, 2018
While relying on x86 servers and Oracle databases to support their stock trading systems, processing rapidly increasing number of transactions fast became a huge challenge for Wanlian Securities. They shifted to IBM FlashSystem that helped them cut average response time for their Oracle Databases from 10 to less than 0.4 milliseconds and improved CPU usage by 15%.
Download this case study now.
As of May 2017, according to a report from The Depository Trust &
Clearing Corporation (DTCC), which provides financial transaction and data processing services for the global financial industry, cloud computing has reached a tipping point1. Today, financial services companies can benefit from the capabilities and cost efficiencies of the cloud. In October of 2016, the Federal Deposit Insurance Corporation (FDIC), the Office of the Comptroller of Currency (OCC) and the Federal Reserve Board (FRB) jointly announced enhanced cyber risk management standards for financial institutions in an Advanced Notice of Proposed Rulemaking (ANPR)2. These proposed standards for enhanced cybersecurity are aimed at protecting the entire financial system, not just the institution. To meet these new standards, financial institutions will require the right cloud-based network security
platform for comprehensive security management, verifiable compliance and governance and active protection of customer data
The purpose of IT backup and recovery systems is to avoid data loss and recover
quickly, thereby minimizing downtime costs. Traditional storage-centric data protection
architectures such as Purpose Built Backup Appliances (PBBAs), and the conventional
backup and restore processing supporting them, are prone to failure on recovery. This
is because the processes, both automated and manual, are too numerous, too complex,
and too difficult to test adequately. In turn this leads to unacceptable levels of failure for
today’s mission critical applications, and a poor foundation for digital transformation
Governments are taking notice. Heightened regulatory compliance requirements have
implications for data recovery processes and are an unwelcome but timely catalyst for
companies to get their recovery houses in order. Onerous malware, such as
ransomware and other cyber attacks increase the imperative for organizations to have
highly granular recovery mechanisms in place that allow
Automation Anywhere’s flagship product is Automation Anywhere Enterprise – a RPA platform offering a variety of tools to help organisations develop, operate and manage RPA bots that automate data entry, data gathering and other repetitive, routine tasks usually carried out as part of high-volume, repetitive work (for example, service fulfilment work in call centres, shared-service centres, and back-office processing environments). Automation Anywhere Enterprise bots can add value both in unattended (server-based, lights-out operation) and attended (desktop-based, interactive) deployment configurations.
In this report, MWD Advisors digs deeper into the features and capabilities of Automation Anywhere’s product portfolio, analysing its fast-growth trajectory and highlighting large-scale implementations.
Published By: Dell EMC
Published Date: Nov 09, 2015
This business-oriented white paper summarizes the wide-ranging benefits of the Hadoop platform, highlights common data processing use cases and explores examples of specific use cases in vertical industries. The information presented here draws on the collective experiences of three leaders in the use of Hadoop technologies—Dell and its partners Cloudera and Intel.
Published By: Dell EMC
Published Date: Oct 08, 2015
Big data can be observed, in a real sense, by computers processing it and often by humans reviewing visualizations created from it. In the past, humans had to reduce the data, often using techniques of statistical sampling, to be able to make sense of it. Now, new big data processing techniques will help us make sense of it without traditional reduction.
Former Intel CEO Andy Grove once coined the phrase, “Technology happens.” As true as Grove’s pat aphorism has become, it’s not always good news. Twenty years ago, no one ever got fired for buying IBM. In the heyday of customer relationship management (CRM), companies bought first and asked questions later.
Nowadays, executives are being enlightened by the promise of big data technologies and the role data plays in the fact-based enterprise. Leaders in business and IT alike are waking up to the reality that – despite the hype around platforms and processing speeds – their companies have failed to established sustained processes and skills around data.
Old Dutch Foods, known for its broad selection of snack foods in the midwest United States and Canada, was struggling to get the right products to the right places at the right time. Its data center included outdated physical servers, and batch processing meant that inventory would not be updated until the end of the day as opposed to real time. In addition, recovering from power outages and disk failures could frequently take up to two weeks.
To modernize its data center, Old Dutch Foods invested in EMC Converged Infrastructure. The fast and easy deployment of two VCE VBlock® systems running JD Edwards, MS Exchange, mobile device apps, and operation of a backup site with replicated applications and data.
This enhanced the IT department's responsiveness to the business, allowed them to shift to real-time inventory, and reduced CapEx and OpEx costs. Operations were simplified by reducing person-hours needed for infrastructure maintenance
by 75 percent.
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