Unleashing end-to-end HR, mobility and payroll services to address talent challenges

IBM’s open ecosystem model is essential to bringing greater value to our clients, allowing us to deliver integrated solutions with speed and scale for true enterprise transformation. This is why the Global EY-IBM Alliance has expanded its focus to address our clients’ biggest challenge for talent: the urgency to attract, retain and upskill their workforce amid evolving employee expectations and a requirement for more engagement and consumer-grade experiences.

The EY-IBM Talent Alliance is not just another two-vendor model. EY and IBM have developed joint offerings and established a new Center of Excellence to address clients’ most pressing HR transformation needs. By combining EY and IBM’s consult-to-operate process and technology expertise, we create a truly unparalleled level of end-to-end services. Let me give you some concrete examples:

◉ For Human Capital Management (HCM) platforms like Workday and Oracle, EY and IBM amplify the value of clients’ HR Cloud Transformation by starting with a digital-first and human-centered design, accelerating implementation by 3x with ready-to-deploy assets, and leveraging data-driven AI to transform employee experience.

◉ For clients struggling with complex regulatory requirements and multi-vendor payroll systems with disparate data input and reporting methods, IBM and EY can provide advisory services and single-sourced payroll operations across 160 countries, all while reducing risk and saving time by leveraging Virtual Agents, pre-built payroll automated digital workers, and a suite of proprietary payroll technology.

◉ For clients looking to reduce operational costs and drive organizational resiliency, EY and IBM offer a holistic consult-to-operate solution for Recruitment Process Outsourcing, Mobility, and HR Outsourcing.

◉ And finally, underpinning the entire portfolio is our unique ability to create differentiated talent-focused technology offerings by combining EY’s assets around mobility and people experience with IBM’s talent AI microservices and deep expertise in platform strategy and hybrid, multi-cloud architecture.

Together we are better equipped to help our clients overcome the urgent need to attract, retain and upskill their workforce while transforming their HR function. The alliance offers an array of solutions including outsourcing to create space and free resources to focus on what matters most, laying the foundation for success with a strong HR Cloud Transformation program or applying AI and Automation technology to transform employee experiences.

And we are not just helping our clients transform; we apply these same principles ourselves. For decades, EY and IBM have partnered to help each other in talent-related challenges, including EY and IBM’s previous collaboration to introduce AI in Recruitment and reimagine employee assistance with the award-winning HR virtual assistant used in-house at both organizations.

Source: ibm.com

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The IBM Research innovations powering IBM z16

From 7 nanometer node chips to built-in AI acceleration and privacy, IBM Research was behind many of the groundbreaking aspects of the new IBM z16 system.

Pictured: Operating an IBM z16 system designed and manufactured in Poughkeepsie, N.Y. From 7 nm node chips to AI acceleration and privacy technology, IBM Research was behind many of z16’s innovations. (Image: courtesy of IBM.)

Today, we're unveiling IBM z16, our next-generation mainframe system, containing several groundbreaking innovations, including the new 7 nm Telum chip that can facilitate on-device AI inferencing that’s 20 times faster than sending an AI request to an x86 server in the cloud, as well as quantum-safe cryptography, multi-cloud support, and data privacy that’s central to the system. Many of those innovations began life in IBM Research.

Scaling to 7 nm

These innovations didn't happen overnight. Back in 2015, IBM Research demonstrated the industry’s first 7 nm test chip with our partners, including Samsung. This 7 nm technology achieved two important milestones: 2.4x logic density and 17.6% frequency gain over 14 nm technology, which was at that time the most advanced technology. This 7 nm chip included the first implementation of Extreme UltraViolet (EUV) lithography technology. We had developed the semiconductor technology that would provide a foundation for better performance and power saving.

Read More: IBM Certifications

This innovation came from our Albany lab, which has since developed into a world-class ecosystem of partners from government, academia and industry. The result of over $15 billion in public and private investment, the Albany Nanotech Complex has continually delivered advances in CMOS logic scaling such as nanosheet architecture, our 2 nm node chip, and most recently vertical-transport FET, or VTFET.

A powerful chip with AI at its core

Our 7 nm node technology served as a foundation for advances in AI hardware, such as our Telum processor. 

Back in August, we unveiled Telum, a new 7 nm CPU chip. It’s IBM’s first commercially available processor to contain on-chip acceleration for AI inferencing, which could have massive impacts on industries like banking, finance, healthcare, and logistics. At the time, we said we believed it to have the potential to be as great of a technological shift as the original IBM zSystem was when it launched. The new IBM z16 system will be the first to ship with Telum onboard.  

The AI core augmenting capabilities for IBM z16 critical workloads is the result of the aggressive roadmap of the IBM Research AI Hardware Center towards sustained innovation for continued efficiency improvement of AI compute resources. The research teams have been working with the IBM Systems teams to integrate the AI core technology from our latest generation chip into the IBM zSystem. 

In traditional computing systems, calculations are usually performed by constantly transferring data between off-chip memory and processors. For AI workloads, though, there’s a far higher computational requirement, as they generally require large amounts of data. The more AI is infused into application workloads, the more critical it is to have an efficient system where both the general-purpose CPU cores and AI cores are tightly integrated on the same chip.

Each Telum chip provides a dedicated AI core alongside the traditional horsepower of eight CPU cores. The CPU cores handle general-purpose software applications, while the AI core is highly efficient for running deep-learning workloads. Each Telum chip contains 22 billion transistors along with 19 miles of wire.

With Telum, it’s possible to detect fraud during the instant of a transaction. It’s possible to determine whether to extend someone a loan as quickly as they applied.

Beyond sheer power, this chip has the potential to revolutionize the way AI is implemented at scale. The chip offers a 20-fold speedup in AI inference over sending the AI request to x86 servers in the cloud. With Telum, it’s possible to detect fraud during the instant of a transaction. It’s possible to determine whether to extend someone a loan as quickly as they applied. 

With past systems, running AI inference on a process would have to happen after the transaction took place. Think about a time your bank notified you that a suspected fraudulent transaction took place on your account: that notification likely came minutes to hours after the fraud happened. By then, the fraudster could have gotten away with money that now needs to be recovered. Being able to detect and prevent fraud  during the moment of a transaction, whether it’s in finance, retail, or myriad other industries, would be a dramatic shift in the way AI is deployed in the modern world. 

But it’s not just about running AI processes more quickly — it’s about the sorts of problems Telum allows you to tackle. An IBM zSystem requires 50% less energy than x86 systems to run the same workload. zSystems can run at 100% utilization, whereas x86 systems run at far lower utilizations.  

Delivering systems that can run AI workloads considerably more efficiently opens up the door for all sorts of new opportunities. Logistics and retail firms can run large-scale inferencing tasks to figure out the places most at risk in their supply chains. Finance houses can determine which trades were most at risk before settlement, and people could find out in an instant, instead of weeks, whether they’ve been approved for a loan.

Companies can also use IBM Research-designed AI software on the IBM z16 to help them find similar instances too; if fraud was just found on one account, you could use SQL Insights which has built in neural networks embedded in Db2 for z/OS to find similar transactions — without having to install, select, tune and configure AI models, or pre-determine what features the AI models should be trained on.

Finance houses can determine which trades were most at risk before settlement, and people could find out in an instant whether they’ve been approved for a loan. 

The IBM z16 system will also offer multi- and hybrid-cloud support through z/OS Container extensions (zCX) for OpenShift, which was co-developed by IBM Research and can be managed as Red Hat OpenShift containers. This will allow for workloads running AI accelerated models to be managed by OpenShift on premises or as part of a multi-cloud setup.

With z/OS container extensions, and support for popular development tools, programmers can build apps for their sites that transparently take advantage of the IBM z16’s hardware. 

Security for today and tomorrow 

IBM Research’s contributions to the new IBM z16 system don’t stop at the hardware innovations or availability of IBM zSystems and services in the cloud. We wanted to ensure that regardless of what these systems are called to do, they can secure the customers’ — and their customers’ — data.  

We designed the IBM z16 to have the highest security out of the box. We provided data privacy for diagnostics that can detect and scrub sensitive data before it goes anywhere outside of the system. Hackers often try to force software and computer systems to stop with errors, as this will force computers to automatically provide diagnostic data, where personal information can be exposed. 

We also provide cryptography hardware that automatically encrypts all data in IBM zSystems’ Hyper Protect databases, virtual machines and containers. Policies can be set to pervasively encrypt z/OS data and Linux on zSystems, via Secure Service containers (on premises) or Hyper Protect (in the cloud).

The IBM z16 includes the IBM Hyper Protect Data Controller, a feature that protects data as it leaves the IBM zSystem. You can set up rules for different data, such as which user has what type of access and how the data should be encrypted. For example, one user can only see part of the data, whereas other users will only get the encrypted version of the data. These rules are enforced even when the data leaves an IBM z16 system, applied wherever the data is sent.  

We’ve also built the IBM z16 system to help with compliance, as we know that 40% of IT departments and business’ time is spent in compliance. If, for example, your system is processing credit card data, you have to abide by the Payment Card Industry Data Security Standards (PCI DSS). Our IBM z16 compliance software checks the components of the zSystem for adherence and provides a compliance dashboard to system administrators.

But it’s not just about helping solve today’s biggest challenges; we want to make sure that IBM z16 can stand up to the biggest threats of tomorrow, too. IBM z16 is the industry’s first quantum-safe system, using and providing quantum-safe encryption, an approach for constructing security protocols that helps protect data and systems against current and future threats. 

Source: research.ibm.com

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Fighting for fairer sentences with data and AI

I’ve always been excited about artificial intelligence and the potential for it to enhance everything – whether that is in the workplace or in society. So when the Call for Code for Racial Justice initiative emerged in 2020 I just felt like I had to get involved in the AI-based projects that were proposed by the Black community in IBM and their allies. Of all the projects the Black community in IBM incubated, I was attracted to Open Sentencing as it had a close connect to the voluntary work I do around community policing. At its core, this solution is about using data to highlight where there are disparities in the judicial system: where individuals may face harsher sentencing purely based on the color of their skin. The initial data we obtained showed that Black people are more likely to be charged with a higher sentence for petty crime than those from other communities. We realized that we would be able to present this data through a dashboard – but where could this information have the biggest impact?

Finding the right end user

We originally thought that judges would be the best recipients for this data when making decisions on the sentences they pass down. However, we realized there are many stages in the criminal justice system before a trial even begins, and it would make sense for us to work earlier in the process, potentially even being able to stop many of these cases from getting to trial.

Public defenders are involved from the outset and can petition to stop cases going to trial, especially if they can make the case that a Black defendant may be prejudiced by the system. So we focused our efforts on building a dashboard specifically for public defenders. We conducted design thinking workshops to get feedback from this group and one thing that came through was the need for a dashboard that was incredibly easy to use and understand, as time is often at a premium.

The Open Sentencing solution

The Open Sentencing model uses AI to detect bias in sentencing. This API-based application uses two trained models: one focused on US Federal sentencing and the other trained on data obtained at the State level. The IBM AI Fairness 360 toolkit is used to identify bias by comparing benchmark data with an individual case that a public defender enters into the system. The Open Sentencing solution then highlights if there is a disparity – say a particularly harsh sentence being proposed for a first-time defendant from the Black community. A public defender can use this data to make the case for a more reasonable sentence or settlement.

Getting hold of the data

To build a dashboard, we needed data on sentencing rates by demographic. One big surprise for us was that although many court houses will publish this data, there is no standardization in the legal system so the format of the data can vary widely at a court level, county level or state level. We had to do a lot of manual work to get the data into a common format so that it could be compared and benchmarked against. This is not a small issue, especially when we think about scaling the solution.

One observation from working on this project is that open standards for data will help bring more technology into the judicial system in a way that will benefit us all. The easier it is for data scientists to get hold of standardized data, the greater the role they can play in fighting for social justice. Learn more about how to make sure you’re standardizing and able to access the right data by reading about a new approach, Data Fabric.

The power of open source teaming

As I said, this project started internally in the summer of 2020. Later that year, it was released as an open source model and published to GitHub. There have been many different contributors along the way and in fact I’m one of the few people that was involved with this project since its inception. There have however been core skills that are useful for us across the board. We’ve needed front-end development, back-end development and data science skills to bring this project to life. We also have the need for someone who can help collect data and organize it into a common format, and a strong project manager and system to keep the team focused.

It has been encouraging for me to know that this project has been the focus of a class at Rensselaer Polytechnic Institute (RPI) who are using the project to learn about open source and its potential for taking on the greatest challenges we face. On that note, I look forward to seeing how this solution could make a difference not just here in the US, but potentially throughout the world.

Source: ibm.com

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AI in retail and the rise of the purpose-driven consumer

That retail has experienced extreme disruption in recent years is beyond questioning. Even before Covid turned the world on its head, headlines about the so-called “retail apocalypse” were near-ubiquitous in the media.

Since then, we’ve seen lockdowns, fluctuating openings and closings, some firms going out of business altogether, celebrations of essential retail workers and a surge in online shopping that brought record profits while yielding more ambiguous results for others. And now, with ongoing supply chain disruption, inflation and a tight labor market, it’s clear that the retail sector still faces substantial challenges.

But these challenges also represent opportunity, and harnessing the power of digital transformation will remain central to every business leader serious about thriving in the post-Covid world. Retail isn’t just big, it’s huge — the National Retail Federation expects sales to grow by as much as 13.5% to an estimated total of $4.56 trillion in the US in 2021.

And while we may not yet be living in the post-Covid era, the outline of what that “next normal” might look like is emerging. New research from NielssenIQ suggests that the widespread availability of vaccines is fueling a “cautious confidence renewal” among shoppers, even while priorities and shopping habits continue to be impacted by the pandemic.

But amidst all this uncertainty just what trends should business leaders be focused on?

The rise of hybrid shopping experiences

Each year analysts pay close attention to retail spending around the holidays, and this year the news was upbeat. Although in December sales dropped by 1.9%, this was offset by overall robust Q4 growth of 17.9% over the same period a year earlier.

As IBM CEO Arvind Krishna suggested in a recent keynote talk at the National Retail Federation (RTF), people appear to have moved from a “just in time” approach to shopping to a “just in case” model — though whether this trend will continue long-term remains to be seen.

If consumer spending patterns are evolving, so too is their relationship to the retail experience. And while the shift to online shopping has been substantial, digital transformation does not signal the end of physical shopping. New research from IBM’s Institute for Business Value (IBV) and the RTF indicates that consumers want a range of experiences beyond simply opening the door and picking up a parcel.

In fact, nearly three in four consumers (72%) report that they still rely on stores as part of their primary buying method. Meanwhile, hybrid retail — including experiences such as curbside shopping, or ordering online and picking up instore — is now the primary buying method for 27%  of consumers.

Strikingly, 36% of Gen Z consumers — so-called “digital natives” — prefer this hybrid model of shopping: the largest share of any age cohort.

Future proofing retail through AI

But while trends show us where we are now and were we might be headed, what can retailers do to future proof their digital transformation strategies?

Here AI represents a powerful opportunity to increase profits and deliver new and improved experiences, with IBM’s Krishna telling the audience at the RTF that we have so far only unlocked 10 percent of the technology’s potential.

We already know that AI can be used to power virtual assistants and automate checkouts. AI-powered logistics management can predict product demand by analyzing historical and location information, and it can get the right products in front of consumers at the right time.

But it’s also important to consider the wider impact of AI. More efficient, automated processes don’t just lead to increased profitability — they also have a human impact. The more that we can get machines to shoulder repetitive time-consuming work, lead to less stressed, more engaged employees and satisfied customers.

The importance of the purpose-driven consumer

Another increasingly important factor for business leaders to consider when pursuing their digital transformation strategies is: what are the broader environmental and social impacts of our actions?

This is not just a matter of satisfying the increasing number of regulatory obligations. Research from the IBV shows that 62% of consumers say they’re willing to change their purchasing habits to reduce environmental impact. Meanwhile “purpose-driven consumers” who seek products and brands that align with their values are on the rise and are now the largest segment of the buying population, representing close to half (44%) of the total. Digital transformation has a central role to play here also: for instance, Heineken recntly teamed with IBM to modernize its integration capabilities in a way that also supports the firm’s environmental and social responsibility initiatives.

The good news is that purpose need not be in conflict with profit. In fact, a recent analysis of business sustainability strategies by the IBV found that between 2018 and the first half of 2021 a select group of “transformational trailblazers” saw an estimated cumulative revenue growth of 51% — a difference of nine percentage points over their next best performing peers.

Meanwhile, according to Gallup, Gen Z and Millennials now make up nearly half (46%) of the full-time workforce in the U.S., and these age groups want to work for companies with ethical leadership. Successfully implementing sustainability strategies may therefore also make firms more attractive to job seekers, and help them overcome the challenges of a tight labor market. Indeed, according to research from PwC, 65% of people worldwide want to work for a company with a social conscience.

Source: ibm.com

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How CDOs create lasting culture change through employee empowerment

Do you remember when you first began to think about data? Long before we learn about the concept, from the moment we are born, we are absorbing, consuming, sorting and organizing data.

Nowadays, not all enterprises use data in a strategic way. Only recently have business leaders had access to much of the important data affecting their enterprises, and the tools to understand it at a granular level.

The growing importance of the CDO

The Chief Data Officer (CDO) role is relatively new to the c-suite. Its evolution into a board-level position is telling, as businesses worldwide come to understand just how much they can benefit from being a data-first organization. Put simply, the goal of the CDO is to help the organization make decisions based on data. But their reach doesn’t stop at the boardroom. Their strategies affect the entire company, making the CDO a powerful agent of change.

As a CDO weaves data decisions into daily workflows, their success is measured along three dimensions:

◉ Growth of the top line

◉ Expansion of the bottom line

◉ Reduction of risk

As CDO at IBM, an essential part is being an evangelist for data literacy and data democratization in service of those dimensions above. Building a data-first culture involves talking a lot about it. So, in a recent episode of the podcast Sunny Side Up, I spoke with Asher Mathew and explained how the most effective way to see success is by transforming your company’s culture to a data-first philosophy.

You can listen to that conversation right here.

So, how does an organization do this in a holistic and lasting way? By building a data-first culture.

Building a data-first culture

Every organization has mountains of data that, when properly analyzed, provide a risk-averse growth strategy unique to the needs of their business or industry. An effective CDO must pay attention to that data, understand how to apply it to advance the company’s business goals, and be prepared to articulate their case at the board level.

Changing an organization can be daunting, which is why it’s crucial to develop a data strategy that aligns with your business strategy. For example, when I joined IBM, I knew their business strategy focused on selling Hybrid Cloud and AI. We thoroughly understood what that journey meant for a consumer, but not so much in terms of an enterprise. In time we recognized we needed to become an AI enterprise, to be in a position to help organizations do the same and infuse AI into their business processes. Therefore, our data strategy became about transforming IBM into an AI enterprise and then showcasing our story to our clients. Of course, you can’t do that successfully without a data-first culture.

To usher in this change, we created a variety of specialized business units to work toward infusing AI throughout the company.

◉ The data standardization and governance unit ensures that all the data we use is fit for purpose.

◉ The adoption unit is fully empowered to work directly with various departments and implement an AI transformation. We recognized that AI needed to be incorporated into our company-wide workflows so the participation of this unit is critical to ensuring that the company’s data and AI platform has full adoption across the company.

◉ The data officer council is made up of members from organizations throughout the company who help validate the direction set by the Chief Data Office and remain responsive to business needs.

Many of IBMs clients look like us: they are complex organizations filled with potential that’s difficult to unlock. Since it’s a challenge to change such complex ecosystems, it is important that IBM be a definitive showcase for the kinds of transformation we can offer to our clients and partners.

The successes of a data-first culture

Let me share some examples of our success. In 2016, we laid down our data foundation to deliver trusted, enterprise-wide datasets and standards. Once we deployed our AI solution along those vectors, we saw a boost in operational efficiency: an over 70% improvement to the average business process cycle time, which is the time a process takes to complete from start to finish.

We had a massive effort around risk mitigation from 2016-2018 to respond to the introduction of GDPR in the EU. More than that, however, it was an opportunity to innovate and develop new services and governance frameworks to facilitate better compliance efforts at scale.

With the creation of our AI Accelerator Team in 2018, we started to focus on revenue growth. The AI Accelerator team takes our internal data and AI transformation and showcases it to our clients to help them drive similar changes within their organizations.

Data literacy is at the core of each unit’s success. This should be the primary goal of any CDO change agent. Data is crucial to everybody’s lives, not just to people in organizations like ours. The public at large needs to understand what can be done with data. The cultural change element of the CDO role is concerned with influencing how data is used from within and being the example, that others can follow. If the CDO does not focus on this, how can they expect anyone else to care?

Above all, we want to empower our people to be data-driven; move at the speed of their insights, to observe the data, act on it and not ask for permission.

You must be prepared to inspire and support your teams to thrive, and more realistically, to fail fast, but learn through that failure. The CDO is in a position to understand all elements of the change, how to navigate challenges, how to learn through failure and how these components transform the culture of the enterprise. Effective communication is critical at the C-suite level, and the data you’re using becomes an integral part of the business strategy.

If you found these ideas intriguing, I invite you to join me in a deeper discussion about exploring data and technology with leading CDOs, CAOs, and CTOs at the upcoming IBM Chief Data Technology Summit Series.

Source: ibm.com

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Redefine cyber resilience with IBM FlashSystem

Today, we’re announcing new data resilience capabilities for the IBM FlashSystem family of all-flash arrays to help you better detect and recover quickly from ransomware and other cyberattacks. We’re also announcing new members of the FlashSystem family with higher levels of performance to help accommodate these new cyber resilience capabilities alongside production workloads.

Cybercrime continues to be a major concern for business. Almost every day we see reports of new attacks. The average cost is $4.24 million, and recovery can take days or weeks. Cyberattacks have both an immediate impact on business but can also have a lasting reputational impact if the business is unavailable for a long time.

How Cyber Vault Can Help Businesses Recover Rapidly

Even with the best cyberattack defense strategy, it’s possible that an attack could bypass those defenses. That’s why it’s essential for businesses to have both defense and recovery strategies in place. Storage plays a central role in recovering from an attack.

IBM Safeguarded Copy, announced last year, automatically creates point-in-time snapshots according to an administrator-defined schedule. These snapshots are designed to be immutable (snapshots cannot be changed) and protected (snapshots cannot be deleted except by specially defined users). These characteristics help protect the snapshots from malware or ransomware and from disgruntled employees. The snapshots can be used to quickly recover production data following an attack.

Recovery from an attack involves three major phases: detection that an attack has occurred, preparing a response to the attack, and recovery from the attack. Each of these phases can take hours or longer, contributing to the overall business impact of an attack.

An offering implemented by IBM Lab Services, IBM FlashSystem Cyber Vault is designed to help speed all phases of this process. Cyber Vault runs continuously and monitors snapshots as they are created by Safeguarded Copy. Using standard database tools and other software, Cyber Vault checks Safeguarded Copy snapshots for corruption. If Cyber Vault finds such changes, that is an immediate sign an attack may be occurring. IBM FlashSystem Cyber Vault is based on a proven solution already used by more than 100 customers worldwide with IBM DS8000 storage.

When preparing a response, knowing the last snapshots with no evidence of an attack speeds determining which snapshot to use. And since Safeguarded Copy snapshots are on the same FlashSystem storage as operational data, recovery is fast using the same snapshot technology. Cyber Vault automation helps speed the process of recovery further. With these advantages, FlashSystem Cyber Vault is designed to help reduce cyberattack recovery time from days to just hours.

IBM FlashSystem Cyber Vault is part of IBM’s comprehensive approach to data resilience: high availability and remote replication for disaster recovery in IBM FlashSystem. Backup, recovery, and copy management using IBM Spectrum Protect Suite. Ultra-low-cost long term storage with physical air gap protection with IBM tape storage. Early attack detection through IBM QRadar and IBM Guardium. And proactive attack protection using IBM Safeguarded Copy.

High Performance Hybrid Cloud Storage Systems

To ensure cyber security does not have to come at the expense of production workload efficiency, IBM is introducing new storage systems with greater performance than previous systems.

Built for growing enterprises needing the highest capability and resilience, IBM FlashSystem 9500 offers twice the performance, connectivity, and capacity of FlashSystem 9200 and up to 50% more cache (3TB). The system supports twice as many (48) high-performance NVMe drives. Likewise,FlashSystem 9500 supports up to forty-eight 32Gbps Fibre Channel ports with planned support for 64Gbps Fibre Channel ports. There’s also an extensive range of Ethernet options, including 100GbE RoCEv2.

Supported drives include new IBM FlashCore Modules (FCM 3) with improved hardware compression capability, Storage Class Memory drives for ultra-low latency workloads, or industry standard NVMe flash drives. FCMs allow 2.3PB effective capacity with DRAID6 per control enclosure and 4.5PB effective capacity with forty-eight 38TB FCMs in a planned future update. These new FCM 3 drives help reduce operational cost with a maximum of 116TB per drive and an impressive 18PB of effective capacity in only 16U of rack space with FlashSystem 9500. FCM 3 drives are self-encrypting and are designed to support FIPS 140-3 Level 2 certification, demonstrating that they meet rigorous security standards as defined by US government.

FlashSystem 9500 also provides rock solid data resilience with numerous safeguards including multi-factor authentication designed to validate users and secure boot to help ensure only IBM authorized software runs on the system. Additionally, IBM FlashSystem family offers two- and three-site replication plus plus configuration options that can include an optional 100% data availability guarantee to help ensure business continuity.

“In our beta testing, FlashSystem 9500 with FlashCore Module compression enabled showed the lowest latency we have seen together with the efficiency benefit of compression. FlashSystem 9500 delivers the most IOPS and throughput of any dual controller system we have tested and even beat some four-controller systems.”

— Technical Storage Leader at a major European Bank.

New IBM FlashSystem 7300 offers about 25% better performance than FlashSystem 7200, supports FCM 3 with improved compression, and supports 100GbE ROCEv2. With 24 NVMe drives, it supports up to 2.2PB effective capacity per control enclosure.

For customers seeking a storage virtualization system, new IBM SAN Volume Controller engines are based on the same technology as IBM FlashSystem 9500 and so deliver about double the performance and connectivity of the previous SVC engine. SAN Volume Controller is designed for storage virtualization and so does not include storage capacity but is capable of virtualizing over 500 different storage systems from IBM and other vendors.

Like all members of the IBM FlashSystem family, these new systems are designed to be simple to use in environments with mixed deployments that may require multiple different systems at the core, cloud, or at the edge. They deliver a common set of comprehensive storage data services using a single software platform provided by IBM Spectrum Virtualize. Hybrid cloud capability consistent with on-prem systems is available for IBM Cloud, AWS, and Microsoft Azure with IBM Spectrum Virtualize for Public Cloud. These systems also form the foundation of IBM Storage as a Service.

Source: ibm.com

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AI can help in the fight against racism

In my role as Open Source Community Manager for the Call for Code for Racial Justice, I oversee a community of developers, data scientists, designers and general problem-solvers all looking to use technology to fight for racial justice. Just like any role, there are challenges I must deal with on a daily basis, but the one thing that has pleasantly surprised me since I started almost a year ago has been the interest and enthusiasm from people all around the world and from different backgrounds who are invested in advancing racial equity using data and artificial intelligence (AI).

The Call for Code for Racial Justice is an initiative external to IBM, so the people I deal with come from big and small organizations from around the globe — yet they all share this common belief and that drives them to give up weekends and work nights building tech for social good.

What is this community building to fight racial injustice?

We currently have seven projects in the Call for Code for Racial Justice. These were originally incubated by the black community inside of IBM as a response to the racial injustice highlighted through the #BlackLivesMatter campaign in 2020. When looking across these projects, you can see that there are certain areas where technology has the greatest opportunity to fight racial bias in society:

◉ Accessing information: When information is dense and difficult to consume, it tends to be hard for people to come together and use it in an effective way. This often happens in the government and policy space, where information can have a significant impact on our lives — especially for underserved communities. Policy related to schools, roads, availability of local shops and resources can often be written in legalese that is hard for people to comprehend. AI can help rectify this. The Legit-Info project utilized Watson Natural Language Understanding to identify titles, summaries, locations and impacts. The results can then be further curated to improve readability and make these meaningful to all members in a community.

◉ Identifying racial bias: Racial bias can creep into all kinds of places — from a police write-up of a crime to technical documentation on a software tool. In some cases, this may be explicit and driven by the bias of the individual writing the document, but in just as many cases, this may be implicit and the result of societal norms carried over from the past. TakeTwo is an API-based tool that can take a document as its input and highlight potential racial bias based on a trained machine learning model. Looking for insights in data is another way to identify racial bias — the Open Sentencing project looks specifically at incarceration rates based on racial demographics to help defense lawyers make the case for black defendants who often face tougher sentences for the same crimes as those committed by people of other races.

Why get involved in building AI solutions to fight racism?

In the case of these open-source projects, community involvement is as important as the technology itself. In a recent survey of community members, many were motivated by a desire for social good. Others were interested in networking and connecting with those sharing similar interests. The development of skills is also a big component — working with industry-leading technology and building skills that they can take into other areas of their lives.

For myself, starting as a contributor and progressing to Community Manager, I’ve experienced all these benefits, but there is another factor that is important when it comes to technology helping with social justice. After earning a post-graduate degree in Mechanical Engineering, I started my career as a product manager for AI products. One thing that has become clear as I have progressed through my career is the need to have the right people in the room when making all kinds of decisions. We need to ensure the AI systems we build are trustworthy. Beyond that, whether it’s the policies that impact communities, the products we build and how we market them or, indeed, almost any facet of our lives, we need proper representation and diversity of thought if we are to realize the dream of creating a more just society. AI has a growing role to play in the fight for social justice, but we can’t rely on it alone.

Get involved with the Call for Code for Racial Justice Projects

We are always looking for new participants in the Call for Code for Racial Justice Projects — find out more about how you can get involved.

Source: ibm.com

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Retain clients with Trustworthy AI in wealth management

2021 sees the need for an acceleration in the transformation of wealth management IT systems. A study from Ernst and Young in 2019 showed that a third of clients plan to switch wealth management providers over the next three years. Insufficient personal attention and advisory capabilities were cited as the main reasons for customers leaving.

The traditional wealth advisor relationship must be considered, and growth in self-service tools will be required to support the newer investment channels. Smarter tools are vital for wealth management companies to enable advisors to create a better service for their clients. Such tools are likely to involve the use of AI and machine learning techniques to maximize information around customer needs, behaviours and data to hyperpersonalize, reinvent the market, and enable investments to fit individual customer circumstances.

Modernisation Pitfalls

Historically, attempts to modernize applications have often failed for wealth management companies. Let’s take a closer look at three main pitfalls which have hindered success along with our recommendations on how to best mitigate these risks:

No user-centric approach for AI

Acceptance of new systems by advisors and customers have failed because systems may not be designed around end user needs. Consequently, AI solutions often fail to create understandable and trustworthy personalization for the customers or the advisor. This results in a lack of confidence in the financial advisor and causes customers to look elsewhere and advisors to fall back on their traditional approach of using their own experience.

Technical challenges with AI applications

Infusing AI into wealth management systems presents many technical implementation challenges. Bespoke solutions are often too cumbersome, difficult to understand, not repeatable and have resulted in higher IT costs and time to implement changes. This is a major barrier to digital modernization, and the inclusion of explainable and usable AI.

Lack of end-to-end AI strategy

Getting value out of AI is not easy. According to MIT Sloan, 40% of organizations making significant investments in AI do not report business gain. Often, companies focus too much on the Data Scientists and clever algorithms in the belief that this alone will bring success, rather than looking at the full end to end process which would deliver the true outcomes they require. Whilst Wealth management organizations have long understood that AI is an essential, they have not followed a proper AI framework.

Tactics to win with AI Best Practices

To combat a lack of trust and confidence in AI, agile methods such as IBM Design Thinking aim to center your focus on user needs. Such methods involve multiple brainstorming sessions at the beginning of the project with client advisors and customers to align AI to the main pain points and system desires for end users.

Prototyping and iterating on these ideas should follow before formulating solutions. When it comes to customer attrition, clients need a smarter system to help prioritize which customers needed attention, and immediate notification when a customer is at high risk of leaving. To truly embrace AI, advisors want a smarter system they can trust – a system which produces AI output they can explain and understand.

The Data Science and AI Elite (DSE) developed machine learning models which identify and provide insight to customers who are at risk for attrition.  Design Thinking helps validate which functionality is most relevant and to address the issues of operational acceptance.

Rather than adopting bespoke, non-repeatable deployment approaches for AI based applications, IBM Cloud Pak for Data addresses challenges by offering a single platform to deploy all AI applications. It offers a wide range of services, including AutoAI to automate the model building approach and Watson Studio to allow for ethical and explainable AI.

The AI Ladder (Figure 1) provides organizations with an understanding of where they are in their AI journey as well as a framework for helping them determine where they need to focus by providing four key areas to consider: how they collect data, organize data, analyze data, and then ultimately infuse AI into their organization.  IBM Cloud Pak for Data standardizes these processes to make AI operational to deliver business outcomes.

Transform your retention strategy to achieve Trustworthy AI:

◉ Use an agile approach to better understand customer’s and user needs, such as IBM Design Thinking

◉ Embrace the AI Ladder for adoption of an end-to-end process for delivering AI applications

◉ Reduce complexity and increase repeatable AI processes by deploying applications on IBM Cloud Pak for Data

Fast-track your journey to AI with IBM Industry Accelerators

Industry Accelerators on IBM Cloud Pak for Data provide tools to help you shorten time-to-value from demonstration to implementation. Learn how these accelerators can help you expedite your business strategy by exploring the new Accelerator Catalog.

For help getting started on your data science project, let our experts assist you. The IBM Data Science and AI Elite (DSE) team works side by side with your team to co-engineer AI solutions and help your business prove value at no cost. Get the skills, methods and tools you need to overcome AI adoption and to solve your business challenges quickly.

Source: ibm.com

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Open innovation with IBM LinuxONE III Express

IBM LinuxONE III Express is designed to be an off-the-shelf Linux server to help get clients up and running quickly. Starting at $135,000 USD*, the single-configuration LinuxONE III Express is designed as a new cost-effective offering for popular workloads, including data serving and hybrid cloud.

Today, we’re excited to let you know that Red Hat Enterprise Linux is available to order bundled with the LinuxONE III Express hardware.

Red Hat Enterprise Linux is the intelligent operating system that is the foundation for the open hybrid cloud and provides the tools needed to deliver critical services and workloads faster and with less effort. Red Hat Enterprise Linux complements LinuxONE to provide a full integrated software and hardware solution.

Organizations have come to rely on IBM Z and LinuxONE systems running Red Hat Enterprise Linux to gain next-level data privacy, hardware and software security, scalability and resiliency and capitalize off of this strong foundation to host their most critical workloads running in hybrid multi-cloud environments.

LinuxONE III Express is bundled with Red Hat Enterprise Linux for IBM Z and LinuxONE with Comprehensive Add-Ons which includes:

◉ Red Hat Enterprise Linux.

◉ Red Hat Enterprise Linux High Availability Add-On for increased uptime.

◉ Red Hat Enterprise Linux Extended Update Support (EUS) Add-On for flexibility in updates and migrations.

◉ Red Hat Smart Management for supported use cases that provide optimization and management of Red Hat Enterprise Linux environments at scale.

◉ Red Hat Insights to continuously analyze platforms and applications to help enterprises better manage hybrid cloud environments

◉ Unlimited virtual guests.

◉ Premium support with unlimited access to Red Hat technical support and 24×7 coverage.

Available since May 25th, LinuxONE III Express features:

◉ Three sizes to fit most workloads.

◉ Improved time-to-value: a single configuration with predictable pricing ensures more rapid delivery and install of the server.

◉ Confidential computing capabilities: Ability for our clients to leverage IBM Cloud Hyper Protect Services for the highest commercial levels of privacy.

For businesses that want to take advantage of open-source technology and hybrid cloud computing solutions, LinuxONE III Express and Red Hat are a logical choice. Learn more about Red Hat, IBM Technology Support Services, and IBM LinuxONE by contacting your IBM or IBM Business Partner sales representative.

Learn more about IBM LinuxONE III Express here.

Source: ibm.com

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From research to contracts, AI is changing the legal services industry

There are many reasons a person chooses to become a lawyer or work in the legal industry, but hours of paperwork every week isn’t one of them. Often legal professionals spend a lot of time trying to find and properly classify information in their complex and siloed filing systems.

To complete the many tasks they are responsible for, paralegals, attorneys, and compliance and contract specialists need the ability to quickly identify relevant information in an overabundance of big data.

That’s where AI comes in. Natural language processing (NLP) is an AI technique that can help legal professionals quickly surface insights across millions of unstructured data sources, like printed books, legal websites, commercial databases and historical case files. By augmenting manual processes with AI, paralegals and attorneys can focus on more rewarding, higher-level tasks like working with clients.

Let’s explore a few of the major responsibilities of legal professionals and the top AI use cases in the legal field.

Legal research and drafting

While making legal decisions, attorneys and their teams spend time sorting through documents and running database searches to locate and review relevant statutes, laws and precedents. Not only is this a time-consuming and detail-oriented process, but if the correct keywords aren’t used, important resources may not even surface. Legal teams can use AI search tools like NLP to pull up information quickly, identify emerging trends and reveal hidden connections that help perform billable work faster.

Paralegals and attorneys can also use AI to help ensure all relevant facts, laws and statutes are included in legal documentation and follow the tedious standard formatting rules. AI solution provider LegalMation created a domain-specific model focused on legal terminology and concepts. The LegalMation platform helps legal teams craft early-phase response documentation in under two minutes.

Contract lifecycle management

Legal organizations create, update and store a large volume of contracts throughout their entire lifecycle. From initial drafting and negotiations to compliance management, maintaining these hundreds and sometimes millions of contracts — often stored across multiple repositories — represents a huge investment.

To ease the workload of contract review, legal professionals can use AI to help quickly identify and surface contracts in need of renewal before they expire. Teams can also use AI to help minimize the negotiation time frame by suggesting standard updates and renewal opportunities during protracted negotiations.

Legal technology firm ContractPodAi offers an end-to-end contract management solution that can analyze inventories of over 400,000 contracts. Designed to help counsel easily and cost-effectively manage any contract throughout its lifecycle, ContractPodAi clients report over 50% reductions in contract renewal time.

Client service

Law firms are experimenting with digital subscription services, which provide fast, affordable online legal services that can help reduce operational costs and empower teams to serve additional smaller clients without sacrificing quality. Teams can use AI-assisted customer service to offer clients a faster way to get common questions answered automatically. For example, teams can deploy AI-powered chatbots equipped with search capabilities that can surface relevant data, present it to customers and perform other tedious tasks, enabling legal teams to focus on higher-level work.

Affordable legal services provider QNC GmbH built its “digital law firm” subscription service Prime Legal to provide fast, affordable, flat-rate online legal services to small businesses in Germany. Lawyers can now match client questions against the Prime Legal database of 180,000 previously answered questions and typically respond to client inquiries in less than an hour.

Source: ibm.com

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With AI, capital markets firms will spend less time wrangling data and more time serving clients

Institutional firms invest heavily in technology that helps employees quickly process information and share insights with clients. Over the past decade, these firms and fintech companies have competed for market share by delivering the most client-centric financial services through innovative means. As a result, organizations that were previously less interested in taking full advantage of artificial intelligence (AI) and machine learning solutions are reconsidering their approach to data and analytics, accelerating AI adoption, and strengthening their technology partnerships.

Unfortunately, finding and processing data is a challenge for capital markets organizations.

◉ 46% of financial executives say they and their teams are unable to fully execute their responsibilities. 

◉ 49% say that this is due to “manual, time-consuming processes. 

◉ 21% attribute this to an “inability to readily access required data.”

Financial services providers can use AI implementations across many processes to conquer these challenges with automation.

Finding relevant information in the era of big data

Big data enables capital markets firms to help employees make better financial decisions on behalf of clients. But finding relevant data points in hundreds of documents is time-consuming, since this is unstructured data and resides in formats that are difficult to analyze using traditional analytics tools.

Sound financial decision-making is informed by the latest reports on market segments and client data. With AI-powered tools like natural language processing (NLP), employees can quickly find insights in unstructured data by using AI to recognize patterns across millions of documents. This approach can also surface meaningful insights that might have previously gone unnoticed.

NLP enabled fintech company EquBot and ETF Managers Group to build the AIEQ, the world’s first AI-powered equity ETF (exchange-traded fund). AIEQ collects and parses data on over 6,000 US companies each day, including unstructured data stored in formats that are difficult for analysts to inspect quickly. This includes posts on blogs and social media, like the ones that drove up video game store GameStop’s stock price in early 2021.

Managing regulations

Working in such a heavily regulated industry, risk and compliance professionals need to adapt to changes that impact their investments in real time. But the pace of change makes it challenging to maintain compliance.

Financial institutions can use AI to help comply with ever-changing regulatory frameworks by automating document processing for regulatory notices, consultation papers, policy statements, and more. Employees can use AI solutions to scan these documents and produce a consolidated view of rules applicable to their firm, depending on specific characteristics.

Buy-side opportunities

On the buy-side of capital markets, organizations can integrate financial services AI across research workflows, using the solution to help scan for investment opportunities, analyze sales-side research and reports, process confidential client information, and identify actionable client insights. From there, analysts can use AI recommendations when drafting research for portfolio managers, suggesting investment strategies, and streamlining meetings and quarterly reviews. Firms can also use AI to help anticipate the needs and behaviors of their customers and use those insights to provide customers with automated, client-centric customer service experiences.

Sell-side opportunities

To help monitor markets, firms can use AI to automate the tracking of companies of interest and financial news feeds. With machine learning algorithms, analysts can look at the unstructured data of a potential investment — founders’ backgrounds, total money raised and old deals — and synthesize that information to gauge the potential ROI of an opportunity. Employees can also use AI to assist deal idea generation by creating a list of potential private equity firms to work with or by analyzing past private equity firm deals. On the buy-side, teams can use AI to gather data about companies in the research phase. AI can even augment pitch development.

Source: ibm.com

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IBM Z and the Open Neural Network Exchange

Nearly everyone recognizes the profound opportunity to bring new insights and better decisions to business workloads using AI and analytics. Enabling AI on IBM Z and LinuxONE is a key focus for IBM,  allowing clients to have a reliable, secured, and high performing environment for delivering critical business insights using Machine Learning and Deep Learning applications.

However, with this opportunity there are also challenges, especially those around deploying AI in a production environment. The use of AI in critical business workloads is a growing space, and as with other new technologies, the path to production can be challenging. Key challenges include the need to deploy data science assets without sacrificing production qualities of service (i.e., meet response time goals) in a consistent, repeatable manner.

That is where the Open Neural Network Exchange (ONNX) comes in. ONNX is an open-source format used to represent machine learning models and is one of the key ecosystem technologies enabling a “Build and Train Anywhere, Deploy on IBM Z” strategy. ONNX helps establish a streamlined path to take a project from inception to production. Models represented in a standard ONNX format can then be implemented by an ONNX backend (i.e., runtime or model compiler), such as on IBM Z.

This journey to production starts with the data scientist, who may use a preferred set of tools to understand a business problem and analyze data. When that data scientist creates and trains a model, they build assets that ultimately need to be deployed in production. Often, however, the deployment platform and production requirements aren’t considered heavily in these early stages. This is where utilizing ONNX in a deployment strategy really shines. Many of the most popular libraries and frameworks, including PyTorch and TensorFlow, support the ability to export or convert a trained model to an ONNX format.

Once a model has an ONNX representation, it can be deployed to run on any platform with an ONNX runtime. This provides several key benefits: the model is now portable, with no runtime dependencies on the libraries or framework it was trained on. For example, an ONNX model that was originally created and trained in TensorFlow can be served without the TensorFlow runtime. Additionally, ONNX allows vendors to create high performing model backends that can optimize and accelerate the model for a specific architecture.

For IBM Z and the mission critical workloads it typically hosts, this combination of portability and optimization makes IBM Z an optimal environment for deploying models. One key example of the use of ONNX is in Watson Machine Learning for z/OS (WMLz), which incorporates an ONNX model compiler technology based on the ONNX-MLIR project. The ONNX model compiler feature of WMLz is focused on deep learning models and produces an executable optimized to run on IBM Z. WMLz allows the user to easily deploy this compiled ONNX model for model serving.

As IBM Z continues to innovate in enterprise AI, ONNX is a key part of IBM’s AI strategy. It allows IBM to build a deployment strategy optimized for the IBM Z architecture, while staying closely aligned with the broader ecosystem.

In August, you may have read that IBM previewed Telum, the next generation IBM Z processor. IBM is now examining opportunities to exploit the on-chip AI accelerator with the ONNX model compiler.

ONNX is part of the Linux Foundation and has widespread support from numerous key vendors that recognize the value it delivers. IBM is an early adopter of ONNX and contributes upstream to the ONNX project.

Be on the lookout for additional updates on how you can leverage ONNX as part of your IBM Z AI story!

Source: ibm.com

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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.

Source: ibm.com

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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.

Source: ibm.com

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