Customers’ expectations of always-on, real-time services and convenience are in stark contrast with the reality of the core systems that serve them, and established institutions are seeing an exodus of customers to providers able to make good on those demands.

The irony is that older banks possess an incredible amount of data accrued over decades, yet are unable to capitalise on that rich seam of information at their disposal. Systems architected 30 or 40 years ago may be proven, reliable and relatively secure, but were never designed to leverage data in the ways now required to guide service design and refinement at the kinds of speeds needed to remain competitive.

Financial institutions have a dual challenge, therefore. Core banking systems need to be replaced or phased out, and the data archives locked into mainframes and legacy storage released to improve customer experience, offer innovation, and take the fight to the new and neobanks that threaten to out-compete longer-established businesses.

Trusted market intelligence organisation IDC recently published papers in conjunction with Thought Machine, a next-generation core banking provider, on the options available to banks reliant on outdated infrastructure. It describes how fourth-generation cloud technologies now offer a choice of migration strategies to replace and/or update core functions.

Source: Shutterstock

Non-exclusive options of progressive, greenfield and ‘big bang’ methods are considered in detail, and the papers assert that an amalgam of these strategies is the most likely to succeed in the majority of cases. All, however, are predicated on the embracing of technologies like interoperability via APIs, microservice-based architecture, low-code compatibility, platform agnosticism and scalability/elasticity.

Using methodologies like architecture-as-code, self-healing and baked-in scalability, banks can develop real-time services and downstream data for analysis, all based on modular architecture that’s secure and resilient. These methods enable fast, iterative development, lowering costs and time-to-market, as well as widening product portfolios (of both customer-facing and business intelligence applications).

Once banks engage in revising their core systems, they can begin to develop an ‘enterprise intelligence’ approach that unlocks their data assets that will inform, with empirical information, long-term strategies unencumbered by the limitations that are imposed at a deep level by aging code. Running on core technology that is based on interoperability and modularity ensures an open future, allowing emerging technologies (machine learning is often quoted in this context) to be implemented – creating the possibility to develop unique and differentiating products.

The emergence of an enterprise-wide data strategy unlocks a strategic treasure-trove of information that helps banks identify, via advanced analytics, key information used to drive innovation, enhance customer experiences, and improve operational efficiencies. The myriad advantages of EI (enterprise intelligence) are explored further and possible roadmaps are outlined in ‘Unlocking Enterprise Intelligence in Banking Systems’ which is available here.

There are clearly challenges on the route to modernisation, not least of which is the maintenance of reliable, steadfast services as work proceeds. The two papers describe the pros and cons of common migration strategies and how institutions might best pick their own course.

It’s important to stress, however, that although there are myriad options, there are a number of ‘givens’, paramount among them being the use of cloud-native methodologies and techniques, even when considering a waterfall, ‘big bang’ strategy. It’s also worth noting that cloud-native methods are not solely implemented on cloud providers’ resources. Like many industries subject to high levels of governance around data security and compliance, many financial organisations adopt a hybrid topology, often to maintain a separation between critically sensitive data (airgapping) or to prevent vendor lock-in, among other reasons. Cloud-native technologies allow for this, and ensure that during and after migration, data governance is strictly observed.

Source: Shutterstock

The agility and scalability of cloud-native confers on platform architects the ability to structure core and ancillary systems however they’re required to be distributed, regardless of hosting platform, remote or on-premise. With the right approach, a bank’s options remain open and it can react quickly to changes in governance, as well as to market conditions, altering topology at will.

Finally, it’s worth circling back to the reasons for overhauling core infrastructure. Replacing old technology with new should not be an empty gesture because of a perceived need to progress. The end goals should remain clear in the minds of decision makers: to enable practical use of existing and future data resources and to create a basis of technology that is adaptable, secure, compliant and agile. On that basis, banks can innovate, lower the cost and time required for innovation, and compete with new-generation financial organisations that operate with little technical debt.

Startup banks and neobanks may come to the table with fewer encumbrances, but they lack the powerful body of historic data spanning multiple products that older institutions have. And it’s data, at the end of the day, that is the one resource that truly empowers.

Read the reports, ‘Driving Innovation Through Cloud-native Core Banking Platforms’ and ‘Unlocking Enterprise Intelligence in Banking Systems’ for fuller discussion of the issues covered here. Discover the cloud-native core banking and data services portfolio from Thought Machine by heading here.

The post Data Strategies That Dictate Legacy Overhaul Methods for Established Banks appeared first on Tech Wire Asia.

Consumers are aware that companies offering cut-price, fast fashion have likely cut corners and abused their supply chain, with horrendous reports of manufacturers’ working conditions making headline news. Consumers increasingly vote with their wallets and deliberately make ethical choices when shopping online. Being an ethical clothing brand and doing so demonstrably is a win-win – it’s a massive competitive differentiator, consumers feel great about their chosen vendors, and workers in the supply chain don’t get exploited.

There remains another challenge, however. If a clothing brand (or indeed, any company) wants to act ethically in every aspect of its activities, it has to examine the ways it provides customer experience.

Creating a customer experience (CX) is a critical area for online companies today, and it hinges on providing personalised and helpful interactions between the seller and buyer. But for the vast majority of the world’s ethical brands, their ethical practice stops here. In previous articles here on Tech Wire Asia, we’ve discussed how many businesses collate third-party data belonging to customers and prospects and form largely inaccurate and privacy-invasive pictures of their customers and prospects. These are then used to create messaging for touch-points that are not only a waste of costly resources but actively alienate users.

Source: Wellbi

This happens when consumers feel that brands know too much about them (which is borderline creepy), are inaccurate (wasted messaging), or are accurate but irrelevant. The latter case is very common: a customer is tagged by third-party data as interested in golf, for example, and so all data-driven fashion suggestions for that customer comprise eye-watering colours.

As part of our exploration into how relevant information, given consensually by customers, can craft customer experiences and the massive advantages this offers, we spoke to Supreeth Kashyap of Wellbi, an ethical clothing brand operating in India. Wellbi carefully sources the hand-woven fabrics used in its range from artisans across the country, paying them a premium rate for their labour and guaranteeing them a consistent commission for their work.

Source: Wellbi

“Basically, I started with the with a vision of empowering rural artisans,” said Supreeth, the founder and CEO of Wellbi. “When I was criss crossing rural India, I found a group of artisans who were producing the finest fabrics, but they didn’t have a good market linkage for the product. They were earning less than three dollars [a day]. Textiles is the second largest employer in India, just after farming and agriculture. […] Before, they were getting work for only two to ten days in a month. Right now, they’re getting work for close to the entire month, 30 days. So now things have changed for them considerably.”

It’s a company that takes its ethical values into every aspect of its operations, including managing its customers’ identities, from which it can create a level of trust in the Wellbi brand that informs the customer experiences it creates.

Source: Wellbi

The concept embraced by Wellbi and many other brands that genuinely care for their customers is that of zero-party data – that’s information willingly exchanged by customers and prospects with the company in a way that ensures their privacy and security. Wellbi uses Holistic Identity Management powered by the Affinidi Trust Network (ATN). You can read more about the concepts of Holistic Identity Management and the ATN in previous articles we’ve published on this site.

By creating a customer experience based on consent-driven data, it formulates the critical description of each individual based on information relevant to the customer’s interactions with the brand. As trust and loyalty build, the picture gains detail, and the quality of CX improves in ways that are non-invasive, secure, and consensual.

Glenn Gore, CEO of Affinidi, told us, “It comes back to a community of like minded consumers working with like minded businesses – people who care about buying organic goods, they’re likely doing it across multiple facets of their life. They’re buying organic beauty products, they’re buying organic fashion. So I think part of [the Affinidi Trust Network] is just connecting brands and businesses that are very focused on that [ethos]. I think there’s a lot of ‘green washing’ out there in the industry for these things. So being able to have that trust network where […] you connect through, saying, ‘We’ve got consumers who want to spend money on these types of goods’. [Consumers] make a conscious decision about investing their money in these types of causes. Businesses act as a marketplace, as gateways that [provide] a discovery mechanism for these very small-batch products. I think is actually what consumers are looking for.”

The concept of zero-party data and the ATN mean that Wellbi can find new audiences that resonate with its ethical ethos and brand vision. With their permission, online shoppers whose personal preferences align with Wellbi can be offered opportunities with similar companies, safe in the knowledge that their personal data is treated ethically.

At present, it’s anomalous that companies that trade on their ethical stance still act unethically when it comes to customer information management, building ‘insights’ from data that they have no consent to use, on prospects and customers whose data is treated with little respect for the values held by the brand and the online citizen.

The poor click-through rate of ‘traditional’ platforms like Meta is around the 2-3% mark, which is indicative of how inaccurate most companies’ perceptions are of their users. With the Affinidi Trust Network, brands can develop CX according to information dictated by the customer, not privacy-invasive and inaccurate data. That promotes an immediate trust on which a meaningful CX can be created. “But there’s not many things we spend money on as businesses where a 98% failure rate is normal. […] You don’t need to shift that needle much. A 1% difference is actually a 50% improvement,” Glenn said.

To find out more about Wellbi and browse its ethical range of menswear, head over to its online store. You can read more about Affinidi Trust Network here and start to create an ethical and powerful customer experience that is truly and accurately personalised.

The post Ethical Threads: Transforming Fashion with Trust and Transparency appeared first on Tech Wire Asia.

To this day, CFOs continue to be at the forefront of technological innovation in the workplace, yet their role has grown considerably in scope. CFOs are now key strategic players in the business, with important roles in regulatory compliance and talent management.

More broadly, the finance team is expected to keep abreast of the economic situation at the macro level – international market conditions, for example – as well as oversee the organisation’s long-term prospects and daily administration. It is no wonder that the finance office continues to embrace the latest technology to keep so many plates spinning.

Source: Shutterstock

Financial Close Process

One of the most critical aspects of a CFO’s role is ensuring a smooth and accurate financial close process. The financial close involves consolidating all financial data at the end of a reporting period, a process that includes reconciling accounts, adjusting entries and preparing financial statements. The financial close process is crucial for accurate financial reporting and strategic decision-making, providing the basis of empirical fact for both internal and external use.

Trintech stands out as a leader in automating financial processes, particularly in streamlining the financial close process. By leveraging cutting-edge technologies, Trintech replaces error-prone manual tasks with automated routines that revolutionise workflows. This transformation frees up resources so staff can address their wider remit in many different areas.

There are several benefits from Trintech’s financial close solutions, specifically:

– Enhanced accuracy and compliance: Automated reconciliation and transaction matching ensure that financial data is accurate and compliant with pertinent regulatory standards. This reduces the risk of errors and non-compliance, which can lead to fines and reputational damage.

– Time and resource savings: By automating routine tasks, finance teams complete the financial close process faster, freeing time for more strategic activities.

– Realtime insight: With realtime access to financial data, CFOs make informed decisions quickly. This is essential for agile financial management in today’s fast-paced business environment.

– Scalability: As businesses grow, the Trintech platform scales to handle an increased volume of transactions and more complex financial structures yet ensures the financial close process remains efficient and accurate.

Conforming to APIs’ and Legislative Rules

Trintech integrates with common software platforms like Workiva, Oracle, and Microsoft Business Dynamics. Integration means relevant information can be collated in formats ready for analysis, improving the efficiency and accuracy of the financial close process.

Changes in legal regulations and their increasing complexity, especially internationally, leave companies open to missteps. Using up-to-date software to identify the shifting legislative requirements means that companies can pre-empt statutory changes and fulfil their obligations in registering and filing all necessary paperwork.

Most businesses face arduous data collation and filing because most necessary compliance work involves evidencing procedures and data. However, automated systems capable of comprehensive data aggregation help finance personnel quickly produce the required information and present it in formats demanded by overseeing bodies.

Adaptable agile software allows the inevitable compliance changes to be adopted without having to dedicate resources to address the needs of multiple regulators. With software from Trintech, CFOs don’t have to sideline resources engaged in more strategically focused tasks, so risking hard-won competitive advantage.

Cost and Spend Management

Controlling spending, lowering rates of incorrect expense submissions, and matching incoming invoices with a company’s records mean that keeping tight reins on monies in and out is more straightforward and less prone to fraud and human error. Digitisation reduces the need for manual paperwork processing. It ensures all staff know the required processes and approvals to access funds, and are aware of expense spend limits & request filing procedures.

In growing businesses, especially those with an international presence, automated systems handle the complexities of managing all aspects of cost control, freeing up finance staff to concentrate on activities that will bring the company more value.

Source: Shutterstock

Considering the Future

In an increasingly complex financial world with more responsibility resting on CFOs, the digital transformation of finance systems is the only way financial professionals can stay ahead of the game and retain a proactive stance.

With the right software platform, the finance function can become more strategic, allowing a new generation of CFOs to lead company strategy using data processing that informs and guides other stakeholders and improves internal workflows. Trintech helps CFOs shape the financial future of their organisation, keep on the right side of regulators, and handle everything the business throws their way!For further insights, explore Trintech’s on-demand webinars and read how Queensland Airports and the Dallas Cowboys improved their financial reconciliation processes, improving visibility & control over operations. There are more case studies over at the Trintech site.

To learn about the wide portfolio of capabilities that Trintech offers, reach out to a local representative to book a demo and discuss how a data-driven approach can transform your finance function.

The post Transformed Finance and Automated Excellence with Trintech appeared first on Tech Wire Asia.

There are, of course, more esoteric offerings from technology companies like augmented reality modelling platforms, drones and imaging solutions, robotics, and IIoT. But it’s important to recognise that the phrase’ core functionality’ is not the same as ‘basic functionality’ in this context. Take, for example, the following list of core capabilities for any piece of technology for the majority of AEC workers:

• Manage drawing sets and navigate quickly through links and labels on a mobile device,
• Make comparisons of revisions quickly and accurately so no changes get missed,
• Reduce time spent co-ordinating and calling project team members because markup and comments can be made by everyone at the same time,
• Invite external partners to collaborate on project documents in real-time,
• Create measurements and take-offs for accurate estimates without needing to click around multiple apps,
• Store information anywhere, wherever it’s needed (from Google Drive to Box to cloud storage),
• Access documents in the field through mobile apps and get deeper access to information at a desk .

Those core capabilities are certainly non-trivial for AEC software to accomplish reliably and easily in industry settings, yet a solution that does is like an irreplacable tool: software that’s focused on achieving essential work, every time.

Focused AEC software is useful for the people who need it, does its job(s) well, and does it/them reliably, day in and day out. If we take as an example the PDF-centred nature of many workflows in the AEC industry, equipping a workforce with some kind of PDF editor/viewer seems logical. But not only does general PDF software tend to ship with multiple features that will never be used, but those capabilities that will come into play are not designed for workflows in construction and engineering. Almost all were created for graphic designers and print shops. (The PDF format is a wrapper around an encapsulated PostScript file, PostScript being the language used by hardware printers.)

For over 20 years now, Bluebeam’s offering has addressed in software the problems encountered in work solely in the AEC industry. It’s iterated on its product over this time, refining tools and functions so they are practical, fast, fantastically useful and simple to use in any environment. It’s the epitome of software that’s focused on a specific vertical. Over the course of its evolution, Bluebeam has taken feedback and suggestions from its user base as to what would work, what doesn’t, and how the platform could be polished. Its features revolve around a PDF workflow, but to compare it in the same breath as, for instance, PDF-Xpress, is like comparing general-purpose Excel with the specialist accountancy software in NetSuite or SAP.

Bluebeam’s tools and features are designed for markup, collaboration, version control, estimation and take-offs, all built in a platform that is simple to use yet solves many of the challenges experienced by people in the industry over the last two decades.

Source: Bluebeam

When we spoke to David Rekker, a senior manager at Bluebeam, he said that his customers, “Learn it in the morning, use it in the afternoon.” The usability piece of the puzzle is a critical part of focused software. A platform like AutoDesk can offer the user too much complexity presented on screen in tools that are sub-optimal on site.

Despite its apparent simplicity, intuitive-to-use software does not mean limited software. The power of Bluebeam can be discovered in lessons from the Bluebeam University, a self-guided training program that helps teams learn features and workflows at their own pace, over and above the basics. Courses have step-by-step videos, interactive exercises and quizzes designed to get the most out of the software.

Practicality also has to mean adaptability. Every company has its own preferences as to where data and work are stored, like it has its own workflows and ways of tackling a project. Rather than confine files to a proprietary ecosystem, users can work with just about any other platform, storing and sharing files wherever they’re needed. “The point here is that users can simply open their PDFs and drawings in Bluebeam, do their work, and save it back to their system. Bluebeam doesn’t force them to keep all their work inside a Bluebeam ecosystem,” Rekker said.

All over the world, Bluebeam is helping create great companies. Over in Dublin, Ireland, ClearTech now wins 50% more contracts than it did before it started using the software, including household name companies like Facebook, Salesforce and Amazon. “The other companies that we work with, they use Bluebeam as well. And we communicate using the same software so we have more concise and accurate comments and reviews on the drawings,” said Thiago Tamm, a structural engineer at ClearTech. The company has now expanded into the APAC and Saudi Arabia.

Closer to home, Anchored Height Safety’s Mark Anderson said, “[Bluebeam] is now one of our key tools for documentation for our quoting markups, installations and re-certifications and for demonstrating to our clients exactly what we have delivered.” The company uses Bluebeam’s markup capabilities and templates to produce quotes, reports and drawings. It’s also integrated with the company’s job management system so workers don’t need to transfer data manually from system to system. Project management information is constantly up-to-date, and there’s better collaboration between people in the field and office teams.

Conclusion
The features we listed at the top of this article as the ideals for a piece of AEC industry software are those present in the Bluebeam platform, ready to start using today, and starting a free trial is the best way to begin. You can find out more about its available tiers and how easy it is to use from the company’s website.

The post Why focused software makes the difference in the AEC industries appeared first on Tech Wire Asia.

The lack of connectivity to the cloud is a core aspect of Enerlyf’s current vision for its CX. “Customers don’t need to share any data and they are still able to get their own personalised temperature and energy saving. That was one of the key things we identified in its popularity,” Chirag told us.

The company’s invention saves its customers up to 35% on their home cooling costs, enough that over two nights, the equivalent electricity could cover two rural households’ energy needs.

The lack of need for potentially privacy-invasive ‘smart home’ technologies comes at a time when data privacy is becoming increasingly central in many consumers’ minds. Data aggregated from multiple sources can be used to influence our behaviour in ways we find disturbing, and when those influences are traced back to a product purchase or service sign-up, the effects can easily negate any investment a brand has made in its CX.

That’s because, at present, there is an imbalance in our current concepts of CX, one that loses companies loyal customers and destroys trust carefully built over months and years.

The unevenly weighted scales

When buying a product or service, and in every interaction with an organisation, we expect a quality CX. It’s part of the reciprocal arrangement entered into at the point of purchase, with the standards of our expected experiences set high by global household names like Uber, Amazon, Rakuten and Ola.

But in addition to the quid pro quo, many companies will additionally monetise or even abuse the data they gain from every interaction. By piecing together information gathered with that from third parties, companies build detailed yet often irrelevant pictures of their buyers or users. Too often, their reasons for doing so have little to do with improving CX or the product now in the customer’s hands. Instead, data is used as a secondary revenue source, the benefits of which never reach the customer.

Source: Shutterstock

Digitisation without tears

Like every product, iterating on Enerlyf’s core designs is how it will improve. To achieve this, Chirag knows that user data can be incredibly helpful for each of his customers. “[We want to] add internet connectivity, IoT capabilities and AI to our systems, so that we can build to make greater systems. When we envisioned adding IoT [functions] and AI, that is where Affinidi came in and [our] mission and vision becomes much stronger,” he said.

Affinidi’s vision for how data can be shared anonymously is central to what Chirag perceives as the next generation of customer experience for Enerlyf customers. With zero-party data (first-party or user data whose source is not identifiable by a third party – see our previous articles here and here for more) the customer experience for Enerlyf’s products can give each customer advantages that will not compromise their personal information.

Initially, Chirag aims to produce personal and community value for Enerlyf users via the Affinidi Trust Network. “So say for example, if there is a community with 1000 apartments, and we have 200-500 users using our product, we wanted to offer access to a local weather station for them. […] For example, if parents want to take their children out, they can immediately check what is the air quality of that area? What is the outside temperature, humidity, so many other things. So that is a roadmap Enerlyf is connecting, like user-personalised profiles with community-level data.”

Affinidi’s CEO, Glenn Gore sees further benefits for Enerlyf user communities that are both empowered and protected by the Affinidi Trust Network (see here for more details): “Air con repair services could do reverse bidding, for example, saying, ‘We know you run your air conditioning for 300 hours, by giving it a service, it’s going to be more efficient, you’ll save some energy.’ These are new techniques that people could use, but while maintaining user anonymity.”

Customer experience at present goes little further than easy-to-use GUIs (graphical user interfaces) and personalised recommendations to buy more product (‘Hey [name], you bought [x], so why not buy [y]?’). What Enerlyf and Affinidi envisage is a CX where the ‘C’ for Customer is writ large – realisable benefits that come from anonymised, specific data deliberately released by individuals and companies to others, with both parties gaining.

Source: Shutterstock

National level advantages

The Indian government’s Green Credits scheme is an example of a nascent market that can be seized on by entrepreneurs like Chriag. He sees the users of Enerlyf products as being able to prove – without compromising their personal information or identities – exactly how much power their activities have saved and be able to ‘spend’ those credits elsewhere.

Glenn said, “There is value in consumers being able to say to brands, ‘Hey, I am taking actions in my own life.’ And brands can turn that into giving you savings or opportunities or preferences based on the decisions being made.”

New definitions of CX

End-users confident of their privacy are so much more likely to trade with brands they know are respectful of data security and anonymity. Customer experience on the Affinidi Trust Network is self-determined by the individual and goes far beyond our present concepts of CX. In 2024, CX is too often designed not for the benefit of the customer, but as a crude and often unwanted opportunity to cross-sell and upsell.

Instead of a reluctance to interact with a brand because of the potential for personal information misuse, brands can build trust with customers and prospects based on information that people approve for release to specific organisations.

As Enerlyf iterates on its energy-saving product line, it’s changing the way we think about personal well-being and energy-saving. With its cutting edge ClimateOS and state-of-the-art CX, Enerlyf allows households to reduce energy consumption and transform into fully-connected, distributed energy resources in the wider energy grid. Doing so offers the potential for enormous new markets and a more sustainable future for all of us.

The Affinidi Trust Network is the basis on which we will see this new data and energy economy built and the new definition of customer experience emerge. You can find out more about Affinidi Trust Network here.

The post Building trust in the data economy: Enerlyf and Affinidi redefine CX, privacy and energy efficiency appeared first on Tech Wire Asia.

Pavel has extensive experience in both finance and advanced machine learning technologies, with a background in data science, analysis, and research. We asked Pavel first to describe what we mean by the phrase ‘fraud network’ and, broadly, how their activities can be counteracted.

“Fraud networks are organized groups or individuals that collaborate to carry out fraudulent activities. [They] exploit gaps where [an organization’s] infrastructure is not connected to transaction or event monitoring. Fraud networks, or fraud rings, are groups of individuals – operating globally or within the same location – who jointly participate in fraudulent activities, such as multi-accounting, money laundering, money muling, and personal data breaches. They collaborate to take advantage of a digital platform like a cryptocurrency exchange, fintech app, dating service, or online casino. By connecting who the person is (with IP addresses, payment details, and so on) to what they actually do on the platform, we can detect and act on fraudulent patterns.”

Although such groups operate globally, there is a greater instance of this type of crime in the APAC region, plus the problem is growing faster there too. Pavel told us that attack numbers are growing about eight times faster than in Europe and the Americas. The issue is not necessarily linked to national economic status: Singapore and Hong Kong are among those countries where fraud networks operate commonly. Singapore, with the highest GDP (PPP) per capita in the world, saw 4,800 people investigated for ‘money muling’ (the transfer of illegally-gained funds on behalf of others) in 2020 alone, according to Singapore Police data. There were 4,700 similar cases in Singapore in the first three months of 2023 alone.

Source: Sumsub

“In Asia, fraud rings are affecting both developed and developing markets alike. For example, Bangladesh has an average fraud network incident rate of a worrying 10.2%, with alarming rates in other growing economies such as Thailand (6.6%), Vietnam (3.7%) and Indonesia (2.2%),” Pavel said.

Of course, the problem isn’t limited to the APAC. In Estonia – arguably one of the most digitized countries in Europe –  a group of several dozen cryptocurrency exchange applicants uploaded identical Proof of Address documents from an unlicensed foreign bank in an attempt to get multiple cryptocurrency-based payment cards issued to the same address.  , Sumsub, discovered the attempt. “This is just one case of how serial fraud operates; other instances include money muling schemes, tech support scams, ransomware, and phishing attacks, and account takeovers.”

Legacy methods of combating the types of fraud we see today depended on only initial KYC (know your customer) checks. Once a new user had passed those, their activity was not monitored. Fraudsters can, in those circumstances, persuade legitimate citizens to ‘lend’ (or sell) their credentials, to then go on to use the account to perpetrate crimes. “Continuous monitoring and analysis ensure that even those applicants who’ve passed initial verification are consistently under observation,” Pavel told us. But there are significant challenges in deploying full-lifespan countermeasures. These include:

• High volumes of data mean manual identity of suspicious activity is resource-intensive,
• False positives can impact genuine users and sour relationships between customers and provider,
• Evolution of methods used by fraudsters means training and adaptation are required constantly by enforcement organizations,
• Regulatory compliance means there are boundaries that simply cannot be crossed during investigation,
• Customer experiences are easily compromised by heavy-handed anti-fraud measures,
• Costs rise and profits take a hit because of the need for fraud prevention.

Pavel Goldman-Kalaydin, Head of Artificial Intelligence & Machine Learning. Source: Sumsub

Pavel told us that today’s machine learning algorithms are a huge boon in fraud detection. “AI is a powerful tool for detecting fraud networks. Should users show signs of fraud after being onboarded, they won’t go unnoticed. Through leveraging AI, companies can implement behavioral analysis and risk scoring algorithms, which were previously challenging to implement effectively. Such advanced capabilities enable continuous monitoring of users’ activities, meaning early detection of suspicious behavior and enhancing fraud prevention measures.”

Sumsub holds an ever-evolving database that has identified and reported over 2 million fraudsters and operates a KYC platform built on over a billion identities. It analyzes more than 5,000 fraud attacks daily, building a comprehensive picture of the digital landscape that examines historical connections and relationships among entities, using ML-powered algorithms.

It’s in proactive, constant monitoring that the company’s platform excels. Its platform pre-screens service applicants using ID verification and behavioral intelligence that adds an invisible layer of fraud prevention. Verification processes detect deepfakes, suspicious device fingerprints, create risk scores, and detect the activities of today’s fraud networks. On an ongoing basis, Sumsub oversees login behavior and looks deeply into account activity using AI that ensures near-zero instances of false positives, guaranteeing a highly accurate and reliable detection of fraudulent activities in real-time.

“We adopt a multi-layered approach, offering a solution with ‘Detect & Act’ capabilities in one platform. This goes beyond traditional anti-fraud or KYC methods to ensure a fortified defense against a wide array of fraud, including account theft, romance scams, payment fraud, and many more. We provide the ability to set action alerts to automatically trigger additional checks, to simplify the decision-making process and reduce false positives.”

Given the sums of money that change hands daily over the internet, it’s perhaps no surprise that fraud networks are constantly developing new methods to launder and steal money from legitimate sources. Sumsub is the only platform that uses technologies that are in advance of those employed by these cyber criminals, preventing fraud and illegal activity in the APAC and across the world.

To find out more about Sumsub, head over to the company’s website and join over 2,000 companies that protect themselves and their customers with preventative measures that don’t impact customer experience or statutory compliance. Sign up for the free demo today.

The post The threat of fraud networks in the APAC: KYC and beyond appeared first on Tech Wire Asia.

And while the majority of companies can only aspire to the same levels of market penetration, what’s apparent is that the quality of customer experience offered by the big players sets the standard by which all are judged.

It’s all too easy for any brand to mis-step in customer engagement, especially given that the journeys customers take with brands are long and, in an ideal world, continuous as companies seek to build loyalty and repeat business.

In previous articles we’ve covered the importance of unifying customer communications by using a CCM platform (customer communication management) to create a coherent, personalised experience at scale both with an existing customer base and prospects. If we take the need for such a solution as a given, choosing a CCM (or migrating to a new CCM platform) comes down to examining the fundamental capabilities of the offerings from different vendors and identifying the business impact the core strengths of each will have on an organisation in 2024.

Source: Shutterstock

The Quadrant Knowledge Solutions SPARK Matrix^™ breakdown of the leading vendors’ offerings in this regard is highly informative, describing the 11 fundamental capabilities that a CCM must offer and highlighting six key differentiators that are especially relevant in today’s business environment. In this article, we want to focus on two standout features that deliver significant competitive advantage for companies that embrace the concept of putting digital technologies and resources at the heart of customer experience building.

Enhanced CX and Personalisation

It’s essential that engagement with customers happens throughout the full lifecycle of the customer journey, and high-impact CX depends on two mutually connected factors: personalisation and channel preferences (the what and how). By giving customers control over which channels they communicate on (how) and ensuring each touchpoint comprises personalised and relevant content (what), brands create better relationships with each individual and establish trust that, in turn, encourages continuing business.

For that to take place, the CCM that oversees each engagement needs to be able to create individually customised content, offering different data in different ways. Blanket messaging down a limited number of favoured channels is a hit-and-miss approach that can easily alienate as many customers as it converts, with potential buyers or service-users looking to competing companies that they feel they have a stronger association with.

An unfocused messaging strategy also sparks fears in a user base that their data is not respected and that their privacy is being abused. Carefully personalised messaging through favoured channels also tells the customer that a brand’s data governance and security measures are in place and observed.

The right data AND the right messaging

Most companies actually use only a proportion of the information they have access to about customers and prospects. Their data sources can be, at best, only partially integrated with the central CCM. With a deeper integration of disparate data sources drawn from right across the business, communication professionals have a deeper insight into what makes each contact tick.

Today’s best CCM platforms draw from rich data sources, using AI tools to sanitise and normalise information to create more detailed pictures of the individual, better to tune messaging at every touchpoint. With a CCM platform like the market’s technology leader Quadient, data can be pulled into the central messaging platform from rich yet uncommonly-used data sources like member portals, helpdesk, or support databases like JIRA, SMS records and email.

Brands using InspireXpress migration technology, for example, get automatic ingestion of data, aided by AI algorithms, from legacy CCMs and templates. The same systems not only normalise all available information so it’s ready for use in communications but also can be used to propose rationalised CCM templates to increase a team’s efficiency and reduce duplicate content. A clear migration path and tools to enable it should be a priority when considering which CCM to deploy.

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Yet whatever the data sources, every message – especially in highly regulated sectors like healthcare and finance – has to adhere to compliance standards to avoid fines and potentially disastrous reputational damage. The CCM platform is responsible for readying data for comms so it complies with evolving rule sets that keep the organisation on the right side of industry-specific governance.

An open and extensible approach to data allows the growth of a CCM over time, extending capabilities with differentiating elements, like BlueRush’s video capability to add enriched experiences to comms, or Salesforce integration for enhanced CRM-based capabilities.

With a host of potential third-party services in the cloud and drawn from a company’s existing IT stack, brands have the capability to go above and beyond their competitors’ offered customer experiences and turn customers into brand advocates.

Next steps

You can read the Quadrant Knowledge Solutions SPARK Matrix^™ report to dig deeper into the 11 fundamental capabilities of a CCM by following this link. The leader in all the paper’s measurements, Quadient, can be found here. If you need measurable business outcomes from your brand communications, you can start building using the best, host-anywhere (on-premise or cloud) solution today.

The post Next-gen CX is based on customer communication management systems. appeared first on Tech Wire Asia.

Microsoft Copilot for M365 is the generative AI that revolutionises organisations’ efficiencies by surfacing strategic insights, finding information, assisting with the curation of content, even summarising and planning your day as efficiently as clicking a button! What is missing from this equation is that deploying the technology isn’t as simple as flicking a switch and ‘going live” with Copilot for Microsoft 365. While the competitive advantage Copilot for Microsoft 365 offers is unparalleled, ensuring the protection of your organisation and its information assets requires more than activating the licenses.

This article examines the necessary preparation organisations should go through to prepare and mitigate potential pitfalls before integrating Copilot for Microsoft 365 into your organisation.

Despite the recent surge in interest in AI over the last 12 months, generative AI is a relatively young discipline as a technology that can engage with everyday users conversationally. Just a few years ago, AI was confined to academic research institutions, the subject of peer-reviewed papers and the research conference circuit. Organisations like OpenAI brought this technology to everyday users and now the workplace has realised the possibilities that context-based GenAI offers, igniting the interest in Copilot for Microsoft 365.

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Copilot for Microsoft 365  allows users and workgroups to automate their day, collaborate more efficiently, be more productive and use collated data from across the organisation to make more informed decisions; all based on information reserves that are continually added to.

While there are benefits generative AI can bring, organisations must address significant concerns regarding security and data governance when embracing such technology.

Here we delve into three steps crucial for the successful implementation of generative AI:

 Step One: Prepare the Environment and Consolidate Your Data

“Make Microsoft 365 the core of organisational information “

Making Microsoft 365 the core basis for business data, and therefore business intelligence is the most operationally logical choice for the majority of organisations. Most IT professionals understand that information becomes much more valuable when it is at users’ fingertips in the most-used platforms, rather than kept  in isolated silos. Migrating data into M365 allows the smart algorithms of Copilot for Microsoft 365 to access relevant information, enabling the AI to enhance its understanding of how your organisation works based on the information available to it.

Fortunately, the AvePoint Confidence Platform helps organisations achieve this consolidation of content through migration, a process which goes beyond a straightforward lift-and-shift.

AvePoint Fly empowers organisations to move from on-premises or remote email to digital collaboration platforms like Google Workspaces, Box, Dropbox, Slack and other collaboration platforms to M365. It discovers and maps existing applications and content, creating migration schedules that minimise operational disruption and downtime. With multiple legacy tenants’ data in one place, businesses can start to better capitalise on their digital resources immediately.

Step Two: Identify and Organise

“Strengthen your data to enable strong Copilot for M365 results”

AvePoint’s Insights and Policies provides the framework to identify high risk content and build efficient and compliant workflows automatically to remediate any potential breaches. Reviewing and strengthening information security provides the ability to establish a solid foundation, encompassing robust cyber protections, identifying areas of sensitive and overshared content and classifying of information according to sensible privilege rules. This is a vital preparatory stage to protect your intellectual property (IP) and ensure your information remains secure and accessible to the right audience.

AvePoint Opus streamlines the classification and organisation of information within M365 (amongst other repositories), ensuring a standard approach to manage content that minimises user intervention. The aim is to ensure information remains accessible and supports compliance with relevant standards and legislation while being available to those who need it.

Simultaneously, organisations should reassess privilege hierarchies and security rules, considering the large investments in solutions like Teams, Groups, OneDrive, SharePoint, Power Automate and so on. Resolving and revising access accumulated access rights accrued over the years, helps bolster security and internal operational efficiency through simplification and consolidation.

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Step Three: Continuous data management

“Ensuring relevant resources”

Tools such as Copilot for Microsoft 365 increase their value to the organisation over time, as they continually improve and refine their capabilities with the accumulation of information.

This is however contingent on the accuracy of your information. Keeping information that is no longer relevant and no longer serves a purpose can impact on the accuracy of machine learning’s findings. Information may represent older products or abandoned work practices that are no longer suitable for the organisation’s current environment.

It is however important to properly store and archive all data, both for compliance and as a source of contextual information for business intelligence with Copilot AI. Here, AvePoint Opus has you covered, keeping data accessible and secure.

Addressing ‘data hygiene’ regularly is imperative, yet many organisations have the resources to manage every aspect of their information assets. AvePoint   automates content lifecycle management by automating the archival of inactive or ROT content and disposing of content that has exceeded its regulatory lifecycle. Furthermore, addressing data hygiene issues also offers a mechanism to reducing storage costs associated with M365.

As outlined in this article, the presence of shadow IT and siloed information can significantly impact the effectiveness of generative AI-driven content curation. Just having access to Copilot for M365 as part of a M365 license does not guarantee quality generate content. Without careful consideration of the organisations information structures, classification methods and content lifecycle management, the potential power of generative AI tools are unrealised.

For organisations committed to realising the maximum benefits of Copilot for M365, we recommend seeking specialised guidance to become “AI ready”. As an early adopter of machine learning technologies, AvePoint has assisted hundreds of organisations across various customer segments to unlock the full potential of generative AI.

With AvePoint’s expert guidance, you can navigate the Copilot for Microsoft 365 journey confidently, leveraging  technology that is specifically designed to deliver optimal results.

To find out more about Copilot for M365 and how it and AvePoint can transform your organisation’s approach to data-based driven operations, contact AvePoint for a demo.

The post 3 Steps to Successfully Automate Copilot for Microsoft 365 Implementation appeared first on Tech Wire Asia.

Any early adoption of an emerging technology that promises a huge market advantage comes with risks. Generative AI promises organizations the potential for significant market differentiation, dramatic cost reduction, and a slew of other pluses, such as improved CX, but its safe implementation is by no means a given. Its dangers include potential resource overrun, customer-facing misfires, and significant PR fallout.

Recent mainstream media coverage of Canada Air’s AI bot mis-step and a New York lawyer’s submission of hallucinated case law show that, at least in the public’s perception, running effective AI instances leaves a great deal to be desired.

Perhaps the disparity between the technology’s potential and its real-world worst-case outcomes is down to the nature of decision-making in large organizations. In fact, an Innovation Catalysts study published this year found that 81% of business decision makers believe there are reasons to exclude the IT department from strategic business decision making. Sure, the IT function has a responsibility to investigate and advocate for technology’s benefits, but it could be argued that there may be broader enterprise concerns that need to be addressed which involves all stakeholders, including IT.

The advantages of deploying AI in workflows for data processing, creativity, and operations are well-known, although every use case varies according to the organization and its approach. But harnessing the technology in production where results are based on local data means considering safeguards around intellectual property and legal & compliance issues, plus the need to embed transparency into the solution. This transparency is important to satisfy regulatory authorities concerned over issues like data processing and sovereignty, as well as customers’ and service users’ concerns about privacy and data practice.

Trustworthy generative AI is a phrase that encompasses a set of smart services and practices that ensure safe operation: trustworthy, legally compliant, and transparent. Building those necessary elements is not a simple undertaking and represents a significant addition to the normal overheads associated with machine learning (compute and storage), and includes extra processes like query & response validation, bias monitoring, data sanitization, and provenance checking.

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Some vendors offer pre-trained models that can form some of the basis of a GenAI solution. But until now, there has been nothing on the market where a solution includes data security, use and development guardrails, manageability, and vendor support. In short, those elements that are mandatory to transform what’s essentially experimental (and therefore has potential risk) into a reliable production-ready platform – which is what Dell and NVIDIA can now offer.

The AI industry is doing its best to address many of the needs of larger organizations that are concerned about some of the potential misfirings that a premature rollout of GenAI could create. NIST’s Artificial Intelligence Safety Institute Consortium (AISIC), for example, has come about to create safe and trustworthy artificial intelligence and comprises more than 200 bodies. It produces empirically backed standards for AI measurement and policy, so organizations leveraging GenAI have guides to safe and legal AI deployment.

NVIDIA, a key member of AISIC, now offers NeMo Guardrails which is designed to support enterprise data security and governance standards, acting as a two-way arbiter between user queries and AI responses.

In enterprise use cases, working with internal data also brings challenges with regard to an organization’s intellectual property. Without proper safeguards, any GenAI instance represents a potential danger to an organization’s ongoing viability. It’s with that challenge and those detailed above that Dell and NVIDIA have partnered to offer a GenAI system that boasts topical, safety and security features, producing the closest to a production-ready, drop-in GenAI solution currently available on the market.

Dell Technologies’ Generative AI Solutions encompass best-of-breed infrastructure designed to greatly simplify the adoption of generative AI for organizations that need the power of machine learning technologies to leverage the value of their digital assets without compromising their ethos, data, customers, or third parties.

Based around the Dell PowerEdge XE9680 GPU-accelerated server, it’s designed for generative AI training, model customization and large-scale inferencing. It comes with NVIDIA AI Enterprise software, which allows rapid deployment of production-ready models in local, hybrid, and remote computing topologies.

The Dell Generative AI Solution range is highly scalable, with hardware that can be expanded according to need, with eight NVIDIA H100 or A100 GPUs fully interconnected with NVLink. The air-cooled 6U devices offer any variation of local and remote deployment at a lower TCO than equitable processing power from other vendors.

With NVIDIA AI Workbench, developers can experience easy GPU environment setup and the freedom to work, manage, and collaborate across workstation and data center platforms regardless of skill-level.

The combination of hardware and software designed from the ground up for generative AI development and deployment, comes with guardrails, data governance, and security baked in. Together, the two mean that organizations can deploy powerful AI-based applications safely and responsibly.

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Building trustworthy generative AI means greater buy-in from business decision-leaders outside IT, as many of their rightly-held concerns around the technology are addressed: transparent development and use, safeguarded IP and customer-facing responses, statutory compliance, and best-in-class operating costs.

To find out more about how the Dell Generative AI Solution portfolio takes machine learning to a fully-viable production setting, contact your nearest representative.

Dell Technologies: https://www.dell.com/en-sg/dt/solutions/artificial-intelligence/generative-ai.htm

NVIDIA: https://www.nvidia.com/en-sg/ai-data-science/generative-ai/

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Perhaps the most groundbreaking change in technology since the birth of the internet is generative AI. Rarely does software have an impact on the business world as much as machine learning algorithms have. Always at the forefront of exploration of the frontiers of tech, the APJ region is already pushing the boundaries of what AI can offer: in media, business intelligence, data processing, marketing, engineering and a hundred other areas.

In one of the largest and widest-reaching surveys on IT in recent years, the Innovation Catalysts study quizzed over 6,000 respondents globally, who gave their answers to a range of queries around innovation, AI, and ML and how their organizations were responding to new technology. (The full survey from Dell Technologies is available here.) The majority of IT professionals (85%) agreed with the proposition that AI and GenAI will significantly transform their industry. And 76% of respondents reported that their organization is already providing intelligent technology in the form of AI optimization software that improves their work experiences.

Given the obvious benefits of AI to all business functions, it’s important, therefore, to understand that access to the compute power and tools required for advanced intelligent algorithms is a prerequisite for today’s businesses. In today’s technology landscape, access to the most advanced AI tools and capabilities can be challenging, especially for businesses looking to innovate and differentiate themselves. The dominance of a few major technology providers has led to a proprietary approach, where the latest AI innovations may be tightly controlled and not easily accessible to a broader range of organizations. This proprietary nature of some AI ecosystems can present obstacles for businesses, especially those in the APJ region, to truly innovate and be creative with AI. They want to use the latest and greatest AI capabilities, but the lack of openness and compatibility between different AI systems gets in their way.

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The need for openness

Developers need open standards to create new uses for AI because it gives them the flexibility to deploy their solutions on-premise, in the cloud, and on edge devices; wherever, in fact, the business’s needs dictate. In parallel with that is the need for compute engines optimized for different devices, capable of delivering AI performance at the point of consumption. Open standards in software and hardware enable interoperability, providing customers the freedom to leverage the AI tools and infrastructure that best suit their unique needs and workflows. This empowers businesses to innovate with generative AI on their own terms, without being limited by a single vendor’s ecosystem.

What the technology industry must pursue, therefore, is a policy of the democratization of GenAI, and those goals are realized by open ecosystems and silicon diversity. Organizations pursuing this strategy of flexibility and choice will gain a significant strategic edge over competitors who primarily rely on public cloud services to manage their AI workloads. This is the key strategy to helping organizations gain a competitive advantage. Empowering businesses with access to the latest hardware optimized for generative AI can further amplify these strategic advantages.

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Powerful hardware

For organizations developing custom AI models and processing large bodies of data, the latest hardware designed from the ground up to be optimized for GenAI significantly lowers TCO, meaning leeway for research and experimentation even within tight budgets.

Better hardware also means projects reach production quicker, and end-users get faster results and an overall better experience. The new Dell PowerEdge XE9680 Server is designed for today’s GenAI workloads. It offers up to eight AMD Instinct MI300X accelerators and provides 1.5TB of coherent GPU accelerator memory per server (the highest ratio in the GPU market currently). That means a lower DC footprint, yet with an increased inference capacity, so very large training datasets can be ingested quickly.

Open software

Hardware power and capabilities unlock an organization’s freedom to innovate without cost overrun, but the software running on it has to offer compatibility with existing AI frameworks, libraries, and models for true portability and compatibility.

Without openness, it’s impossible to achieve that portability across platforms, and therefore, AI can’t be considered democratized. The AMD MI300X Instinct Accelerators in the Dell XE9680 Server, which will be ready to ship in May, offers over 21 petaflops of FP16 performance, yet out-of-the-box, run the common standards in data science of PyTorch and TensorFlow, plus natively supports JAX, Open Neural Network Xchange (ONNX), and OpenAI Triton, inside the AMD ROCm software stack.

ROCm consists of a collection of drivers, development tools, and APIs that enable GPU programming from low-level kernel to end-user applications, and brings together hardware and software optimized for GenAI, large models and fast time-to-market for a business’s AI projects.

AMD’s ROCm is optimized for Instinct MI300X accelerators and is a freely available open software stack that’s capable of evolution and adaptation according to a business’s evolving needs.

An integral part of democratized GenAI is, of course, the open-source ethos. OSS (open-source software) drives quality and excellence, with thousands of users and developers refining and improving the code, allowing increased innovation.

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Better together

Open-source also equates to open flexibility, a situation that means developers can create GenAI-based products and services that operate on a range of devices with upstream support that is available from hundreds of the open-source projects that dominate the GenAI world.

The ultimate flexibility possible today is provided by the combination of the Dell PowerEdge XE9680, AMD’s Instinct MI300X accelerators’ 3rd Gen AMD CDNA (Compute DNA), and ROCm 6 software. A firm foundation and open portability allow businesses and organizations the tools and infrastructure needed to innovate at this critical juncture in technology’s evolution.

GenAI’s transformative powers offer APJ businesses a unique opportunity to develop the next generation of AI-powered software outside the constraints and deliberate roadblocks placed by big tech’s policies of separation and compartmentalization. The horizons can open with just a straightforward deployment configuration tailored to their needs and running on optimized hardware.

To find out more about the Dell PowerEdge XE9680, head to these pages according to your geography: Australia, New Zealand, Singapore, or India. Plus, you can head here to read more about the AMD Instinct MI300X accelerator at the heart of Dell’s next-gen AI-focused hardware.

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