The United Nations reports that the estimated amount of money laundered globally in one year is 2 – 5% of global GDP, or US$800 billion – US$2 trillion, if you’re keeping track. Due to the clandestine and nebulous nature of money laundering, it’s difficult to estimate the total amount of money that actually goes through the money washing cycle.

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As such, financial regulators around the world continue to advocate anti-money laundering and impose high fines on banks suspected of conducting these illicit activities. While technology like blockchain and AI have enabled more visibility in transactions today, money laundering is still affecting global economies around the world.

In Southeast Asia, Singapore’s central bank plans to create a digital platform to enable banks to share information on customers and transactions as part of its anti-money laundering efforts. According to a Reuters report, the Monetary Authority of Singapore plans to launch the platform in the first half of 2023 and make it available to the city-state’s six biggest commercial banks during its initial phase.

Over in Thailand, Bangkok Bank has deployed a solution that integrates and consolidates anti-money laundering (AML) processes across its 300 branches worldwide. Developed together with tech company SAS, the AML solution leverages SAS Anti-Money Laundering to establish a global standard of compliance, crossing 14 economies that include eight other Southeast Asian markets, among them China, Hong Kong, Japan, Taiwan, the United Kingdom, and the United States.

“Money-laundering is a serious and growing challenge for the world’s finance system, and financial authorities around the world have put in place stringent requirements for banks to tackle it. It is a global problem, requiring a global solution,” said Suteera Sripaibulya, Senior Executive Vice President of the IT Division at Bangkok Bank.

She explained the various rules-based anti-money laundering systems the bank had in place around the world were still adequate for compliance with each local regulator’s requirements on a case-by-case basis, but the bank also recognized the need and opportunity for transformation.

(Source – SAS)

Using advanced analytics tools for anti-money laundering

 To develop the AML solution, both Bangkok Bank and SAS collaborated to replace the bank’s legacy systems with more advanced analytics tools. The project involved auditing each location’s regulatory needs and banking activities and incorporating global best practices and guidelines. The audit yielded the most comprehensive and relevant list of business requirements and helped define minimum AML standards for the bank in its entirety.

The bank had to make several changes during the development of the solution. This included having new resources and data sources, as well as new ways of working among compliance, business, and IT. The bank instituted a strong data team to ensure the model functions effectively. A skilled technical team operates data extract, transport, and load tools to give enough focus for data-mapping exercises.

These joint efforts culminated in a SAS AML Global Image, which was piloted at the bank’s Hong Kong operations and later rolled out globally. The Global Image enables Bangkok Bank to apply a more advanced, score-based approach to risk-rate its customers. The bank can apply scenarios and risk factors to detect potential suspicious activity against threshold values specific to each segment based on customer type, risk level, and product.

With the global solution implemented, AML risk management and decisions are strengthened by a standardized AML case investigation workflow across all global operations, helping ensure fully AML-compliant client services worldwide.

For Andy Zook, Senior Vice President of Asia Pacific at SAS, the SAS Anti-Money Laundering solution is the third SAS solution deployed at Bangkok Bank, following SAS Customer Intelligence and SAS Fraud Management.

“We have helped simplify and consolidate the bank’s AML infrastructure and process while delivering a complete set of technical tools spanning the full implementation lifecycle – from early development and testing to the deployment through the monitoring and maintenance of the solution,” explained Andy.

The AML solution met the functional, process, and technical-related requirements set by the bank through its analytics-brokered risk mitigation, which in turn strengthens the bank’s AML capabilities with more advanced analytics techniques. Integrated AML and customer due diligence case management workflow capabilities have also enabled bank staff to improve their productivity, reducing case resolution times.

The post Bangkok Bank leveraging data in its anti-money laundering solution appeared first on Tech Wire Asia.

The Covid-19 pandemic has profoundly impacted and completely transformed the way organizations function — so are companies digitally ready to deal with the challenges and opportunities? 

As IT departments started deploying new technologies, upgrading existing solutions, and keeping a distributed workforce secure, new challenges arose, especially when it comes to security linked to remote working.

A global survey by ManageEngine sought to find out the effects of remote working on cloud usage and organizational security. It also investigated trends related to AI, business analytics, and technology adoption by users across the US, Canada, UK, and the Asia Pacific.

A total of 1,210 qualified executives and technology professionals were polled — and all participants were directly responsible for IT and procuring business technologies.

The lowdown on being digitally ready

8 out of 10 IT professionals report that the pandemic has led to an increase in cloud usage. 83% of respondents revealed that remote workers increase security risks. Only 56% of companies have changed their security strategy—despite remote employees being directly targeted more often.

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The popularity of AI continues to flourish with 81% of organizations reporting that their confidence in AI has grown over the last two years. Due to the pandemic, technology professionals are increasingly prioritizing cloud security, VPN usage, and remote worker support.

Cloud reliance is increasing

Globally, Cloud customers are looking for improved security, performance, and reliability from cloud solutions — with 96% of companies saying they will be sticking to remote working for at least the next two years.

India is the only region where public and private cloud usage was among the top three skills acquired during the pandemic, with 97% of organizations reporting that their reliance on cloud had increased, compared to the global figure of 83%.

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Private cloud was the top-learned skill in Singapore, with 50% of respondents equipping themselves with knowledge about private cloud. 86% of respondents say their organization’s reliance on the cloud has increased, compared to the global figure of 83%.

However, in Australia and New Zealand (ANZ), 42% of respondents stated that ‘improved compliance’ will increase their organizations’ confidence in cloud-based solutions. When asked about the new tech-related skills learned due to the pandemic situation, only 22% of respondents chose compliance.

Is security a bottleneck to being digitally ready?

Security threats have increased, making it difficult for many organizations to handle phishing, malware, and other network endpoint attacks. Admittedly, 83% of respondents believe that remote workers increase security risks.

However, 78% of companies fail to control applications and services employees use — only 56% have adopted a security strategy for remote workers. 52% of organizations report that phishing attacks have increased due to the pandemic.

Only 30% had to learn about VPN in India — well below the global average of 35%. However, 48% of companies have implemented zero trust networks, as opposed to the global figure of 31%.

Security and remote worker support were among the top three acquired skills in Singapore. However, online meeting tools and mobile apps are particularly likely to be purchased without IT team approval, similar to India. 

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In ANZ, roughly 48% believe that social media-based attacks were the most common, and only 37% of respondents stated that implementing a zero-trust network model was a key security action adopted by their company.

AI and analytics bearing fruit

IT teams are increasingly relying on AI-based tools and analytics software (89% globally) to inform their business decisions. 64% of organizations have reported using business analytics to improve decision-making.   

Respondents in India resoundingly think AI will meet business expectations, with 91% saying that confidence in AI has increased over the last two years. 93% of Indian respondents say AI has delivered measurable business results—compared to 71% globally. 

Notably, Indian respondents have experienced improved customer experience, operational efficiency, and business analytics. Interestingly, competition and profitability are driving the usage of business analytics in India, something not seen as much in other regions.

In Singapore, “increased competitiveness” and “reliability” were key factors leading to the adoption of new technologies, more than respondents from any other region. Desiring better decision-making, top reasons for increased analytics use include using available data (69% vs 61% globally), and more efficient and faster decision-making.

In ANZ, 63% of respondents are using AI to make the most of their available data. 89% of the respondents believe that the use of business analytics in their companies increased over the last two years.

Being digitally ready is not just about adopting tech

Remote working is not going away — and according to 83% of workers and experts, a hybrid work model incorporating both remote and on-site work is expected to be the new norm in the near future.

With this in mind, it is imperative that companies ensure that their adoption of technologies such as cloud and AI is well-supplemented by strong and robust cybersecurity practices.

One way is to adopt better cybersecurity practices such as zero-trust, identity-first security, privacy-enhancing computation, and using breach and attack simulation (BAS) tools, among others.

A zero-trust strategy is a necessary proactive approach that may just save businesses. As small and medium enterprises (SMEs) are often targeted by cybercriminals due to minimal cybersecurity protections, a full suite of cybersecurity protection may be costly. Such enterprises may consider enlisting the services of managed security services (MSS).

MSS companies offer security services such as managing firewalls, intrusion detection, VPNs, vulnerability scanning, and anti-virus services. Importantly, they can also help SMEs adopt a zero-trust approach to security.

The post Are APAC companies digitally ready? It’s a mixed bag, says survey appeared first on Tech Wire Asia.

E-Commerce is big business in Southeast Asia (SEA). With movement restrictions coming and going so often in the region, consumers have increasingly moved towards digital means of retail, from fast-moving consumer goods (FMCG) to electronics, to fashion.

Whilst segments like electronics are often perceived to be popular, statistics show that the fashion segment of e-commerce, surprisingly, gets the lion’s share of the multi-billion e-commerce pie. 

The SEA digital fashion industry reported a 22% GMV growth in 2020, with a projected market volume of US$ 18 billion for 2021. These impressive stats suggest a massive opportunity in this e-retail sector for brick and mortar SMEs to digitalize their services to tap on this growing market.

E-Commerce in Southeast Asia

SEA is a hotbed of online retail, with multiple e-commerce platforms serving different markets. Prominent players include Shopee, Lazada and Tokopedia.

All are or intending to serve the SEA market at large, with Tokopedia recently merging with delivery giant Gojek to form an e-commerce, delivery, and financial services giant.

Elsewhere, smaller players, usually brands, have taken to selling their products online due to easier access to customers, and cheaper operating costs.

Popular Thai fashion brand Pomelo entered the fashion e-Commerce scene in 2013, offering a hybrid brick and mortar and online marketplace fashion and lifestyle service to consumers in Thailand. They eventually expanded their digital offerings to the rest of Southeast Asia. 

Leveraging the power of big data

When it comes to goods that rely on unstable trends, such as fashion, demand forecasting is a vital component to drive efficiency in operations and management. 

The fashion industry is notorious for suffering from demand uncertainty, especially with a lack of historical data and ever-changing trends. These often result in overstocked apparel, which retailers may be forced to sell off at loss, in order to make way for more current trends on the shelves.

Pomelo CEO David Jou, who co-founded Lazada Thailand, realized the opportunity to leverage the massive customer, sales, and historical performance marketing data collected since 2013.

Recently, Pomelo launched PRISM, “an end to end brand solutions platform that aims to provide total solutions for brands to scale their business”. The platform offers a comprehensive suite of tools and services from merchandising, to analytics, to marketing, to even logistics to help smaller fashion retailers achieve better economies of scale.

They also employ a team of data scientists who can help brands identify the best potential customers through their various technological solutions including big data.

Machine learning and AI drives powerful insights

In an exclusive interview with CEO David Jou, Tech Wire Asia sought to better understand the technology powering a crucial part of their newest platform — its demand forecasting service.

According to Jou, their demand forecasting service is based on a proprietary machine-learning (ML) engine that took 18 months to develop. Machine learning is a subset of artificial intelligence (AI) that enables machines to process information and execute automated decisions in a more accurate manner. 

The engine plays heavily into supporting both the customer and supply chain sides. On the customer front, it offers personalized services based on the user’s behavior on their website and mobile application, taking into account “hundreds of millions” of data points which are then fed back into the engine. 

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On the supply chain side, the ML engine uses predictive analytics to analyze data points from products and matches them to the movements of similar products elsewhere in the world in order to accurately understand and predict their popularity. 

Prism feeds its ML engine with data from both their online e-commerce platform Pomelo, as well as from their brick and mortar stores around Thailand. This information will then provide demand forecasting, which will inform inventory and merchandize planning, as well as product development.

“We have done a variety of tests, with very accurate results north of 85%. We’re all about getting the best quality data and figuring out which data points are the biggest driver of sales.”, shared Jou.

Most of the platform uses cloud computing from Amazon Web Services (AWS). However, they have yet to introduce edge computing into their digital infrastructure.

On data privacy and cybersecurity

When queried about their cybersecurity and data privacy practices, Jou shared that they have a separate team that works on data, with a focus on ensuring the right permissions are granted. 

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He asserts that customer data is strictly obtained from their mobile apps and online store, and does not track user activity elsewhere, such as on social media or external websites.

Additionally, they anonymize customer data before feeding them into their ML systems. Since Pomelo’s inception in 2013, they have yet to face any data leaks or issues. When we asked who their existing clients are, Pomelo declined to answer. However, Jou did state that, down the road, some authorized case studies of clients will be released.

Machine learning and AI also powers the supply chain

On the supply chain end, Pomelo doesn’t own nor operate factories, and instead, purchases from third-party manufacturers.

Currently, Pomelo does not plan to incorporate any internet-of-things (IoT) solutions in their supply chain nor brick and mortar stores.

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“We utilize a proprietary stack called Henry to automate processes. It manages everything from forecasting, prototyping, material sourcing, and even automated bidding with factory partners. We try to digitize our operations as much as we can, with our approach based on software”, he added.

At time of writing, Jou shared that the company intends to shortly launch across all markets in SEA, with a focus on onboarding fashion and lifestyle brands, with a long-term focus on selling sustainably produced fashion.

He also indicated that there will be opportunities to bring on board makeup, cosmetics, and skincare brands to the platform as well.

The post Thailand’s Pomelo is revolutionizing SEA’s fashion industry with machine learning and AI appeared first on Tech Wire Asia.

The use of emerging technologies in the biotech market is growing, with a market valuation of US$752 billion in 2020 alone. This is especially true in the Asia Pacific (APAC) region, whose biotech market is expected to expand even faster, with a CAGR of 16.8% up to 2028. 

This speed can be attributed to improvements in healthcare infrastructure, supportive government policies, clinical trial services, and epidemiological factors. 

In the US, over 78% of the healthcare sector intend to or are already implementing digitalization efforts using cloud and other emerging technologies.

There is also growing interest by foreign companies to work or collaborate closely with regional APAC biotechnology players, making the region’s biotech market ripe for investment.

Insilico and its value proposition

Yesterday, Hong Kong-based AI-powered biotech startup Insilico Medicine raised another US$255 million, led by Warburg Pincus and over 25 expert biotechnology, pharmaceutical, and AI investors. These include Sequoia Capital, Lilly Asia Ventures, and Baidu Ventures. Since its inception in 2014, this brings their total investments up to US$310 million.  

Insilico’s innovative and unique services revolve around the heavy use of deep learning to drive their efforts in drug discovery and development. Deep learning (DL) is a subset of machine learning (ML) under the umbrella of artificial intelligence (AI).

Insilico and technological disruption of the pharmaceutical industry

Traditional drug development from discovery to market exists on a continuum, divided amongst three to four stages, depending on the manufacturer. 

It takes approximately 10 to 12 years to achieve that, with most of the time spent on clinical trials. Complicating that, only about 14% of drugs manage to get regulatory approval (such as from the Food & Drugs Administration, or, FDA), with costs averaging US$2.8 billion.

As such, speed is the biggest challenge for pharmaceutical companies, and also why novel drugs cost so much while their patents are active. 

Before drugs can reach the clinical stage, it has to undergo several rigorous steps to demonstrate its strong efficacy and safety for clinical trial organisms, be it animals or humans. 

Typical development processes up to the nomination of a preclinical candidate can take approximately four and a half years or about 53 months. 

With Insilico’s method, it can shorten the process by as much as 66% (which is 18 months, or, one and a half years). Not only that, but it can also drastically reduce associated costs by 90% as compared to similar programs.

The novel system by Insilico will save up to 66% of preclinical process time. Image by Insilico.com

Powering drug development with AI

Insilico has built a strong, AI-powered, end-to-end drug discovery platform, which, according to their CEO, Dr. Alex Zhavoronkov, has taken them seven years to develop.

The platform includes PandaOmics™, an AI-powered novel target discovery engine; Chemistry42™, a deep generative reinforcement learning system (which allows for de-novo design of novel molecules with the desired properties that do not exist in the known chemical space), and InClinico™, which predicts clinical trial outcomes.

PandaOmics is a biology-solving engine, whereas Chemistry42 is a compound-generating engine. Both these engines have been built on years of modeling large biological, chemical, and textual datasets using deep learning. 

Insilico is the world’s first company that has managed to use AI to identify a novel target for a major pulmonary disease. How it works is that the algorithm generates novel molecules for that novel target. 

The novel target generated by PandaOmics presents a significant breakthrough and is relevant for a broad range of fibrosis indications. Chemistry42 then uses the newly discovered target as the basis for the structure-based design of a first-in-class novel small molecule inhibitor. This then completes the preclinical experiments required to nominate a preclinical candidate.

Next steps for Insilico

Since the launch of Chemistry42 in September 2020, seven out of the top 30 pharmaceutical companies have deployed its software, including Merck KGaA and UCB. 

Additionally, Insilico has deployed a drug discovery team and platform in China to work on multiple therapeutic programs targeting novel, difficult, and previously undruggable targets.

“Over the last two years, we have built an AI-friendly drug discovery team and a “frictionless drug discovery” ecosystem of about 80 contract research organizations and partners like the cloud robotics provider Arctoris, that specialize in specific assays, to generate disease-relevant machine-learnable proprietary data and to develop our own drug discovery pipeline”,

shared Dr Zhavoronkov in a message spotted by Tech Wire Asia.

He also stated that the company is preparing to innovate clinical development (which takes around seven or more years) using a similar approach. 

Fred Hassan, former Chairman, and CEO of Schering Plough Corporation, commented on behalf of Warburg Pincus,

“Artificial Intelligence and Machine Learning is a powerful tool to revolutionize the drug discovery process and bring life-changing therapies to patients faster than ever before.”

In the meantime, Insilico is ready to license its AI platform to pharmaceutical and biotech companies for on-premise deployment or via SaaS to expand their own AI-drug discovery processes.

The post Asian biotech startup Insilico is disrupting the pharmaceutical industry using Artificial Intelligence appeared first on Tech Wire Asia.

Of interest is how artificial intelligence (AI) might feature in human resources (HR), a field not generally associated with intelligent automation. Contrary to popular belief, AI is not a new technology. From its first proof of concept in 1955, it has evolved tremendously since. Buzz aside, AI can impact organizations in profound ways, especially when coupled with novel problem-solving approaches. 

The AI hype has enthralled many businesses, most prominently in how it proffers automation of processes, thus improving efficiency and lowering costs. Additionally, AI confers powerful analytics capabilities, optimizing decision making, reducing human error, and improving customer responsiveness.

Applying Artificial Intelligence in Human Resources

At first glance, it may sound counterintuitive to have an artificial intelligence in a human-centric field, but if AI can automate processes and provide insights, it stands to reason that these can be applied to human resources too.

A 2019 survey by Gartner identified three ways AI would impact HR, namely: in talent acquisition; voice of the employee (VoE) analytics, and HR virtual assistance. 

AI in talent acquisition is rather controversial, especially regards the fear that AI will replace humans in the decision-making process as well as introduce bias and discrimination as in the case of Amazon. Thankfully, HR personnel now use AI for ‘labor market analyses, competency identification, skills matching, and bias detection in job descriptions and candidate ranking’, according to Helen Poitevin, Research VP at Gartner. Furthermore, AI in recruitment is known to be precise, accurate, and fast.

Voice of the employee (VoE) analytics can monitor employee sentiment, helping to inform decision making in workplace improvement policies, whereas HR virtual assistants can optimize the front-end of employee-related HR processes (for instance, via employee helpdesk services). 

Key considerations for SMBs 

Implementation of AI in HR processes for small-to-medium businesses (SMBs) is affected by different barriers, priorities, and opportunities. Limited by smaller budgets, pricier AI options are often out of reach for them. Additionally, SMBs tend to lack HR departments, with many opting to outsource or automate HR processes, such as using payroll solutions. 

However, SMBs benefit from a shorter chain of communication between the different strata of staff. HR processes tend to be faster and rely on organic human interactions versus the complicated layers of processes common in larger companies. As such, SMBs look for optimized organizational fit, making teams tightly-knit and agile. It is precisely this sort of ‘small company’ feel that larger companies try to imitate using AI.

What’s the best approach for SMBs?

Anne Bailey, an analyst at Kuppingercore, suggests that SMBs focus on using AI to handle two issues: Recruitment, and HR visibility. Leadership Strategist Jeanne Meister also advises companies to improve the employment and employee experience by identifying the right business problems to be solved using AI. 

As with all businesses big or small, talent acquisition is a time-consuming and tedious process. Sourcing for qualified talent due to smaller candidate pools can be a challenge, and a lack of HR visibility within the organization can affect employee performance and retention.

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Recruitment

Within recruitment, AI tools can quickly source for potential candidates across job portals or internal databases, or optimize marketing efforts for open roles. It can also quickly scan through hundreds of applicants, shortlisting best-fit candidates through techniques such as resume parsing, or behavioral and skill assessments, while at the same time, reducing hiring bias. 

Predictive performance, a feature of skill assessments, is more efficient at matching candidates to available positions, which can cut down on the time spent to manually screen applicants prior to interviews. 

Increasing HR Visibility

HR visibility is crucial, but often overlooked. AI can transform the employee experience, and improving visibility helps in employee management and strategy, viz monitoring/analyzing sentiment and feedback from staff. It can also analyze company policies and practices, track employee performance and activity, and manage payrolls, amongst others. 

Ultimately, the adoption of AI in HR depends greatly on how HR personnel are trained and aware of AI, as well as identifying the needs and priorities of the SMB in order to find the right tools to deliver the appropriate services.

The post How will SMBs gain from artificial intelligence in human resources? appeared first on Tech Wire Asia.

The banking industry has undeniably experienced many shake-ups. Technology has brought about the digitization of money, opening up numerous possibilities for fintech innovation, capturing value-added data insights while we’re at it. In and around Asia, fintech – the usage of technology to improve financial services – is booming.

Technology-led financial services are set to bring in US$11 billion in revenue in Southeast Asia (SEA). By 2025, it will account for 11% of total financial services in SEA, itself considered the fastest-growing region worldwide.

Asian fintech companies such as China’s Ant Finance and Indonesia’s OVO are already dominating the fintech landscape and despite setbacks like Ant’s failed blockbuster IPO, are showing no signs of slowing down.

As consumers are used to the digital experience offered by companies such as Google, Amazon, Facebook, and Apple, they expect the same level of customer experience from their financial services providers. Fintech provides a very appealing alternative to traditional banks, as they provide customer-centric solutions that incumbent banks cannot provide.

At the recent Fintech and Blockchain session, Revolut’s COO, Richard Davies, the COO of global fintech super app Revolut that has been spreading its wings in APAC, stated that fintech is already poised to bring major economic benefits globally. According to Davies, there are a few key reasons why fintech is appealing to consumers.

1. Price

Davies noted that 27% of consumers opt for fintech services because it is significantly cheaper compared to incumbent banks. An example is cross-border payments. Often, fintech offers lower foreign exchange rates, and does not come with any hidden fees.

2. Efficiency

For the tech-savvy consumer, fintech services are more efficient compared to those offered by traditional banks. Fintech apps, for example, often come with user-friendly interfaces that would seamlessly guide a consumer through the process that they are undertaking.

20% of consumers opt for fintech services because of the ease of setting up, with 12% of consumers siding with it because of better user experience.

3. Fintech product innovation

Because fintech companies do not need to contend with legacy staff and systems, they can afford to innovate relevant products quickly and cheaply.

Also, fintech companies often have a dedicated focus on fixing specific problems, and can, therefore, come up with tailor-made solutions for their customers. This is rarely possible for legacy banks as there is little room for agility –solutions are usually provided in a one-size-fits-all form.

Davies believes that we have already reached an inflection point for the mass adoption of fintech. This couldn’t be truer for Asia, whose fintech landscape is already vibrant and highly competitive. Regulators in the region are supportive of fintech, as can be seen in countries such as Singapore and Thailand.

Both countries have agreed to open up regulatory sandboxes that allow fintechs to test their solutions prior to bringing them into the market. Further, Singapore and Malaysia are also giving out virtual banking licenses that can make the operation of neobanks easier.

Ultimately, traditional banks need not fear technology. The willingness to adapt and collaborate with others will enable technology to serve all parties – banks, fintechs, and consumers- in the financing sector well.

The post Can fintech innovation really move economies forward? appeared first on Tech Wire Asia.

While leveraging cybersecurity software can be deemed effective, but MIT professor Daniel Weitzner believes that pooling insights on cybersecurity cases will profoundly impact the way companies choose to protect themselves. The way in which CISOs treat threats and strategize protective measures need to change for the better – this means their attitude and mindset as well.

According to the expert, there are several prominent issues that are straining companies from effectively tackling cyber threats and securing their data. However, a viable solution is within reach if companies start working with each other within similar industries by sharing intelligence, insights, and experience dealing with risks or attacks.

To begin with, companies do not have a holistic understanding of the different threats that exist and the risks that are present within the proximities of their existing systems. This will then cause most companies to invest unstrategically in security products or innovative solutions – and the results may not always be as impressive, too.

Weitzner commented, “When you have an honest talk with CISOs and ask them much they spend on security and if it’s enough, they tend to make comparisons to their peers (rather than assess the effectiveness of the spend). No one is able to measure the ROI on different cybersecurity defensive approaches.”

For this reason, the expert has lobbied for companies to pool information on cybersecurity strategies and measures – which he is actively doing through a cyber-intelligence project with 10 large enterprises. Among intelligence and insights that are being gathered include records of attacks and threats, ineffective or failed defense strategies as well as the cost of the failures.

While it is understandable that companies resort to pursuing the latest solutions marketed because providers make great promises, it is more effective to compile data from peers to drive better defense decisions. He added, “We are gradually moving to a point where we will be able to make concrete formal claims about what kinds of defenses are effective and which ones are not.”

Although this method is noticeably rare, the expert believes once insight pooling is practiced, companies can steadily structure a sound defense system. The pooled insights and experiences can then, be streamlined into data that can be integrated into artificial intelligence (AI) capabilities.

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This is why the expert also draws attention towards companies developing greater trust in AI because the solution will be a fundamental proponent in combatting cybercrimes as we move forward. AI is gradually becoming a reliable tool in mitigating cyber risks and countering attacks as it is faster, smarter, and drives better decisions – especially when it is fed with a rich amount of insightful data.

While companies do not generally have high trust in AI because they do not understand how the tool comes up with a decision or solution to a problem.

One way to resolve this is to develop a thorough understanding of the operational mechanism of AI and particularly, how it would work within the context of cybersecurity. With a more defined understanding of threats, availability of insights and higher trust in AI, companies can ultimately devise an effective defense mechanism to protect their data and digital platforms.

The post Expert: To ensure security, companies must rely on insights and trust AI appeared first on Tech Wire Asia.

These are not just empty claims as a survey by the MIT Technology Review revealed that a staggering 80 percent of healthcare providers are already reaping the benefits of AI tools and seeing results in expanded revenues. While it has been established that AI can help automate processes, augment analytics capabilities and empower specific data-driven applications, medical facilities have further explored how AI solutions can be integrated cohesively to create a network of connected clinical systems.

This, in turn, allows providers to work efficiently and increase collaboration between colleagues and between man and machine. According to the survey, facilities with full AI deployment allow providers to spend 68 percent more time collaborating with other staff as the network of connected systems allows constant access to real-time data.

AI-enabled automated administrative processes is an important part of establishing this synergy as it relieves providers from the burden of data entry workload during consultation time and enhances medical diagnosis.

With real-time analytics capabilities, medical professionals can work alongside intelligent systems to produce accurate and insightful diagnoses during consultations. When tailored properly, AI can also be programmed to automatically schedule clinical follow-ups or medical appointments based on the needs of patients which eliminates the time staffers manually spent allocating slots.

Staff also collaborate effectively because automated systems shift the dynamics from providers having more focus on meeting the demands of manual administrative systems to having intelligent, connected systems work for providers.

Once a more collaborative workforce is established, providers can now scale AI-powered connected systems to process patients’ data and augment predictive analytics in driving smarter decisions. Using advanced analytical tools, data-justified predictive capabilities, and healthcare professionals’ expertise, both medical and operational decision-making processes can be more accurate as well as precise.

This is further supported by 93 percent of providers who agree that AI boosts the speed and accuracy at which patient data is analyzed and shared. As a result, patient care and treatment can be delivered more efficiently and speedily without the need to wait for manual data updates and analysis.

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The future of healthcare will see greater prominence of “human touch” inpatient care, and improved processes when medical systems are powered with AI solutions. Cardiologist Bijoy Khandheria says it best, “humans are not going away; they are just going to make smarter decisions, with fewer errors.”

The post AI fuels collaboration and smarter decisions in healthcare appeared first on Tech Wire Asia.

Insurers have had to tiptoe around covering enterprises for cyber risk protection for years now, but a new tie-up between Google Cloud, Allianz, and Munich Re is providing companies with an avenue to make cyber insurance more mainstream and embed it into cloud services.

Initially to be targeted at US-based companies with annual revenues of between US$500 million and US$5 billion, the two insurance companies headquartered out of Munich, Germany will cover up to US$50 million in potential online losses for Google Cloud customers.

“We didn’t want to overreach coming out of the box. But the plan is obviously to expand this offering both up and down the revenue scale very shortly,” said Bob Parisi, the head of cyber solutions in North America for Munich Re, in response to the online insurance policy being made available to firms outside of the US.

The new initiative dubbed the Risk Protection Program will look to reduce cloud security risks and offer an enhanced, yet still-evolving cyber insurance scheme targeted at Google Cloud users. Phil Venables, the vice president and CISO of Google Cloud, claimed in his blog post announcing the Program that the stated aims of the partnership were “to reduce risk, potentially reduce costs, and build further trust in our platform” among its enterprise clientele.

Gartner Research says that by 2024, nearly half (45%) of all IT spending will shift from traditional on-premise solutions to the cloud. As the use of cloud services ramps up, expanding to sensitive workloads and new data types, cloudified companies will need more assurances than ever that their use of new cloud services is integrated tightly into their overall risk management plan.

The essential components of Google’s Risk Protection Program at the outset will feature Cloud Protection +, a specialized cyber insurance policy exclusively for Google Cloud. To provide the necessary data to empower informed decision-making, there is also Risk Manager, a reporting and diagnostic tool that makes a security posture report available to Google Cloud customers by request, in order to manage and measure risks on the platform.

The Risk Manager tool provides underwriters with insight-driven information they can tap into that goes beyond the usual data that is gathered from their stock questionnaires. Customers can uncover their own cyber risk status after running Risk Manager, and only then send out to Allianz Global Corporate and Specialty (AGCS) and Munich Re to obtain a quote for online insurance, provided the customers are eligible for Cloud Protection +.

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The belief is that this pilot initiative would greatly simplify the cyber insurance procurement process, with the integration with Google Cloud. Not only will it futureproof insurtech for the cloud, given that there’s where most workloads are moving towards, but it will also streamline the underwriting process by providing the relevant data in real-time.

“Risk Manager gives us an inside-out look at a company,” said Parisi. “We’re driving underwriting toward a more data-driven approach.” The additional trust efficiencies will also benefit Google Cloud, who can now target more stringent regulated industries such as financial services and healthcare for their cloud offerings, with the promise that Allianz and Munich Re will equally split the cyber risk coverage, for eligible customers.

The wonder of this solution is that it should in theory be applicable to other technology partners and service providers, offering protection against cyber risk to a wider berth of potential clients. These clients in turn would be capable of supplying their services to more compliance-heavy industries, adding more security value to the cloud ecosystem.

The post Google teams with Allianz, Munich Re to manage cloud cyber risk appeared first on Tech Wire Asia.

And behind that repetition is a lot of substance; as increasingly digitized organizations, we’re amassing more data than ever before – tapping into the insights it holds can inform and transform approaches inside and out of the business, be it learning about customer preferences or how to streamline our recruitment process, that ultimately can make us market leaders. 

According to a new report by SQream, 59% of companies surveyed expect to see an increase of over 50% in their data volume in 2021. 60% of the surveyed companies already have over 500 TB of storage, meaning the expected increases will be enormous amounts of data.

The study found that 99% of management teams understand how critical data analytics is to make informed business decisions today. But the constant pressure to maximize the use of this ever-expanding wealth of data to derive ‘real-time insights’ and ‘data culture’ – and to avoid ‘dark data’ at all costs – likely induces a pang of anxiety among even hardened business leaders who find the task of being data-driven resource-intensive and mountainous. 

While management teams want to prioritize data analytics, they generally don’t have the budget necessary to cover the costs. Only 13% of the companies surveyed were in a good financial position when it comes to supporting data analytics activities. Over half (55%) will not fare as well, as their budgets will cover less than 75% of their actual data analytics needs in 2021. 

According to SQream, it remains to be seen if shifting priorities to focus on analyzing the treasure trove of data that companies are gathering will result in a matching shifting of budgets. 

The expense comes with the need to invest in and effectively implement solutions and tools to analyze data, such as Hadoop, Spark, HANA, and Tableau. But even with sufficient technology in place, organizations need to recruit and commit talent to utilize these tools effectively and contribute to cheerleading and embedding a culture of data analytics within the business. 

KPMG suggests that organizations with a CDO are twice as likely to have a clear digital strategy. The ongoing education of the organization’s workforce is a fundamental part of such a strategy.

On top of the expense of data analytics tech and talent, 82% of companies shared a variety of challenges related to how long it takes to prep data, ingest data, run queries, and create analytic reports. Only 18% of surveyed companies claim to have no challenges when it comes to running data analytics.

Indeed, IBM found that even if you do invest in professionals such as data scientists, up to 80% of their time can be spent data cleaning, or making it ‘usable’ for analytics and other applications. 

When it comes to becoming a ‘data-driven’ company, then, it seems there aren’t half measures. Organizations that want to leverage as much of the potential of their data as possible must make the initiative a priority central to their business strategy.

That said, it won’t happen overnight. An interview with Asian pharmacy giant Zuellig Pharma on Tech Wire Asia revealed how a well-thought, step-wise process can be the most effective. After investing in the necessary infrastructure, the firm focused on training staff on the benefits of using data and analytics, it educated clients and customers about the value of this new information and built a robust but agile data governance framework.

“As traction among staff, clients and customers grew, so too did our data culture and the number of ROI accretive use cases,” said Zuellig Pharma’s VP of data & analytics, Tristan Tan. 

Only after putting the foundations in place could the company explore more advanced areas: “[…] we have now started leveraging blockchain in our handling of information with our partners across the supply chain and have embarked on organization-wide automation agenda using data-science and robotics process automation to drive efficiencies in our operations,” Tan said.

“These efforts would not have been successful without the foundations we built in the earlier phases of our journey.”

Now the company has a “critical mass” of people who understand the importance of ensuring data is informing the work being done across the organization. Data culture is not an intangible concept but is embedded practically into business processes.

“Ensuring that a piece of information, a data report, a dashboard or analysis, is a regular part of normal business process has been key to our data transformation,” he said. 

Even for some of the most advanced, ‘data-native’ companies, a piecemeal process of individual education and daily discipline has been crucial. UK fintech Revolut is one of those; it maintains around 800 dashboards and runs around 100,000 SQL queries on a daily basis across the organization, and can optimally analyze large datasets spanning several sources to assist in fraud detection, improving customer satisfaction and financial reporting. Queries that used to take hours are now completed in seconds, enabling self-serve data analytics for all employees across all business functions. 

But behind this success with data analytics is an objective to ensure every employee at Revolut has access to the data they need for their work, every day, in a simple and efficient manner. The data science teams uses the central database as a single point of truth, from which it can download real-time extracts and insights at any time.

This is just one example of a company that has built a culture of data literacy from the first instance, but it showcases the potential of making data analysis and intelligence central to the entire business and making it a day-to-day discipline.

The post Data analytics – expensive, complex but worth it appeared first on Tech Wire Asia.