Pan Asia bank recruits IBM for digital bank infrastructure upgrade
IBM announces its collaboration with Pan Asia Banking Corporation PLC of Sri Lanka to modernize the bank's digital infrastructure and deliver faster, more secure banking services across Sri Lanka.
0
This content is provided by an external author without editing by Finextra. It expresses the views and opinions of the author.
Banks in Sri Lanka are undergoing major transformations to meet the needs of new and existing customers seeking personalized product experiences, transparency, and security, all in real time. To support future growth and 24/7 banking operations, Pan Asia Bank upgraded its technology infrastructure with advanced servers, AI-powered observability for application monitoring and scalable data storage. These helped the bank enhance cloud readiness and provide real-time visibility into application performance— reducing downtime and improving customer experience.
Working with its business partner South Asian Technologies (SAT), IBM deployed the latest AI and hybrid cloud solutions including IBM Power10 servers, IBM FlashSystem storage and IBM Instana for real-time, full-stack observability for application performance monitoring.
The transformation has already delivered strong results:
40% boost in application performance
50% faster deployment of new services
Improved uptime and lowered operational costs
With this modernization, Pan Asia Bank is better positioned to meet the evolving needs of Sri Lankan customers and drive digital innovation in the banking sector.
'As part of our commitment to deliver impactful, innovative, and responsible banking solutions to our customers and stakeholders, refreshing our technology systems that power our services was critical,' said Kanchana Devasurendra, Chief Information Officer, Pan Asia Bank. 'With their proven track record of helping banking institutions around the world digitally transform, it was a natural choice for us to adopt these solutions from IBM. We believe that this transformation will help us strengthen our customer relationships through better service delivery and accelerate our innovation capability.'
Speaking on the collaboration, Sandip Patel, Managing Director, IBM India & South Asia said, 'Sri Lanka presents tremendous opportunities for digital transformation, and we're proud to collaborate with forward-thinking institutions like Pan Asia Bank in harnessing the power of AI and hybrid cloud. By combining the right system, software, and services with the local expertise of our ecosystem partners, we're helping Sri Lankan enterprises tackle complex challenges, unlock new possibilities, and drive measurable business outcomes — ultimately contributing to country's broader economic growth.'
Recognizing the unique needs of the Sri Lankan market, IBM continues to foster strategic collaborations and co-create tailored solutions that help local businesses drive efficiency, boost productivity, and achieve sustainable growth across industries.
Hashtags

Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles


Geeky Gadgets
14 minutes ago
- Geeky Gadgets
How AI is Predicting Viral Videos : The Secret Tech Behind Tomorrow's Viral Sensations
What if you could predict the next viral sensation before it takes over your feed? Imagine knowing which quirky dance challenge, heartfelt story, or jaw-dropping stunt was about to explode in popularity—days or even hours before the rest of the world caught on. Thanks to the rise of AI-powered tools, this isn't just a futuristic fantasy; it's already happening. By analyzing massive datasets and uncovering hidden patterns in real time, artificial intelligence is transforming how we understand and anticipate digital trends. For content creators, marketers, and platforms, this technology offers a innovative advantage in the race to capture attention in an increasingly crowded online space. Creator Magic explores how AI is reshaping the way viral videos are identified and used. You'll discover how advanced algorithms and data pattern recognition allow these tools to predict which videos are poised to dominate timelines—before they even hit their stride. From analyzing engagement metrics to decoding emotional tones in comments, AI provides insights that human intuition alone could never achieve. Whether you're a creator looking to ride the next big wave or a marketer eager to align with emerging trends, this technology holds the key to staying ahead of the curve. The question is: how will you use it to your advantage? AI-Powered Viral Video Prediction How AI Identifies Viral Video Potential AI's ability to predict viral content is rooted in its capacity to process and analyze massive amounts of data in real time. These systems evaluate key factors such as user interactions, engagement metrics, and historical trends to uncover patterns that indicate a video's potential to gain popularity. For example, an AI tool might detect a sudden surge in shares, likes, or comments within a specific demographic. By comparing this activity to patterns observed in past viral videos, the system can forecast whether the content is likely to achieve broader appeal. This predictive capability enables AI to identify trends that might otherwise go unnoticed by human observers, offering a unique advantage in spotting viral content early. The Role of Advanced Algorithms in Trend Prediction The foundation of AI's predictive power lies in its advanced algorithms, which are designed to analyze complex datasets and identify correlations that drive content virality. These algorithms evaluate multiple factors, including: Video length and format Topic relevance to current events or audience interests Engagement metrics such as comments, shares, and watch time Natural Language Processing (NLP) plays a critical role in this process by analyzing video titles, descriptions, and comments to understand the emotional tone and context of the content. Additionally, AI systems assess visual and audio elements—such as color schemes, themes, and music—that are essential for capturing viewer attention. By combining these insights, the algorithms can make highly accurate predictions about a video's potential to go viral, offering actionable intelligence for content creators and marketers alike. AI Agent Finds Viral Videos Before They Go Mainstream Watch this video on YouTube. Here are additional guides from our expansive article library that you may find useful on AI videos. Spotting Trends Early Through Data Pattern Recognition AI's ability to recognize data patterns is a powerful tool for early trend detection. By continuously monitoring user behavior and content performance, these systems can identify emerging trends before they reach mainstream popularity. This capability is particularly valuable for marketers and creators who aim to stay ahead of the curve in a fast-paced digital environment. For instance, if an AI system identifies a growing interest in videos featuring a specific challenge, theme, or format, it can alert users to this trend. Content creators can then produce relevant material while the trend is still gaining momentum, significantly increasing their chances of reaching a larger audience. This proactive approach not only helps creators maximize their impact but also allows brands and advertisers to align their campaigns with current audience interests, making sure greater relevance and engagement. Real-World Applications of Viral Content Forecasting The ability to forecast viral content has far-reaching applications across various industries. Social media platforms can use this technology to recommend trending videos to users, enhancing engagement and user satisfaction. By identifying high-potential content, platforms can also optimize their algorithms to prioritize videos that resonate with their audience. For brands and advertisers, AI tools provide a strategic advantage by identifying content that aligns with current trends. This allows them to collaborate with creators or promote videos that are likely to perform well, making sure their campaigns achieve maximum visibility and impact. Content creators, on the other hand, benefit from actionable insights into what resonates with their audience. By understanding the factors that drive virality, they can refine their strategies, focusing on topics, formats, and styles that maximize their reach. In a competitive digital landscape, this combination of creativity and data-driven decision-making is essential for standing out and building a loyal audience. The Future of AI in Trend Prediction AI-powered tools for predicting viral videos represent a significant advancement in trend analysis. By using advanced algorithms, data pattern recognition, and early trend detection, these systems provide unparalleled insights into the dynamics of content popularity. Whether you're a marketer, content creator, or platform operator, adopting this technology offers a clear path to making informed decisions and staying ahead of the competition. As AI continues to evolve, its role in shaping the future of digital content will only grow, influencing how trends are identified, analyzed, and used across industries. Media Credit: Creator Magic Filed Under: AI, Top News Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.


Daily Mail
3 hours ago
- Daily Mail
Layoffs sweep America as AI leads job cut 'bloodbath'
Elon Musk and hundreds of other tech mavens wrote an open letter two years ago warning AI would 'automate away all the jobs' and upend society. And it seems as if we should have listened to them. Layoffs are sweeping America, nixing thousands of roles at Microsoft, Walmart, and other titans, with the newly unemployed speaking of a'bloodbath' on the scale of the pandemic. This time it's not blue-collar and factory workers facing the ax - it's college grads with white-collar roles in tech, finance, law, and consulting. Entry-level jobs are vanishing the fastest, stoking fears of recession and a generation of disillusioned graduates left stranded with CVs no one wants. Graduates are now more likely to be unemployed than others, data has shown. Chatbots have already taken over data entry and customer service posts. Next-generation 'agentic' AI can solve problems, adapt, and work independently. These 'smartbots' are already spotting market trends, running logistics operations, writing legal contracts, and diagnosing patients. The markets have seen the future: AI investment funds are growing by as much as 60 per cent a year. 'The AI layoffs have begun, and they're not stopping,' says tech entrepreneur Alex Finn. Luddites who don't embrace the tech 'will be completely irrelevant in the next five years,' he posted on X. Procter & Gamble, which makes diapers, laundry detergent, and other household items, this week said it would cut 7,000 jobs, or about 15 per cent of non-manufacturing roles. Its two-year restructuring plan involves shedding managers who can be automated away. Microsoft last month announced a cull of 6,000 staff - about three per cent of its workforce - targeting managerial flab, after a smaller round of performance-related cuts in January. LA-based tech entrepreneur Jason Shafton said the software giant's layoffs spotlight a trend 'redefining' the job market. 'If AI saves each person 10 per cent of their time (and let's be real, it's probably more), what does that mean for a company of 200,000?' he wrote. Retail titan Walmart, America's biggest private employer, is slashing 1,500 tech, sales, and advertising jobs in a streamlining effort. Citigroup, cybersecurity firm CrowdStrike, Disney, online education firm Chegg, Amazon, and Warner Bros. Discovery have culled dozens or even hundreds of their workers in recent weeks. Musk himself led a federal sacking spree during his 130-day stint at the Department of Government Efficiency, which ended on May 30. Federal agencies lost some 135,000 to firings and voluntary resignation under his watch, and 150,000 more roles are set to be mothballed. Employers had already announced 220,000 job cuts by the end of February, the highest layoff rate seen since 2009. In announcing cuts, executives often talk about restructuring and tough economic headwinds. Many are spooked by President Donald Trump's on-and-off tariffs, which sent stock markets into free-fall and prompted CEOs to second-guess their long-term plans. Others say something deeper is happening, as companies embrace the next-generation models of chatbots and AI. Robots and machines have for decades usurped factory workers. AI chatbots have more recently replaced routine, repetitive, data entry, and customer service roles. A new and more sophisticated technology - called Agentic AI - now operates more independently: perceiving the environment, setting goals, making plans, and executing them. AI-powered software now writes reports, analyzes spreadsheets, creates legal contracts, designs logos, and even drafts press releases, all in seconds. Banks are axing graduate recruitment schemes. Law firms are replacing paralegals with AI-driven tools. Even tech startups, the birthplace of innovation, are swapping junior developers for code-writing bots. Managers increasingly seek to become 'AI first' and test whether tasks can be done by AI before hiring a human. That's now company policy at Shopify and is how fintech firm Klarna shrank its headcount by 40 per cent, CEO Sebastian Siemiatkowski told CNBC last month. Experienced workers are encouraged to automate tasks and get more work done; recent graduates are struggling to get their foot in the door. From a distance, the job market looks relatively buoyant, with unemployment holding steady at 4.2 per cent for the third consecutive month, the Labor Department reported on Friday. But it's unusually high - close to 6 per cent - among recent graduates. The Federal Reserve Bank of New York recently said job prospects for these workers had 'deteriorated noticeably'. That spells trouble not just for young workers, but for the long-term health of businesses - and the economy. Economists warn of an AI-induced downturn, as millions lose jobs, spending plummets, and social unrest festers. It's been dubbed an industrial revolution for the modern era, but one that's measured in years, not decades. Dario Amodei, CEO of Anthropic, one of the world's most powerful AI firms, says we're at the start of a storm. AI could wipe out half of all entry-level white-collar jobs - and spike unemployment to 10-20 per cent in the next one to five years, he told Axios. Lawmakers have their heads in the sand and must stop 'sugar-coating' the grim reality of the late 2020s, Amodei said. 'Most of them are unaware that this is about to happen,' he said. 'It sounds crazy, and people just don't believe it.' Frustrations: Sacked workers have taken to social media to vent their frustrations about the new tech crunch Young people who've been culled are taking to social media to vent their anger as the door to a middle-class lifestyle closes on them. Patrick Lyons calls it 'jarring and unexpected' how he lost his Austin-based program managing job in an 'emotionless business decision' by Microsoft. 'There's nothing the 6,000 of us could have done to prevent this,' he posted. A young woman coder, known by her TikTok handle dotisinfluencing, posts a daily video diary about the 'f***ing massacre' of layoffs at her tech company as 'AI is taking over'. Her job search is going badly. She claims one recruiter appeared more interested in taking her out for drinks than offering a paycheck. 'I feel like s***,' she added. Ben Wolfson, a young Meta software engineer, says entry-level software jobs dried up in 2023. 'Big tech doesn't want you, bro,' he said. Critics say universities are churning out graduates into a market that simply doesn't need them. A growing number of young professionals say they feel betrayed - promised opportunity, but handed a future of 'AI-enhanced' redundancy. Others are eyeing an opportunity for a payout to try something different. Donald King posted a recording of the meeting in which he was unceremoniously laid off from his data science job at consulting firm PwC. 'RIP my AI factory job,' he said. 'I built the thing that destroyed me.' He now posts from Porto, in Portugal - a popular spot for digital nomads - where he's founded a marketing startup. Industry insiders say it won't be long before another generation of AI arrives to automate new sectors. As AI improves, the difference between 'safe' and 'automatable' work gets blurrier by the day. Human workers are advised to stay one step ahead and build AI into their own jobs to increase productivity. Optimists point to such careers as radiology - where humans initially looked set to be outmoded by machines that could speedily read medical scans and pinpoint tumors. But the layoffs didn't happen. The technology has been adopted - but radiologists adapted, using AI to sharpen images and automate some tasks, and boost productivity. Some radiology units even expanded their increasingly efficient human workforce. Others say AI is a scapegoat for 2025's job cuts - that executives are downsizing for economic reasons, and blaming technology so as not to panic shareholders. But for those who have lost their jobs, the future looks bleak.

Finextra
3 hours ago
- Finextra
The AI Risk Equation: Delay vs Safety – Calculating the True Cost: By Erica Andersen
In the race to adopt artificial intelligence, too many enterprises are flooring the brakes while neglecting the accelerator. As the saying goes, "AI may not be coming for your job, but a company using AI is coming for your company." The pressure to integrate AI solutions is becoming intense, and organizations that have missed early adoption windows are increasingly turning to external vendors for quick fixes. The longer enterprises wait, the faster and riskier it becomes when they are forced to adopt AI. By delaying, they have to learn fast how to do it with no experience under their belt. This article explores the significant risks of unchecked AI deployment and offers guidance for navigating the challenges. When AI Tools Go Rogue Remember the UK Post Office Horizon scandal? A conventional software system led to hundreds of innocent people being prosecuted, some imprisoned, and lives utterly destroyed. That was just normal software. The AI tools your organization might be preparing to unleash represent an entirely different beast. AI is like an adolescent—moody, unpredictable, and occasionally dangerous. Consider Air Canada's chatbot debacle: it confidently provided customers with incorrect bereavement policy information, and the courts ruled that Air Canada had to honor what their digital representative had erroneously promised. While in this case one might argue the chatbot was more humane than the company's actual policies, the financial implications were significant. The critical question is: will your AI tool be trusted to behave and do its job, or will it go on a rampage and wreck your business? Learning how to deploy AI with robust oversight is a critical skill organizations must master for successful AI deployments, and not to play Russian roulette. Companies starting now, are getting a significant edge in learning how to control this critical technology. The Zillow Cautionary Tale Zillow's failed foray into real estate flipping highlights the dangers of AI relying solely on past data. The algorithm, confident in its predictions, failed to account for rapidly changing market conditions, such as a drop in demand or nearby property issues—it could take months for Zillow's algorithm to recognize the impact on valuation. Meanwhile, savvy sellers capitalized on this, unloading properties to Zillow before Zillow detected the prices plummeting, costing the company 10% of its workforce. The problem? Zillow's AI was backward-looking, trained on historical data, and unable to adapt to dynamic environments. This same issue plagues stock-picking algorithms and other systems. that perform beautifully on historical data but collapse when faced with new market conditions. If your AI is making decisions based solely on past data without accounting for environmental changes, you're setting yourself up for a Zillow-style catastrophe . To mitigate this risk, ensure your AI's training data represents current and anticipated future conditions. Consider the risks carefully! This is particularly crucial for financial systems, where tail risks are more frequent than models predict. Medical applications, like analyzing skin conditions, are much less susceptible to changing environments, as long as the AI is trained on a representative sample of the population. Startup Corner-Cutting: From Unicorns to Bankruptcy Your vendor might be cutting corners. While they may not be another Theranos, the risk is real. Take the UK tech unicorn that recently collapsed into bankruptcy amid financial reporting discrepancies. It has now emerged that was a fraud, and people using the service are left with orphaned applications. Startups face intense pressure to deliver results, which can lead to critical oversights with inconvenient truths often getting swept under the rug. One common pitfall is bias in training data. When your system makes judgments about people, inherent biases can lead to discriminatory outcomes, and can even perpetuate and amplify discriminatory outcomes. Even tech giants aren't immune. Amazon attempted to build an AI resume screening tool to identify top talent by analyzing their current workforce's resumes. The problem? AWS, their massive cloud division, was predominantly male, so the AI learned to favor male candidates. Even after purging overtly gender-identifying information, the system still detected subtle language patterns more common in men's resumes and continued its bias. If you're using AI to determine whether someone qualifies for financing, how can you be sure the system isn't perpetuating existing biases? My advice, before deploying AI that makes decisions about people, carefully evaluate the data and the potential for bias. Consider implementing bias detection and mitigation techniques. Better yet, start now with an internal trial to see the problems that bias in the data might cause. Those organizations getting hands on experience right now, will be well ahead of their peers who have not started. The Hallucination Problem Then there are "hallucinations" in generative AI—a polite term for making things up, which is exactly what's happening. Just ask Elon Musk, whose chatbot Grok fabricated a story about NBA star Klay Thompson throwing bricks through windows in Sacramento. Sacramento might be bland, but it did not drive Klay to throw bricks through his neighbor's windows. Such fabrications are potentially damaging to reputations, including your company's. How can you prevent similar embarrassments? Keep humans in the decision loop—at minimum, you'll have someone to blame when things go wrong. It wasn't the AI you purchased from "Piranha AI backed by Shady VC" that approved those questionable loans; it was Johnny from accounting who signed off on them. A practical approach is designing your AI to show its work. When the system generates outputs by writing code to extract database information, this transparency, or "explainable AI", approach allows you to verify the results and logic used to arrive at them. There are other techniques that can reduce or eliminate the effect of hallucinations, but you need to get some hands-on experience to understand when they occur, what they say, and what risk this exposes your organization to. The Economic and Societal Costs of AI Failures The costs of AI security and compliance failures extend far beyond immediate losses: Direct Financial Costs: AI security breaches can lead to significant financial losses through theft, ransom payments, and operational disruption. The average cost of a data breach reached $4.45 million in 2023, with AI-enhanced attacks potentially driving this figure higher. Regulatory Penalties: Non-compliant AI systems increasingly face steep regulatory penalties. Under GDPR, companies can be fined up to 4% of annual global revenue. Reputational Damage: When AI systems make discriminatory decisions or privacy violations occur, the reputational damage can far exceed direct financial losses and persist for years. Market Confidence Erosion: Systematic AI failures across an industry can erode market confidence, potentially triggering investment pullbacks and valuation corrections. Societal Trust Decline: Each high-profile AI failure diminishes public trust in technology and institutions, making future innovation adoption more difficult. The Path Forward As you enter this dangerous world, you face a difficult reality: do you delay implementing AI, and then have to scramble to catch up, or are you more cautious and start working on AI projects now. The reality is that your competitors are likely adopting AI, and you must as well in the not-so-distant future. Some late starters will implement laughably ridiculous systems that cripple their operations. Don't assume that purchasing from established vendors guarantees protection—many products assume you will manage the risks. Trying to run a major AI project with no experience is like trying to drive a car with no training. Close calls are the best you can hope for. The winners will be companies that carefully select the best AI systems while implementing robust safeguards. Don't assume established vendors are immune to the risks. Consider the following steps: Prioritize Human Oversight: Implement robust human review processes for AI outputs. Implement robust human review processes for AI outputs. Focus on Data Quality: Ensure your training data is accurate, representative, and accounts for potential biases. Ensure your training data is accurate, representative, and accounts for potential biases. Demand Explainability: Choose AI systems that provide transparency into their decision-making processes. Choose AI systems that provide transparency into their decision-making processes. Establish Ethical Guidelines: Develop clear ethical guidelines for AI development and deployment. Alternatively, an AI consultancy can provide guidance. However, vet them carefully or you might end up with another problem rather than a solution. Develop clear ethical guidelines for AI development and deployment. Alternatively, an AI consultancy can provide guidance. However, vet them carefully or you might end up with another problem rather than a solution. Apply Proper Security and Compliance Measures: This isn't just good ethics—it's good business. In the race to AI adoption, remember: it's better to arrive safely than to crash spectacularly before reaching the finish line. Those who have already started their AI journey are learning valuable lessons about what works and what doesn't. The longer you wait, the more risky your position becomes. For everyone else, all you can hope for is more empty chambers in your Russian roulette revolver. Written by Oliver King-Smith, CEO of smartR AI.