For banks, this changes the question entirely. The objective is no longer limited to digitization, cost optimization, or mobile channel enhancement. The real priority is resilience: how to continue serving customers, protecting trust, managing operational risk, and creating growth even when the external environment becomes volatile.

This is where technology, data, and artificial intelligence move from being innovation topics to becoming core strategic instruments. The banks that will lead over the coming years will not necessarily be those with the largest branch networks or the broadest product portfolios. They will be the ones capable of sensing change faster, responding in real time, and reconfiguring customer and operational journeys with speed and intelligence.

Why the current crisis requires a different banking response

Historically, many banks in the region have been optimized for relatively stable operating models. Processes were built around predictable demand cycles, centralized approvals, siloed systems, and gradual change. Even digital transformation programs often focused more on launching channels than rethinking the bank’s underlying decision and execution model. That approach becomes fragile during periods of instability. In a stressed environment, banks can face sudden surges in service demand, rapid shifts in customer behavior, increased support volumes, heightened fraud attempts, liquidity sensitivity, and reputational exposure amplified by digital channels. Customers expect instant reassurance, instant service, and clear communication. Regulators expect stronger controls, clearer auditability, and resilient continuity. Executive teams need visibility in hours, not weeks. In such moments, traditional architectures and operating models show their limits. Batch reporting is too slow. Manual processes become bottlenecks. Disconnected channels create inconsistent customer experiences. Static segmentation fails to reflect changing customer realities. Even well-designed mobile apps lose value if the bank behind them cannot adapt fast enough. The lesson is clear: banks do not simply need more digital capabilities. They need adaptive banking capabilities.

From digital banking to adaptive banking

Adaptive banking is the ability of a financial institution to sense change in real time, make informed decisions quickly, and translate those decisions into immediate action across channels, operations, and customer journeys. It is where digital, data, automation, and AI come together to make the bank more responsive under pressure. This shift is especially relevant in the Gulf and Middle East, where banking institutions often operate across multiple customer segments, languages, markets, regulatory expectations, and economic conditions. The winning model is not a rigid one-size-fits-all platform. It is a composable, orchestrated, intelligence-enabled model that allows banks to adapt without rebuilding everything every time the environment shifts.

The five strategic roles of technology and AI during crisis conditions

1. Strengthening operational resilience

The first responsibility of a bank in a crisis is continuity. Customers must still be able to log in, transfer money, access cards, receive support, and trust that the institution is fully in control. Technology architecture therefore becomes a business resilience issue, not just a technical concern. Modern banks need scalable, cloud-ready, service-based platforms that can absorb sudden spikes in activity, isolate failures, and recover quickly. Microservices, API-led integration, workflow orchestration, real-time monitoring, and event-driven processing are not simply modern design choices. They are enablers of continuity. A resilient digital banking platform should allow the bank to scale critical services independently, reroute processes when dependencies fail, monitor incidents in real time, and deploy changes without destabilizing the wider ecosystem. In practice, this means the bank becomes less dependent on fragile monolithic release cycles and more capable of controlled adaptation.

2. Improving decision-making speed through real-time data and AI

During periods of uncertainty, the value of information declines rapidly with time. Weekly dashboards are too late. Delayed management reports are too late. Even next-day views may be insufficient. Leadership teams require immediate visibility into customer behavior, channel usage, service bottlenecks, deposit patterns, support demand, risk signals, and operational anomalies. This is where AI and advanced analytics can materially reduce crisis impact. By combining transactional data, behavioral signals, support interactions, product activity, and contextual indicators, banks can move from descriptive reporting to predictive and prescriptive decisioning. For example, AI can help detect unusual transaction behavior, flag customers at higher attrition risk, predict support surges, prioritize collections strategies, optimize communication timing, and identify operational weak points before they become visible at board level. The critical advantage is not just insight. It is speed. Faster insight enables faster intervention, which reduces downstream damage.

3. Protecting customer trust through personalization and proactive engagement

In times of crisis, trust becomes one of the bank’s most valuable assets. Customers do not judge a bank only by product pricing or feature depth. They judge it by whether it feels present, responsive, transparent, and helpful when uncertainty rises. Technology allows banks to deliver this trust at scale. AI-powered personalization can help banks shift from generic mass communication to context-aware engagement. Instead of sending static campaigns, the bank can identify which customers may need reassurance, financial guidance, deferred payment options, alternative transaction routes, or targeted offers aligned with their current behavior and risk profile. For marketing and customer experience leaders, this is a major strategic opportunity. The role of marketing in banking is expanding from acquisition and campaigns toward relationship management, trust management, and intelligent lifecycle orchestration. During periods of instability, the institutions that communicate clearly, personally, and helpfully are the ones that preserve wallet share and long-term loyalty.

4. Reducing manual dependency through intelligent workflow automation

Crisis conditions expose process friction very quickly. Activities that were manageable under normal volumes become operational liabilities when demand spikes. Manual approvals, slow exception handling, fragmented onboarding, disconnected servicing flows, and rigid back-office dependencies all create delays precisely when speed matters most. Automation is therefore not just about cost efficiency. It is about removing operational drag. Intelligent workflow orchestration, dynamic business rules, straight-through processing, AI-assisted case handling, and configurable digital journeys allow banks to react faster without compromising governance. This matters across onboarding, service requests, complaints, card controls, limit changes, lending workflows, fraud handling, collections, and internal approvals. The more the bank can standardize and automate the predictable, the more its people can focus on the exceptions that genuinely require judgment.

5. Enhancing security, fraud response, and compliance readiness

Periods of uncertainty often create ideal conditions for bad actors. Fraud attempts increase, phishing becomes more sophisticated, suspicious patterns become harder to isolate, and regulatory scrutiny intensifies. Banks need security and compliance capabilities that are not only robust, but also adaptive. AI can materially improve detection capabilities by identifying anomalies that static rule sets may miss. Behavioral intelligence can strengthen fraud monitoring. Integrated case management can speed up investigation workflows. Centralized audit trails can improve regulatory defensibility. Automation can help enforce policies consistently across channels and journeys. For technology executives, the implication is clear: security can no longer sit at the edge of the system. It must be embedded in architecture, workflows, data design, and customer interaction models.

What banking executives should prioritize now

For CEOs, boards, and business leaders, the current environment requires a more integrated transformation agenda. The conversation should move beyond “do we have a mobile app?” or “have we launched AI?” and focus instead on higher-value questions. Can the bank identify and respond to customer stress patterns in real time? Can it reconfigure journeys without long delivery cycles? Can it scale critical services independently? Can it use data intelligently to preserve trust and reduce risk? Can it modernize without creating even more fragmentation? The right response is not a collection of disconnected technology projects. It is a platform strategy.

What marketing leaders should prioritize now

For chief marketing officers, customer experience leaders, and heads of digital engagement, the current crisis is a reminder that brand strength in banking is inseparable from service quality and timing. Customers do not experience the bank through internal org charts. They experience it as a single relationship. That means marketing teams need better access to real-time signals, tighter integration with servicing and product teams, and stronger orchestration capabilities across channels. The most effective banking communication strategies in uncertain periods are not the loudest. They are the most relevant. The goal is to anticipate customer concerns before they become complaints, and to deliver guidance that feels timely, useful, and personalized. AI can support this by refining segmentation, predicting intent, optimizing communication timing, improving message relevance, and linking engagement directly to operational outcomes. Done well, marketing becomes not only a growth function, but also a stabilizing force.

What technology executives should prioritize now

For CIOs, CTOs, chief digital officers, enterprise architects, and engineering leaders, the message is even more direct: architecture now has direct business consequences. The difference between a rigid platform and a composable one can determine how well the bank survives stress. Technology leaders should therefore focus on eliminating bottlenecks, decoupling high-risk dependencies, improving observability, strengthening integration layers, and enabling real-time orchestration. They should prioritize platforms that are modular, API-first, workflow-driven, secure by design, and ready to integrate AI services without major rework. Equally important, AI should not sit in isolation as an experimental layer. It should be embedded where business value is created: in journeys, recommendations, alerts, support tools, risk signals, fraud detection, and service optimization.

Where a modern banking platform becomes critical

This is exactly why the role of the digital banking platform has changed. It is no longer just the channel layer through which customers view balances and perform transactions. It has become the bank’s execution layer: the environment where journeys, integrations, decisioning, personalization, workflow logic, and experience all come together. A modern platform should help the bank launch and adapt journeys quickly, integrate with core banking and enterprise systems cleanly, orchestrate end-to-end processes, expose services securely across channels, and provide the flexibility needed to embed data and AI into daily operations. This is where platforms such as SoftGrad’s Digital Banking Platform fit strategically into the picture. Rather than treating digital banking as a front-end application problem, the platform approach aligns experience, orchestration, integration, scalability, and intelligence in one execution model.

For banks operating in the Gulf and Middle East, this matters because the environment demands both speed and control. Institutions need to move fast without introducing chaos. They need configurable customer journeys, omnichannel consistency, strong integration capabilities, workflow intelligence, robust security, and an architecture capable of evolving with the bank’s priorities. In practical terms, this means enabling banks to build not only better digital channels, but better digital operating capability. It means making it easier to launch products, automate flows, personalize experiences, integrate services, and gradually introduce AI into the core of banking delivery.

The next frontier: AI-enabled banking operating models

Looking ahead, the most successful banks in the region will go beyond isolated AI use cases. They will adopt AI-enabled operating models. In these models, data is captured continuously, decisions are enhanced algorithmically, workflows adapt dynamically, and customer interactions become more contextual and predictive. This does not mean removing human judgment. On the contrary, it means allowing leadership and frontline teams to focus their judgment where it matters most by automating the routine, surfacing the risks, and guiding the next best action. Banks that succeed in this transition will be able to operate with greater clarity during disruption, greater relevance in customer interaction, and greater efficiency in internal execution. They will be better positioned not only to withstand crisis, but to grow through it.

Conclusion

The current crisis in the Gulf and Middle East is a stress test for the banking sector, but it is also a catalyst. It is exposing which institutions are still operating through fragmented systems and delayed decision cycles, and which ones are building adaptive, intelligent, platform-based capabilities. Technology and AI are no longer optional enablers at the edge of the bank. They are becoming central to resilience, trust, and competitiveness. Banks that invest wisely now in real-time data, intelligent automation, personalized engagement, resilient architecture, and scalable digital platforms will not simply reduce the impact of instability. They will redefine how banking performs under pressure. For decision-makers across business, marketing, and technology, the priority is therefore not just transformation for its own sake. It is building a bank that can sense faster, decide smarter, act quicker, and adapt continuously.

That is what the next generation of banking in the region will require.

About SoftGrad

SoftGrad helps financial institutions design and deliver enterprise-grade digital banking experiences through modern, scalable, and AI-ready platforms. By combining strong product thinking, deep engineering capability, orchestration-driven architecture, and advanced digital channel expertise, SoftGrad enables banks to build resilient, customer-centric ecosystems ready for the demands of today and tomorrow.

The post How banks in the Gulf and Middle East can use technology and AI to minimize the impact of today’s regional crisis appeared first on SoftGrad.

Supply chains break. Payments stall. Teams scatter. Markets shrink. Fear grows.

But something unprecedented is happening today, something that didn’t exist in past conflicts.

For the first time in history, entire economies can continue to function… without physical proximity.

This is not theory. This is the power of AI, cloud infrastructure, and borderless digital ecosystems.

The Shift: From Geography to Connectivity

Traditionally, economic resilience depended on geography, proximity to ports, infrastructure, and stable regions.

Today, resilience depends on something else entirely: connectivity and digital independence.

A company in Beirut can operate from Riyadh.
A development team in Amman can support a bank in Dubai.
A startup in Cairo can scale globally, even during regional instability.

War disrupts land. Technology bypasses it.

The Data Is Clear: Digital Economies Are More Resilient

This is not just a vision, it is backed by global data.

Across multiple crises, digitally mature economies and organizations have consistently outperformed their peers.

📊 1. Digital leaders recover faster

According to global consulting research, companies with advanced digital capabilities achieved 2 to 3 times faster recovery rates during major disruptions compared to less digitized organizations.

📊 2. Cloud-enabled businesses reduce downtime dramatically

Organizations leveraging multi-cloud and distributed infrastructure reported up to 70% reduction in operational downtime during regional or infrastructure-related disruptions.

📊 3. AI-driven companies outperform financially

Businesses that actively integrate AI into their operations see an average of 20–30% improvement in operational efficiency and significantly higher resilience under stress conditions.

📊 4. Cross-border digital services are accelerating

Digital services exports from the Middle East and emerging markets have been growing at over 2x the rate of traditional trade, signaling a structural shift away from geography-dependent economies.

📊 5. Remote-capable operations are now a necessity

More than 60% of companies globally now consider remote and distributed operating models as critical to their business continuity strategies.

What This Means for the Arab Region

The implication is powerful:

Resilience is no longer about avoiding disruption, it’s about designing systems that operate despite it.

In a region frequently exposed to geopolitical tension, this shift is not optional.

It is a strategic necessity.

The New Opportunity: A Digitally United Arab Economy

What if crisis could trigger collaboration instead of isolation?

The Arab world holds a unique advantage:

• Shared language
• Aligned cultural context
• Overlapping business ecosystems
• Rapidly growing digital infrastructure

Yet, these markets still operate in silos.

Today’s tensions present a new question:

What if Arab countries started functioning as a distributed digital economy?

Not politically unified, but technologically interconnected.

How AI Becomes the Backbone of Resilience

Artificial Intelligence is no longer just a productivity tool. It is becoming the infrastructure of continuity.

Here’s how:

1. Intelligent workload distribution

AI systems can dynamically shift operations across countries based on risk, availability, and performance, ensuring uninterrupted service delivery.

2. Smart customer engagement during crises

AI-driven platforms can adapt communication, offers, and services in real-time based on user sentiment, location, and risk exposure.

3. Autonomous operations

From customer onboarding to financial transactions, AI enables processes to continue with minimal human dependency.

4. Predictive risk management

AI can anticipate disruptions before they happen, rerouting operations, resources, and infrastructure accordingly.

The Role of Cloud and Infra-Agnostic Architecture

Technology alone is not enough. Architecture matters.

In a volatile region, dependency on a single infrastructure, vendor, or geography is a risk.

The future belongs to infra-agnostic platforms:

• Multi-cloud deployments across regions
• Decoupled microservices architectures
• Data replication across jurisdictions
• Zero-downtime failover strategies

In simple terms: systems that can “move” when the world around them becomes unstable.

From Survival to Strategic Advantage

Companies that adopt this model won’t just survive crises, they will outperform competitors.

Why?

• They remain operational while others pause
• They gain market share during instability
• They build trust through reliability
• They attract regional partnerships

Resilience becomes a competitive advantage.

A Call for Regional Collaboration

This is not just a technology shift. It’s a mindset shift.

Instead of reacting individually, Arab countries and businesses can:

• Share digital infrastructure strategies
• Enable cross-border service delivery
• Collaborate on AI-driven platforms
• Create regional digital alliances

In a fragmented physical world, unity can be built digitally.

The Silent Enabler

Behind every resilient system is a partner who understands both technology and uncertainty.

Not just how to build platforms, but how to design them for instability, scale, and continuity.

Organizations that embrace AI-driven, infra-agnostic, and resilient architectures today will define the region’s economic future tomorrow.

Final Thought

Wars will always test economies.

But this time, the outcome is not solely determined by geography, politics, or resources.

It will be determined by who can adapt fastest, and who can stay connected when everything else disconnects.

Because when borders close… code doesn’t.

About SoftGrad

In a region where uncertainty is not an exception but a recurring reality, the ability to design systems that adapt, scale, and survive disruption is no longer optional.

SoftGrad operates at the intersection of AI, digital platforms, and resilient architecture, helping organizations across the Middle East build solutions that are not tied to a single geography, infrastructure, or operating model.

By leveraging infra-agnostic development, cloud-native architectures, and AI-driven capabilities, SoftGrad enables businesses to:

• Maintain continuity across multiple regions
• Scale operations dynamically based on demand and risk
• Deliver seamless digital experiences even during instability
• Future-proof their platforms against geopolitical and operational disruptions

From digital banking platforms to large-scale consumer applications handling millions of users, SoftGrad’s approach is built on one core principle:

Technology should not just power growth, it should guarantee resilience.

In a world where disruption is inevitable, SoftGrad helps organizations stay connected, operational, and ahead, no matter the circumstances.

The post When Borders Close, Code Connects: How AI Can Keep the Arab Economy Alive During War appeared first on SoftGrad.

The visit was led by Edgard Hanna, CEO and Co-founder of SoftGrad, and Ziad Tawk, CTO and Co-founder, who guided the LebHub founders through SoftGrad’s AI & Innovation Labs, development floors, and SoftGrad Academy, a dedicated program for nurturing Lebanese digital talent.

The gathering served as a platform for meaningful dialogue around the most pressing challenges in Lebanon’s tech sector, from brain drain and funding gaps to the alignment of academic curricula with real-world innovation needs. The leaders shared a mutual commitment to strengthening Lebanon’s role as a regional hub for artificial intelligence and advanced technologies, fueled by the country’s strong foundation in mathematics, statistics, and computer science.

“Welcoming the founders of LebHub was more than an exchange of ideas, it was a meeting of missions,” said Edgard Hanna, SoftGrad CEO. “We are united in our belief that Lebanon can lead the AI revolution in the region by investing in its youth and connecting them to real-world opportunities.”

Ziad Tawk, CTO, added: “This visit is a clear step forward in building a sustainable, innovation-led tech ecosystem. Together, we can retain and empower Lebanese talent while exporting digital excellence globally.”

The visit concluded with a shared roadmap to enhance collaboration, accelerate entrepreneurship, and position Lebanon as a global provider of AI and software engineering talent.

The post SoftGrad Welcomes LebHub Founders for Strategic Visit to Innovation Labs in Adma appeared first on SoftGrad.

SoftGrad is proud to announce the launch of its highly anticipated AI and Data Analytics Academy, an initiative aimed at empowering the next generation of AI and Data Analytics professionals in Lebanon and the MENA region. The academy welcomed 35 exceptional candidates from 15 prestigious universities across the region, marking the beginning of an intensive six-week program designed to equip participants with cutting-edge skills, knowledge and experience in AI and Data Analytics.

 A New Era of Learning and Innovation

The AI and Data Analytics Academy represents SoftGrad’s commitment to fostering innovation and building a strong talent pipeline in the fields of Artificial Intelligence and Data Analytics. Over the next six weeks, selected candidates will immerse themselves in a comprehensive curriculum that covers everything from foundational AI concepts to advanced Data Analytics techniques. The program includes hands-on projects, interactive workshops, and guest lectures from industry experts, ensuring that participants gain practical experience and insights into real-world applications.

Diverse and Talented Cohort

The 35 candidates selected for this program come from a diverse range of academic backgrounds and institutions, reflecting the rich talent pool within Lebanon and the MENA region. The participating universities include some of the most reputable educational institutions known for their academic excellence and commitment to research and innovation. This diverse cohort brings together a wealth of perspectives and ideas, encouraging a collaborative learning environment that will undoubtedly lead to innovative solutions and breakthroughs.

Objectives of the Academy

The primary goal of the AI and Data Analytics Academy is to empower participants with the skills and knowledge needed to excel in the rapidly evolving fields of AI and Data Analytics. By the end of the program, participants will have:

• Practical Skills: Mastery of various AI and Data Analytics tools and techniques, enabling them to tackle complex problems and drive data-driven decision-making within their organizations.
• Innovative Thinking: The ability to think creatively and develop innovative solutions to real-world challenges, leveraging the power of AI and Data Analytics.
• Collaborative Mindset: Enhanced teamwork and collaboration skills, as participants work together on projects and share insights and experiences.
• Industry Insights: Exposure to industry best practices and trends through guest lectures and mentorship from experienced professionals.

A Commitment to Excellence

SoftGrad is dedicated to ensuring the success of this program and providing participants with an unparalleled learning experience. The academy’s curriculum is meticulously designed to be both rigorous and engaging, offering a blend of theoretical knowledge and practical application.

We are thrilled to launch the AI and Data Analytics Academy and welcome such a talented and diverse group of candidates,” said Dr. Elie Badine, Head of AI and Data Science at SoftGrad. We are confident that the participants will gain invaluable skills and insights that will propel them to become leaders and innovators in their respective fields.”

As the AI and Data Analytics Academy progresses, SoftGrad will share updates and highlights from the program, showcasing the achievements and milestones of the participants. The company invites stakeholders, partners, and the broader community to join in celebrating this exciting journey and supporting the development of future leaders in AI and Data Analytics.

For more information about the AI and Data Analytics Academy and SoftGrad’s initiatives, please visit SoftGrad.net or contact info@SoftGrad.net.

The post SoftGrad Launches Groundbreaking AI and Data Analytics Academy for Lebanese Talent appeared first on SoftGrad.

Enter AI, a powerful tool that holds the potential to optimize operations, improve patient experiences, refine diagnostics, and pave the way for personalized treatment plans. 

Studies by the Arab Artificial Intelligence Council (AAIC) estimate that AI could add $200 billion to the combined GDP of Arab countries by 2030, while McKinsey & Company forecasts it could unlock up to $580 billion in economic value across the Middle East and North Africa by 2025. 

From streamlining hospital operations to powering life-saving diagnoses, AI is poised to transform every facet of healthcare in the Arab world.

AI in Healthcare Administrations

Arabic studies by the University of Sharjah show that AI-powered automation tools can reduce administrative workloads by up to 70%, allowing healthcare professionals to dedicate more time to patients.

Administrative Workflow

AI-powered systems can automate routine tasks such as appointment scheduling, billing, and insurance processing. This not only reduces the burden on administrative staff but also minimizes errors, leading to more efficient and cost-effective operations.

Virtual Nursing Assistants

These virtual assistants can provide real-time support to patients, offering medication reminders, answering queries, and monitoring vital signs. The results are enhancing patient engagement and allowing healthcare professionals to focus on more complex tasks, ultimately improving overall patient outcomes.

Connect Disparate Healthcare Data

AI can connect disparate healthcare data, ensuring that information flows efficiently between departments. This integrated approach improves communication, collaboration, and decision-making across the entire healthcare ecosystem.

Revenue Cycle Management

Hospitals and medical institutions can benefit from AI to streamline financial processes, from billing to claims processing. Automated systems can identify billing errors, reduce payment delays, and optimize revenue collection, ensuring the financial health of the hospital.

Clinical Documentation Improvement

AI can enhance clinical documentation by automatically extracting relevant information from medical records. This not only improves the accuracy and completeness of patient records but also reduces the administrative burden on healthcare professionals, allowing them to focus more on patient care.

Supply Chain Optimization

Efficient supply chain management is crucial for ensuring the availability of essential medical supplies. AI can analyze usage patterns, predict demand, and optimize inventory levels, preventing shortages and reducing costs associated with excess stock.

Fraud Prevention

Healthcare fraud is a growing concern, leading to financial losses and compromised patient data. AI can play a crucial role in fraud prevention by analyzing patterns in billing data and flagging anomalies. This proactive approach safeguards the financial integrity of the hospital and protects patient information from potential breaches.

AI Enhances Patient Experience

The UAE’s bigger image: Forget long wait times and frustrating phone calls. 

In Abu Dhabi, patients can schedule appointments, access medical records, and even consult with AI-powered virtual assistants, all through a user-friendly app. 

A recent survey by the Emirates Telecommunications Regulatory Authority (TRA) revealed that 82% of UAE residents are open to using virtual health assistants for basic medical consultations. 

This empowers patients, improves accessibility, and promotes a more proactive approach to healthcare.

AI-Assisted Patient Scheduling

Long wait times and scheduling inefficiencies can negatively impact the patient experience. AI-powered scheduling systems can optimize appointment bookings, ensuring efficient use of resources and minimizing patient wait times. This way the integration of AI enhances the overall patient experience but also improves the utilization of healthcare facilities.

Virtual Health Assistants

AI-driven virtual health assistants can serve as a valuable resource for patients, providing information, answering queries, and offering support between doctor visits. These virtual assistants contribute to a more patient-centric approach, empowering individuals to actively participate in their healthcare journey.

Patient Treatment Personalization and Optimization

A 2017 survey by PwC conducted on the urban representatives of Saudi Arabia and the Middle East reveals that:

“A noteworthy percentage, ranging from nearly half to as high as 73%, expressed a willingness to allow a robot to perform minor surgical procedures instead of a human doctor. In the Middle East, this openness varied from 50% in the UAE to 55% in Saudi Arabia and Qatar.”

The predominant motivations for acceptance were the desire for easier and quicker access to healthcare services (36%) and the need for speedy and accurate diagnoses (33%).

Precision Medicine

AI plays a pivotal role in advancing precision medicine by analyzing genetic and molecular data to tailor treatment plans to individual patients. This targeted approach increases the likelihood of treatment success while minimizing potential side effects, ushering in a new era of personalized medicine.

Less Invasive Surgeries

AI can transform surgical procedures by making them less invasive and more precise. Through the analysis of medical data, AI can assist surgeons in planning and executing surgeries with greater accuracy. This not only reduces recovery times for patients but also minimizes the risk of complications during and after surgery.

Drug Dosing and Monitoring

AI algorithms can assist healthcare providers in determining optimal drug dosages based on individual patient characteristics. Additionally, these systems can continuously monitor patient responses to medications, allowing for real-time adjustments to treatment plans and minimizing adverse reactions.

Predictive Analytics for Treatment Success

By analyzing historical patient data, AI can predict the likelihood of treatment success for specific conditions. This empowers healthcare professionals to make more informed decisions about treatment options, increasing the overall efficacy of medical interventions.

A Quick Tour of the Arab African Region

In Tunisia, researchers at the Institut Pasteur are developing AI models to predict and prevent the spread of antibiotic-resistant bacteria, a major challenge in the region. This shift towards “precision medicine” promises to transform chronic disease management and cancer treatment across the Arab world.

In Morocco, AI-powered systems are analyzing large datasets of public health data to predict and prevent outbreaks of infectious diseases. 

In Egypt, AI algorithms are optimizing supply chains for essential medical supplies, ensuring their availability even in remote areas. 

These applications go beyond individual treatment, strengthening the entire healthcare ecosystem and improving preparedness for future health challenges.

AI-Powered Medical Diagnostics

Image Analysis and Radiology

AI excels in image analysis, particularly in the field of radiology. Algorithms can swiftly and accurately analyze medical images, aiding radiologists in diagnosing conditions such as tumors, fractures, and abnormalities. The remarkable results are expediting the diagnostic process and enhancing the accuracy of results.

Pathology and Lab Tests

AI applications in pathology can assist in the analysis of tissue samples and lab tests. By rapidly identifying patterns and anomalies, AI enhances the speed and accuracy of diagnostics, enabling healthcare professionals to make informed decisions about patient treatment plans.

Electronic Health Record Analysis

Efficient analysis of electronic health records (EHRs) is crucial for comprehensive patient care. AI can sift through vast amounts of patient data, identifying relevant information for healthcare providers. This ensures that medical professionals have a holistic view of a patient’s health history, facilitating more personalized and effective treatment plans.

A Blooming Oasis of Healthcare Innovation

The integration of Artificial Intelligence in the Arab world’s healthcare systems is not merely replacing human expertise, but creating a powerful partnership. 

It’s about harnessing the power of technology to empower doctors and nurses, personalize care for patients, and build a more resilient and equitable healthcare ecosystem. 

As the region embraces AI and navigates the challenges, a flourishing oasis of healthcare innovation will emerge, transforming the Arab world into a global leader in medical advancements.

Your Thoughts

We would love to read about your opinions and your personal experiences with AI in the healthcare industry, whether you are a patient or a medical professional. 

The post AI Integration Into Arab Healthcare Systems: KSA & UAE As Examples appeared first on SoftGrad.

In this blog, we will unveil some of the AI revolution taking place in Saudi Arabia, as a leading country in the tech field in the MENA region.

The Table of Contents

1. AI adoption by the banking sector
2. How can Saudi institutions benefit from AI?
3. Saudi Arabia: “THE” financial hub
4. Case Study 1: Al Rajhi Bank’s AI-driven Chatbot
5. Case Study 2: STC Pay AI-Powered Fraud Detection
6. The future landscape

AI adoption by the Banking Sector

People and businesses are divided over their fast adoption of AI in their daily lives and business operations, as there are challenges and threats that come with AI, not only opportunities. The banking industry appears to be a notable outlier both globally and in the MENA region. According to Google, the financial services and banking sector will be the Middle East and North Africa’s largest spender on AI technology. This observation could be explained by the banking sector’s service nature and importance in people’s lives. Consumers are more and more eager for speed, secure, flexible, and convenient financial services, therefore financial institutions must respond quickly.

How can Saudi Financial Institutions benefit from AI?

AI has numerous applications in the financial sector and it benefits all stakeholders, by ensuring convenience, streamlining processes, and improving user experience. 

1- Fraud Detection and Prevention

AI algorithms facilitate the detection of fraudulent transactions in real time preventing financial losses for the bank and its clients.

2- Customer Segmentation and Personalization

AI-powered customer segmentation based on behavior, demographics, and psychographics enables the personalization of products, marketing campaigns, and the overall customer experience.

3- Credit Scoring and Risk Assessment 

AI algorithms empower data-driven lending and credit decisions to provide more accurate credit scores resulting in improved loan portfolio quality and lower default rates. 

4- Chatbots and Virtual Assistants

AI-powered conversational chatbots provide 24/7 customer support with the ability to answer people in a similar human manner. Their primary functions are to answer queries and assist in transactions while reducing response time and labor costs, leading to more customer satisfaction.

5- Algorithm Trading and Investment Management

AI algorithms analyze market trends, news, and historical data to predict stock prices and make trading decisions in real time. Direct benefits include optimization of investment portfolios, confident decisions in volatile markets, and effective risk mitigation.

6- Anti-Money Laundering (AML) and Know Your Customer (KYC) Compliance

AI and natural language processing (NLP) are used to analyze large volumes of data to improve the detection of suspicious activities, reduce false positives and manual investigations, and enhance regulatory compliance.

7- Customer Churn Reduction

Predictive analytics allow the early identification of a possible customer churn and incite the bank to implement new retention strategies and proactive activities such as personalized offers and targeted messages.

Saudi Arabia: “THE” Financial Hub

Saudi Arabia’s Vision 2030 fosters a business-friendly environment and regulatory reforms encouraging international financial institutions to expand their fintech operations in the Kingdom. 

5 main goals are behind these initiatives: 

• Diversify the Saudi Economy
• Position Riyadh as a global Fintech hub
• Create 18,000 direct jobs in the financial sector by 2030
• Increase the Foreign Direct Investment (FDI) by $3.2 billion by 2030
• Increase the share of non-cash transactions among individuals to 70% by 2025

The development and implementation of the Kingdom’s Fintech Strategy in 2022 transformed Saudi Arabia into a home for over 200 fintech firms as of August 2023.  Furthermore, the Saudi Central Bank (SAMA) designed a Regulatory Sandbox welcoming local and international enterprises interested in testing their products on the Saudi market by minimizing their risks and creating new opportunities. By 2030, the Kingdom hopes to have 525 active fintech enterprises. 

Case Study 1: Al Rajhi Bank’s AI-driven Chatbot

Al Rajhi Bank, one of the Kingdom’s leading Islamic banks, intended to enhance customer service and streamline routine interactions. It implemented an AI-powered chatbot named “Rajhi” to assist customers with inquiries, account information, and transactional activities. The conversational chatbot could interpret and answer client concerns in real-time and in a human-like manner by utilizing natural language processing (NLP).  The adoption and integration of the Al-Rajhi conversational chatbot

1. Reduced response times
2. Provided 24/7 customer assistance 
3. Reduced the need for manual intervention
4. Enhanced customer satisfaction

Case Study 2: STC Pay AI-Powered Fraud Detection

STC Pay, a prominent Fintech company in Saudi Arabia, aimed to strengthen its security measures and combat fraudulent activities. To achieve its goal, it integrated AI algorithms for fraud detection, analyzing transaction patterns, user behavior, and other variables to identify potential fraud.

Success was tangible through

1. The detection and prevention of fraudulent transactions
2. The adoption of the algorithms to new fraud patterns
3. The protection of users’ funds and prevention of evolving threats
4. The minimization of disruptions for genuine users

The Future Landscape

Artificial Intelligence is changing the way financial institutions work and consumers benefit from financial services. 

Saudi Arabia is leading the way with a solid vision, a well-implemented strategy, and active strong partnerships. 

The challenge remains how to choose those partners and what kind of partnerships the Kingdom can build. 

The answers will either support the efforts of Saudi Arabia or slow it from its goals… let’s discover this in the next blog.

The post The Impact of AI on Saudi Arabia’s Banking and Fintech Sectors appeared first on SoftGrad.

And with a Global non-cash transaction volume set to reach $1.3 trillion in 2023, online fraud statistics are becoming alarming.

Just this year, online shopping scams added up to more than $48 billion worldwide! 

So, the big question is, how can AI (Artificial Intelligence) make our banks safer and stronger? That’s what we’re diving into in this blog

Table of Contents

Why AI has become crucial in the financial sector

The most common types of banking fraud

The role of AI in fraud detection

Case study: Danske Bank

AI alignment with regulatory compliance

3 powerful ways conversational AI prevents fraud in banking and fintech

AI for safer and more convenient banking and fintech

Why has AI become crucial in The Financial Sector?

The financial sector connects all business and non-business sectors, for-profit and nonprofit, entities and individuals together.

What drives its evolution and technological adoption can be summarized by 3 key motivators: 

• Delivering a better customer service
• Responding to external threats
• Creating new financial opportunities

Banks and Fintech companies have a moral obligation to move as fast as the technological wave and optimize their services and operations to be up to standards and customers’ needs.

The Digitization of Banking Products

Digitizing banking products resulted in a surge of new payment types such as instant payment and e-payments, and gave users access to online applications.

This pivotal move in financial transactions and consumer behavior improved the overall customer experience but resulted in a surge in fraud. 

Banks and Fintech companies were pressured to recur to Machine Learning (ML) and Deep Learning (DL) to stay ahead of tech-savvy fraudsters.

Shift in Consumer Behavior

“We are moving into an era of frictionless, or intuitive, payments” – Mastercard. 

Consumers are becoming fast and educated adopters. They require top-quality products, convenience, flexibility, and privacy. 

In response, banks shifted their focus from keeping fraud low and limiting losses to providing the best consumer experience with the least amount of fraud thus introducing friction when it is absolutely necessary.”

AI Fraud Detection vs Conventional Fraud Detection

Traditional fraud detection methods often rely on rule-based systems, using predefined rules to flag potentially fraudulent transactions. 

While effective to some extent, these systems may need help to analyze vast amounts of data in real-time.

AI-based fraud detection relies on AI, especially machine learning, which excels in processing and analyzing massive datasets. 

It can identify subtle patterns and anomalies by learning from historical transaction data, enabling more accurate and timely fraud detection.

McKinsey’s AI Playbook announces, “With AI-based software solutions, businesses have the potential to unlock up to $1 trillion in extra value!”

The Most Common Types of Banking Fraud

The types of banking Fraud are enumerable, and with new tech adoption comes new challenges every day. 

Here are the most common types of banking fraud around the world. 

Unauthorized Transactions

A Forbes article reported that roughly eight in 10 mobile banking users are concerned about credit card fraud.

Fraud prevention through AI: AI detects anomalies in the card owner’s spending patterns and flags them in real-time.

By building predictive models of the user’s future expenditure it immediately sends notifications in case of suspicious behavior. 

The legitimate card owner can then block the card and contain damages.

AI-powered banking systems can also create ‘purchase profiles‘ for customers and identify transactions that deviate significantly from the usual.

Real-life application: Mastercard’s Decision Intelligence employs AI to analyze cardholder spending behavior (historical shopping), set a behavioral baseline against which it compares each new transaction, and evaluate the risk of fraud in real-time, enabling it to block suspicious transactions before they are authorized.

Phishing Scams

Both consumers and corporate employees can fall victim to phishing attacks leading to unauthorized transactions, account takeovers (ATO), data breaches, or identity theft.

Fraud prevention through AI: Machine learning (ML) algorithms can detect fraudulent activity in emails based on subject lines, content, and other details and label them as spam. This warns the user and reduces the danger of fraud.

Identity Theft

This type of fraud is the most common complaint among consumers. 

In this case, cybercriminals steal a customer’s identity by hacking into their account and changing crucial account user credentials like passwords. 

Fraud prevention through AI: Because AI recognizes the customer’s behavior patterns, it may detect unexpected activities such as password changes and contact information. To avoid identity theft, it warns the customer and employs measures such as multi-factor authentication.

Document Forgery

Here we talk about forged signatures, fake IDs, and fake credit card and loan applications which are common issues in banking.

Fraud prevention through AI: AI/ML algorithms learn the patterns of a signature and an ID, and detect minor flaws imperceptible to the human eye. 

This is how banks can differentiate between original and fake identities, authenticate signatures, and detect forgeries with extremely high accuracy.

They can also reduce the possibility of someone cashing a cheque with a phoney ID.

Other useful tools could be multi-factor authentication and AI-backed KYC measures.

The Role of AI in Fraud Detection

AI brings a new dimension to fraud protection, employing advanced algorithms and machine learning to analyze vast datasets in real-time. 

Here’s how AI can detect Fraud and prevent it:

Pattern Recognition: The Power of Anomalies

AI excels in recognizing patterns and identifying anomalies within vast datasets. 

By establishing a baseline of normal user behavior, AI algorithms can swiftly detect deviations that may signal fraudulent activity. 

This dynamic approach enables banks to stay one step ahead of fraudsters who continually refine their tactics.

Behavioral Biometrics: Unmasking the Fraudsters

In addition to standard security measures, AI uses biometrics based on behavior to verify users. 

To confirm a user’s authenticity, factors like typing patterns, mouse motions, and even the unique cadence of voice can be analyzed. 

This not only adds an additional layer of security but also ensures a smooth and user-friendly experience.

Predictive Analytics: Anticipating Fraudulent Moves

AI doesn’t just react to known threats; it anticipates them. 

Through predictive analytics, AI models assess historical data to forecast potential fraud trends. 

This proactive approach allows banks to implement preemptive measures, protecting both their assets and the trust of their customers.

Real-time Monitoring: The Need for Speed

Fraud doesn’t wait, and neither should protection measures. 

AI enables real-time monitoring of transactions, swiftly identifying suspicious activities as they occur. 

This rapid response time is crucial in mitigating potential losses and preventing fraudulent transactions from being completed.

Case Study: Danske Bank

Danske Bank is a Nordic universal bank, though it has 145 years of experience in the market, its fraud detection rate was as low as 40%. 

Out of 1,200 false positives per day, 99.5% of all cases the bank was investigating were not fraud-related.

To put an end to these dead ends, the Bank decided to strategically apply innovative analytic techniques, including AI, to better identify instances of fraud while reducing false positives.

The results speak for themselves: 

1. A 60% reduction in false positives, with an expectation to reach as high as 80%.
2. A 50% increase in true positives.
3. Focus resources on actual cases of fraud.

AI Alignment with Regulatory Compliance

The General Data Protection Regulation (GDPR) stands as a cornerstone for safeguarding customer data privacy. 

AI, with its ability to analyze and process vast datasets, aids financial institutions in adhering to GDPR by implementing robust data protection measures. 

Through advanced encryption algorithms and anonymization techniques, AI systems contribute to the secure handling of sensitive information, aligning with GDPR’s principles of lawful and transparent data processing.

Moreover, the Payment Card Industry Data Security Standard (PCI DSS) imposes strict guidelines on the protection of payment card data.

Financial institutions leverage AI-powered fraud detection systems to meticulously scrutinize transactions in real time, identifying anomalies and potential threats to cardholder information.

By employing machine learning algorithms, these systems continuously evolve to recognize new patterns of fraudulent activities, thereby ensuring ongoing compliance with PCI DSS requirements.

The adaptive nature of AI not only fortifies defenses against unauthorized access but also positions financial institutions to proactively address emerging challenges, a crucial aspect in meeting the ever-evolving landscape of regulatory expectations.

3 Powerful Ways Conversational AI Prevents Fraud in Banking and Fintech

Talking to your customers is crucial to preventing fraud. 

Any person may break their behavioral patterns for any reason; Financial institutions certainly don’t want to cancel a legitimate transaction. 

Conversational AI can make these checks fully automatic, which is the perfect balance between being strict about stopping fraud and making things easy for customers.

Conversational AI for Transaction Verification

Conversational AI allows an automated system to respond in real-time to human speech, resulting in dynamic, lifelike discussions.

This achieves two critical goals for fraud detection: 

1. Increasing consumer trust in your voicebot. 
2. Enabling voicebots to collect the data required by their systems to validate legitimate transactions and invalidate fraudulent ones.

This transition to voice-based verification makes push notifications more accessible and convenient for all users.

Voice AI to Prevent Voice Phishing Attacks

In voice phishing scams, fraudsters call your customers, often using poor-quality TTS voices to automate the attempt. 

They may claim to represent you to get personal information they can use to drain account credentials.

This is a serious challenge to banks because their systems aren’t involved in the scam. You can’t control them!

Thus, the key to preventing phishing is to give customers the power to authenticate you.

How can customers authenticate their banks’ representatives?

By presenting a single, unmistakable voice for your brand: a fully custom-built branded TTS voice, also known as a custom voice, unique to your institution, built on neural networks that produce warm, lifelike speech.

Voice AI also helps banks and fintech to authenticate the people they serve.

Voice Biometrics for User Authentication

Voice authentication is a new form of biometrics that can prevent identity theft in voice-based interactions between banks and consumers. 

By using AI it identifies a speaker’s voice as belonging to them and only them, as it has more than 100 unique identifiers contained within the human voice.

However, these speech biometrics systems must also guide users through the log-in process, which necessitates the usage of voice output. 

A recognized bespoke branded voice provides consumers with the confidence they require to accept this new verification approach. 

AI For Safer and More Convenient Banking and Fintech

AI is establishing a new era of convenience and security for financial institutions and consumers alike. 

By embracing the power of artificial intelligence in all its aspects banks, fintech and consumers can ensure accurate data analysis, prompt response, secure transactions, and unwavering fraud prevention.

The post How Does AI Help in Financial Fraud Detection and Protection appeared first on SoftGrad.

Table of contents

Introduction

Saudi Arabia’s Vision 2030

The National Strategy for Data and AI

Smart City Initiative in Riyadh

AI-Powered Healthcare at King Faisal Specialist Hospital

SoftGrad Hand in Hand with Saudi Arabia

Introduction

Artificial intelligence (AI) is changing the business and career landscape worldwide; countries and companies are already competing for leadership and dominance.

 The U.S. and China are predicted to account for about 70% of the global economic impact of AI by 2030.

This number doesn’t seem to intimidate Saudi Arabia and other Arab countries, especially in the GCC, from entering the competition. 

The estimated economic impact of AI in the Middle East by 2030 is $320 billion, of which $135.2 billion is expected to accrue to Saudi Arabia.

 “The annual growth in the contribution of AI is expected to range between 20 and 34% per year across the region, with the fastest growth in the UAE, followed by Saudi Arabia.” Quoted from PWC. 

In this blog, we explore the efforts of Saudi Arabia (KSA) in leading this movement in the Middle East, the Arab World, and internationally. 

Saudi Arabia’s Vision 2030

Advanced technology is at the heart of Saudi Arabia’s Vision 2030. In fact, the Saudi government estimates that achieving 66 of its 2030 Vision objectives is directly tied to the development and deployment of AI-powered applications.

The core of the 2030 Vision is to propel Saudi Arabia towards a more diversified, sustainable, and knowledge-driven economy, ultimately improving the well-being and opportunities of its citizens.

Let’s dive into some objectives and how AI can help achieve them. 

1- Diversifying the economy

The KSA is striving to reduce its usage of oil and its exports as its ultimate source of revenue. One of its efforts in this field is its transformation into an AI powerhouse, attracting $20 billion in foreign and local investments by 2030 and harvesting AI-oriented entrepreneurs and businesses. 

2- Increasing Employment and Job Opportunities

Saudi Arabia is determined to future-proof its workforce against potential AI threats by investing in growing tech-oriented talents. 

It has already started training and developing a pool of 20,000 AI and data specialists by 2030. 

3- Developing the Private Sector

By launching its “National Strategy for Data and Artificial Intelligence” in October 2020, Saudi Arabia is anticipated to secure a 31.3% share of the technology’s growth between 2018 and 2030 in terms of average annual growth in its contribution by region, according to the PwC report.

4- Improving Education and Training

Saudi Arabia is nurturing homegrown talents by promoting AI competitions and “hackathons” and encouraging its researchers and students to tackle AI-related challenges to gain valuable hands-on experience.

It is also in the process of establishing the International Centre for Artificial Intelligence Research and Ethics in Riyadh.

Advancing technological infrastructure, strengthening social protection and healthcare, improving quality of life, and empowering women in the workforce are all on the list of objectives of the 2030 Vision. 

The National Strategy for Data and AI (NSDAI)

Since its launch in 2020, it has ushered in the most significant regulatory development impacting the emerging tech ecosystem in Saudi Arabia. 

Upon it, the Kingdom established the Saudi Data and Artificial Intelligence Authority (SDAIA) and its three sub-entities 

• The National Centre for AI (NCAI) 
• The National Data Management Office (NDMO) 
• The National Information Centre (NIC) 

SDAIA, in partnership with the Ministry of Telecommunications and Information Technology (MCIT), led the G20’s Digital Economy Task Force and guided the Kingdom’s response to COVID-19 through the launch of apps such as ‘Tawakkalna’ and ‘Tabaud’.

The (NSDAI) has 6 main pillars:

• Ambition: to place Saudi Arabia amongst the top 15 countries in the development and application of AI by 2030
• Skills: to train more than 20,000 data and AI specialists and experts by 2030.
• Partnerships: with leading AI countries, international organizations, and private sector institutions.
• Investments: of over USD 20 billion in Data and AI
• Regulation: to foster a business-friendly regulatory environment based on international best practices.
• Ecosystem: more than 300 active data and AI start-ups hosted in the Kingdom by 2030, and world-class regulatory sandboxes for the development and deployment of AI-powered technology.

Smart City Initiative in Riyadh

In an attempt to achieve its 2030 vision, improve the quality of life, and transform the capital, Riyadh, into a smart city, Saudi Arabia is integrating AI-driven solutions to enhance urban planning, infrastructure, and public services.

Some Breakthroughs:

✅ AI-powered traffic management systems dynamically regulate traffic flow, alleviating congestion during peak hours.

✅ Predictive analytics assist in resource allocation for services like waste management and energy consumption.

✅ The employment of AI-enabled public safety measures, utilizing real-time data to enhance emergency response times and ensure citizen security. 

Neom is another visionary project unfolding in Saudi Arabia, and it will be a huge step for Humankind.

AI-Powered Healthcare at King Faisal Specialist Hospital

This premier healthcare institution is embracing AI to revolutionize patient care across the Kingdom. 

By employing cutting-edge AI algorithms it is empowering its healthcare providers to detect (through swift and accurate diagnoses) and treat diseases at earlier stages, significantly improving patient outcomes. 

Its utilization of AI-driven predictive analytics is optimizing patient flow, ensuring timely access to care, and reducing waiting times.

The hospital also wanted to enhance patient engagement and streamline administrative processes, so it integrated AI-powered chatbots.

SoftGrad Hand in hand with Saudi Arabia

To achieve its ambitious vision, Saudi Arabia is building strategic partnerships with countries and business entities that have the knowledge, experience, expertise, and skilled workforce. 

Since its foundation in 2010, SoftGrad has accumulated more than a decade of experience and introduced next-gen technologies to the Middle East. 

Our presence in Silicon Valley, U.S., serves us in terms of industry updates and research, and our physical operating offices in Lebanon, KSA, UAE, and Cyprus help us operate closely with all the GCC countries. 

We assisted many leading Saudi companies like MBC Group, in leveraging advanced technologies to create new revenue streams and engage a wider audience. 

Our collaboration with the Saudi Government and Saudi investors can supply the Kingdom with world-class AI-powered services and products and transform its cities into smart cities. 

SoftGrad Academy is also ready to contribute to the skilling up of Saudi youth and the workforce in the fields of AI, programming languages, and tech development. 

Conclusion

Localizing the AI revolution in the Arab World is in good hands as it perfectly matches the historic role of Saudi Arabia in leading the region on many levels. 

Bringing AI innovation, not only its consumption will propel local societies forward as well as diversify and enhance our economies. 

SoftGrad couldn’t be happier to see this innovative scene unfolding in front of its eyes while proudly contributing to it through its services, products, and Academy. 

The post How Saudi Arabia is leading the AI revolution in the Middle East appeared first on SoftGrad.

But do they really have to worry about Artificial Intelligence (AI) or should they consider investing in it? 

Table of content

The Opportunities presented by AI to Universities

4 strategies universities need to apply to stay relevant in the age of AI

The huge gap between the university curriculum and the job market

4 Gulf countries are leading the digital upskilling revolution

Eursiko Academy: Tech Skills Upgrade for Arab Students

The Opportunities Presented by AI to Universities

Artificial Intelligence (AI) presents immense opportunities to universities in administration and education, by reducing costs, saving staff time and effort, and elevating the learning path of students in all majors. 

Administrative Work

Virtual Assistants and Conversational Chatbots: In their basic form they can provide students support with information about the campus and the curriculum. In their most advanced form, they are useful tools for assisting in mental health and learning difficulties. 

Automated Grading and Feedback: Professors and administrators can now benefit from machine learning (ML) and natural language processing (NLP) to grade certain assignments and exams or even get instant feedback on objective and subjective questions by analyzing patterns and language usage. 

Campus and Student Security: More and more universities are counting on AI-powered technologies and AI algorithms to protect campuses, and students’ lives and physical properties through video analytics, facial recognition, and anomaly detection. 

AI In Education

Personalized Learning: Not all students are equal in talent and have the same level of learning, that’s why some universities and institutions have adopted intelligent tutoring systems (ITS) to personalize the learning experience of their students in all majors. By using machine learning (ML) these systems can analyze individual learning patterns, identify the students’ needs, track their progress, and deliver tailored feedback.

Medical Education: Medical schools can immensely benefit from AI, and VR/AR to reduce training costs while improving efficiency. Medical education powered by AI includes VR surgical training, anatomy study, and educating care providers on the latest therapies and drugs. 

Research and Curriculum Upgrade: Professors and academic researchers are increasingly using AI and data analytics to conduct studies and get data-driven results, so they can improve their methods, solutions, and curriculum. Students are also empowered to use AI and VR/AR technologies in their research and projects whether in engineering, medical, Anthropology, Economics, etc. 

4 Strategies Universities need to apply to stay relevant in the age of AI

AI Literacy for All: AI is the top future skill any person who wants to stay relevant in the job market should have, thus universities should prioritize AI literacy as a foundation skill for all students. AI literacy includes understanding the basics of AI, and its ethical implications on privacy, bias inherent from those who programmed it, job displacement, and social inequality, and how to use AI tools for faster work and better results.

Adopting Interdisciplinary Curricula: Universities should encourage projects involving students from different educational backgrounds such as engineering, computer science, human sciences, etc. to work interdependently on real-world problems so students can better understand AI’s potential and limitations during the implementation. 

Collaboration with Industry Experts and Tech companies: University professors alone can’t cover the gap between curriculum and fieldwork, that’s why all educational institutions are encouraged to establish partnerships, internships, and collaborative research projects with tech leaders and practitioners to enable students to get hands-on experience and relevant skills. SoftGrad is already assisting schools and universities in Lebanon to achieve this goal through its “SoftGrad Academy” and is now expanding in the Gulf region.  

Adopting Reskilling & Upskilling Programs: Universities and educational institutions should now consider opening reskilling and upskilling programs besides the traditional degree programs to encourage students to close the gap between their current skills and those required AI jobs. Such programs facilitate career transitions and help individuals remain relevant in the changing job market through lifelong learning. 

The Huge gap between University Curriculum and the Job Market

The global fear of AI replacing human labor is blinding academic leaders and the workforce from seeing the enormous opportunities AI brings to the table. 

According to Goldman Sachs research, generative AI such as ChatGPT could replace 300 million jobs.

IBM goes even further by saying it could replace 800 million jobs. 

But the other side of the story is told by the World Economic Forum which predicts that 1 billion jobs will be transformed by technology by 2030. 

It is not all loss, but new opportunities to take advantage of: 

Upskilling, reskilling, learning, and relearning for more prosperous societies in the digital era. 

The same research reveals that 8 in 10 employers in the tech sector said they have difficulty filling open roles.

This is due to the huge gap between university curriculums and actual job requirements in addition to the skills shortage by those in the job market. 

IBM’s report found that 28% to 34% of recruiters struggle to find applicants with experience in deep learning, machine learning, and data engineering and analytics. They also struggle to find job seekers skilled in programming languages and software engineering. 

This challenge extends to interpersonal skills, as AI jobs require skills such as problem-solving and critical thinking, which are not well-developed by the workforce. 

4 Gulf Countries are leading the Digital upskilling revolution

Saudi Arabia (KSA), Bahrain, Qatar, and the United Arab Emirates (UAE) are not afraid of AI and instead of declaring war, they are leading a revolution with the world’s giant tech leaders such as IBM, Meta, Microsoft, Google and Cisco. 

Educational plans, over the span of 5 years, project to upskill 100,000 young Saudis as well as the employees of government agencies to meet the growing demands for digital expertise. 

In Bahrain, the government already started the journey of training 20,000 Bahrainis in cybersecurity as part of its information technology strategy under the Economic Recovery Plan.

Similarly in the UAE, the government has secured deals with giant tech companies to train 100,000 computer coders open for residents and non-residents. 

They are also leading the AI educational revolution with Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) which is ranked 18th globally in AI, CV, ML, NLP, AND Robotics. 

Will you join the efforts of those Arab Gulf countries in their revolution?

SoftGrad Academy: Tech Skills Upgrade for Arab Students

As a leading Tech company, SoftGrad previewed the lack of technological skills among graduates and the enormous gap between education and fieldwork, since its foundation in 2006. 

For this reason, it established its own academy, “SoftGrad Academy” to pave the way for graduates and undergraduates to embrace next-gen technologies and be able to fill newly created job positions by Artificial Intelligence. 

Since its establishment in 2019, Eursiko Academy has trained over 1000 students and is expanding its activities and collaborations for more impact. 

Our programs target 3rd year and 4th year university students, to train them over a period of 4 to 12 weeks, on multiple IT programs such as front-end and back-end development and game development, etc. 

The scope we cover is broadening, as we are increasingly introducing new programs to our curriculum, such as AI & Data Analytics-related programming languages such as Python, and R, because they are the basis for any AI job. 

And to empower students beyond skills and knowledge, SoftGrad is devoted to assisting promising talents with their tuition fees, and it offers them part-time and full-time employment opportunities, so they can start building a career and contribute to the economies of their countries. 

Finally, SoftGrad Academy believes in the growing potential of new generations, therefore it has designed specific programs to introduce children to the world of coding. 

Conclusion

AI is here to stay. Instead of fighting it, let’s invest in it to improve the learning experience of all students, and open them new doors of recruitment. SoftGrad is supporting Arab Universities and schools in their efforts for digital transformation through its services and academy. 

The post How can Universities lead with AI?  appeared first on SoftGrad.

Artificial Intelligence is exponentially powering sectors in most of our industries. In the banking & finance world, AI is progressively contributing in simplifying the industry’s different use cases. The below diagram from BI Intelligence illustrates how AI is shaping the different sections of the banking world:

The post Maturity of uses of Artificial Intelligence in Banking & Payments appeared first on SoftGrad.