Fueling Asia’s Innovation Ecosystem

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Since the start of this millennium, no region has transformed as much as Asia. There has been significant paradigm shifts in the region and the perception that innovation starts in the US or in Europe and percolates through to Asia after a time lag, has been shattered. Asia is constantly demonstrating how dynamic, and technology-focused it is. This is getting fueled by the impact of the growing middle class on consumerism and the spirit of innovation across the region. The region has also seen a surge in new and upcoming business leaders who are embracing change and looking beyond success to creating impact.

What is Driving Innovation in Asia?

The “If you ain’t got it, build it” attitude. One of the key drivers of this shift is the age of the average population in Asia. According to the UN the Asia Pacific region has nearly 60% of the world’s youth population (between the age of 17-24). With youth comes dynamism, a desire to change the world, and innovation. As this age group enters the workforce, they will transform their lives and the companies they work in. They are already showing a spirit of agility when it comes to solving challenges – they will build what they do not have.

The Need to enable Foundational Shifts. The younger generation is more aware of environmental, social and governance issues that the world continues to face. Many of the countries in the region are emerging economies, where these issues become more apparent. COVID-19 has also inculcated an empathy in people and they are thinking of future success in terms of impact. The desire to enable foundational shifts is giving direction to the transformation journey in the region. The wonderful new paradigm that is the Digital Economy allows us to cut across all segments; and technology and its advancements has immense potential to create a more sustainable and inclusive future for the world. 

Realising the Power of Momentum. The pandemic has caused major disruptions in the region. But every crisis also presents an opportunity to perhaps re-imagine a brighter world through a digital lens.  The other thing that the pandemic has done is made people and organisations realise that to succeed they need to be open to change – and that momentum is important. As organisations had to pivot fast, they realised what I have been saying for years – we shouldn’t “let perfect get in the way of better”. This adaptability and the readiness to fail fast and learn from the mistakes early for eventual success, is leading to faster and more agile transformation journeys.     

Where are we seeing the most impact?

Industries are Transforming. There are industries such as Healthcare and Education that had to transform out of a necessity and urgency brought about by the COVID-19 pandemic. This has led to a greater impetus for change and optimism in these industries. These industries will continue to transform as governments focus significantly on creating “Social Safety Nets” and technology plays a key role in enabling critical services across Health, Education and Food Security. Then there are industries, such as the Financial Services and Retail, that had a strong customer focus and were well on their digital journeys before the pandemic. The pandemic boosted these efforts.

Ecosystm Industry Optimism Index

But these are not the only industries that are transforming. There are industries that have been impacted more than others. There are several instances of how organisations in these industries are demonstrating not only resilience but innovation. The Travel & Hospitality industry has had several such instances. As business models evolve the industry will see significant changes in digital channels to market, booking engines, corporate service offerings and others, as the overall Digital Strategy is overhauled.

Technologies are Evolving. Organisations depended on their tech partners to help them in their make the fast pivot required to survive and succeed in the last year – and tech companies have not disappointed. They have evolved their capabilities and continue to offer innovative solutions that can solve many of the ongoing business challenges that organisations face in their innovation journey. More and more technologies such as AI, machine learning, robotics, and digital twins are getting enmeshed together to offer better options for business growth, process efficiency and customer engagement. And the 5G rollouts will only accelerate that. The initial benefits being realized from early adoption of 5G has been for consumers. But there is a much bigger impact that is waiting to be realised as 5G empowers governments and businesses to make critical decisions at the edge.

Tech Start-ups are Flourishing.  There are immense opportunities for technology start-ups to grow their market presence through innovative products and services. To succeed these companies need to have a strong investment roadmap; maintain a strong focus on customer engagement; and offer technology solutions that can fulfil the global needs of their customers. Technologies that promote efficiency and eliminate mundane tasks for humans are the need of the hour. However, as the reliance on technology-led transformation increases, tech vendors are becoming acutely aware that they cannot be best-in-class across the different technologies that an organisation will require to transform. Here is where having a robust partner ecosystem helps. Partnerships are bringing innovation to scale in Asia.

We can expect Asia to emerge as a powerhouse as businesses continue to innovate, embed technology in their product and service offerings – and as tech start-ups continue to support their innovation journeys.


Ecosystm CEO Amit Gupta gets face to face with Garrett Ilg, President Asia Pacific & Japan, Oracle to discuss the rise of the Asia Digital economies, the impact of the growing middle class on consumerism and the spirit of innovation across the region.

Designed for change in a rising digital economy
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Building Trust in your AI Solutions

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In this blog, our guest author Shameek Kundu talks about the importance of making AI/ machine learning models reliable and safe. “Getting data and algorithms right has always been important, particularly in regulated industries such as banking, insurance, life sciences and healthcare. But the bar is much higher now: more data, from more sources, in more formats, feeding more algorithms, with higher stakes.”

Building trust in algorithms is essential. Not (just) because regulators want it, but because it is good for customers and business. The good news is that with the right approach and tooling, it is also achievable.

Getting data and algorithms right has always been important, particularly in regulated industries such as banking, insurance, life sciences and healthcare. But the bar is much higher now: more data, from more sources, in more formats, feeding more algorithms, with higher stakes. With the increased use of Artificial Intelligence/ Machine Learning (AI/ML), today’s algorithms are also more powerful and difficult to understand.

A false dichotomy

At this point in the conversation, I get one of two reactions. One is of distrust in AI/ML and a belief that it should have little role to play in regulated industries. Another is of nonchalance; after all, most of us feel comfortable using ‘black-boxes’ (e.g., airplanes, smartphones) in our daily lives without being able to explain how they work. Why hold AI/ML to special standards?

Both make valid points. But the skeptics miss out on the very real opportunity cost of not using AI/ML – whether it is living with historical biases in human decision-making or simply not being able to do things that are too complex for a human to do, at scale. For example, the use of alternative data and AI/ML has helped bring financial services to many who have never had access before.

On the other hand, cheerleaders for unfettered use of AI/ML might be overlooking the fact that a human being (often with a limited understanding of AI/ML) is always accountable for and/ or impacted by the algorithm. And fairly or otherwise, AI/ML models do elicit concerns around their opacity – among regulators, senior managers, customers and the broader society. In many situations, ensuring that the human can understand the basis of algorithmic decisions is a necessity, not a luxury.

A way forward

Reconciling these seemingly conflicting requirements is possible. But it requires serious commitment from business and data/ analytics leaders – not (just) because regulators demand it, but because it is good for their customers and their business, and the only way to start capturing the full value from AI/ML.

1. ‘Heart’, not just ‘Head’

It is relatively easy to get people excited about experimenting with AI/ML. But when it comes to actually trusting the model to make decisions for us, we humans are likely to put up our defences. Convincing a loan approver, insurance under-writer, medical doctor or front-line sales-person to trust an AI/ML model – over their own knowledge or intuition – is as much about the ‘heart’ as the ‘head’. Helping them understand, on their own terms, how the alternative is at least as good as their current way of doing things, is crucial.

2. A Broad Church

Even in industries/ organisations that recognise the importance of governing AI/ML, there is a tendency to define it narrowly. For example, in Financial Services, one might argue that “an ML model is just another model” and expect existing Model Risk teams to deal with any incremental risks from AI/ML.

There are two issues with this approach:

First, AI/ML models tend to require a greater focus on model quality (e.g., with respect to stability, overfitting and unjust bias) than their traditional alternatives. The pace at which such models are expected to be introduced and re-calibrated is also much higher, stretching traditional model risk management approaches.

Second, poorly designed AI/ML models create second order risks. While not unique to AI/ML, these risks become accentuated due to model complexity, greater dependence on (high-volume, often non-traditional) data and ubiquitous adoption. One example is poor customer experience (e.g., badly communicated decisions) and unfair treatment (e.g., unfair denial of service, discrimination, misselling, inappropriate investment recommendations). Another is around the stability, integrity and competitiveness of financial markets (e.g., unintended collusion with other market players). Obligations under data privacy, sovereignty and security requirements could also become more challenging.

The only way to respond holistically is to bring together a broad coalition – of data managers and scientists, technologists, specialists from risk, compliance, operations and cyber-security, and business leaders.

3. Automate, Automate, Automate

A key driver for the adoption and effectiveness of AI/ ML is scalability. The techniques used to manage traditional models are often inadequate in the face of more data-hungry, widely used and rapidly refreshed AI/ML models. Whether it is during the development and testing phase, formal assessment/ validation or ongoing post-production monitoring,  it is impossible to govern AI/ML at scale using manual processes alone.

o, somewhat counter-intuitively, we need more automation if we are to build and sustain trust in AI/ML. As humans are accountable for the outcomes of AI/ ML models, we can only be ‘in charge’ if we have the tools to provide us reliable intelligence on them – before and after they go into production. As the recent experience with model performance during COVID-19 suggests, maintaining trust in AI/ML models is an ongoing task.

***

I have heard people say “AI is too important to be left to the experts”. Perhaps. But I am yet to come across an AI/ML practitioner who is not keenly aware of the importance of making their models reliable and safe. What I have noticed is that they often lack suitable tools – to support them in analysing and monitoring models, and to enable conversations to build trust with stakeholders. If AI is to be adopted at scale, that must change.

Shameek Kundu is Chief Strategy Officer and Head of Financial Services at TruEra Inc. TruEra helps enterprises analyse, improve and monitor quality of machine


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Woolworths Announces Future of Work Fund

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2020 was a strange year for retail. Businesses witnessed significant disruption to supply chains, significant swings in demand for products (toilet paper, puzzles, bikes etc!) and then sometimes incredible growth – as disposable income increased as many consumers are no longer taking expensive holidays. Overall, it was a mixed year, with many retailers closing down and others reporting record sales. The grocery sector boomed – with many restaurants and fast-food providers closed, sometimes the supermarkets were some of the few remaining open retailers.

For many retailers, technology has become a key enabler to their transformation, survival and success (Figure 1).

Technology Focus 2021 - Retail Industry

Woolworths, Australia’s largest retailer, operates across the grocery, department store, drinks, and hospitality sectors. They hold a significant market share in most markets that they operate in. The company had a strong 2019/20 (financial year runs from July 2019 to June 2020) with sales up 8% – and in the first half of the 2020/21 financial year, sales were up nearly 11%. But the company is not resting on its laurels – one of its 6 key priorities is to “Accelerate Digital, eCom and convenience for our increasingly connected customers”. This requires more than just a deep technology investment, but a new culture, new skills, and new ways of working.

Woolworths’ Employee Focus

Woolworths has committed to invest AUD 50 million in upskilling and reskilling their employees in areas such as digital, data analytics, machine learning and robotics over the next three years. The move comes as a response to the way the Retail industry has been disrupted and the need to futureproof to stay relevant and successful. The training will be provided through online platforms and through collaborations with key learning institutions.

The supermarket giant is one of Australia’s largest private employers with more than 200,000 employees. Under Woolworths’ ‘Future of Work Fund’ their staff will be trained across supply chain, store operations, and support functions to enhance delivery and decision-making processes. The retailer will also create an online learning platform that will be accessible by Woolworths employees as well as by other retail and service companies to support the ecosystem.  Woolworths has plans to upskill their staff in customer service abilities, leadership skills and agile ways of working.

Woolworths’ upskilling program will also support employees who were impacted by Woolworths planned closures of Minchinbury, Yennora, and Mulgrave distribution centres due in 2025.

Woolworths’ Tech Focus

Woolworths has been ramping up their technology investments and having tech-savvy employees will be key to their future success. In October 2020, Woolworths deployed micro automation technology to revamp their eCommerce facility in Melbourne to speed up the fulfilment of online grocery orders, and front and back-end operations. Woolworths also partnered with Dell Technologies in November 2020 to bring together their private and public cloud onto a single platform to improve mission-critical processes, applications and support inventory management operations across its retail stores.

Future of Work

For many years, Ecosystm has been advising our clients to invest more in the skills of the business. Every business will be using more cloud next year than they are this year; they will suffer more cybersecurity incidents; they will use more AI and machine learning; they will automate more processes than are automated today. More of their customer engagements will be digital, and more insight will be required to drive better outcomes for customers and employees. This all needs new skills – or more people trained on skills that some in the business already understand. But too many businesses don’t train in advance – instead waiting for the need and paying external consultants or expensive new hires for their skills. Empowered businesses – ones that are creating a future-ready, agile business – invest in their people, work environment, business processes and technology to create an environment where innovation, transformation and business change are accepted and encouraged (Figure 2).

Future of Work

Empowered businesses can adapt to new challenges, new market conditions and respond to new competitive threats. By taking these steps to upskill and empower their employees, Woolworths is building towards empowering their own business for long term success.


 Transform and be better prepared for future disruption, and the ever-changing competitive environment and customer, employee or partner demands in 2021. Download Ecosystm Predicts: The top 5 Future of Work Trends For 2021.

Ecosystm Predicts: The Top 5 FUTURE OF WORK Trends for 2021
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The Future of Work: Trust, Good Faith & the Engagement Process

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Many opinions exist on how automation and machine learning will help our return to the office environment. Removing physical touchpoints and leveraging machine learning to trace employee behaviour can help with the transition back to the workplace. But will people trust the office’s automated suggestions on where to work in the building, or help themselves to alternative workspaces?  

Processes & Trust for People Engagement

Organisations such as Disney and Amazon understand what kinds of processes and trust it takes to engage people. These organisations took their time to create a vision of the contactless trusted experience before developing an implementation plan. The RFID wristbands at Disney that open hotel doors and get you on to rides involve many elements of trust and privacy. The automated order and delivery tracking of Amazon, along with suggestions and buying patterns, require the person to opt-in and share information to make happen.

So for your company, once employees re-enter the workplace, how will your company create those processes, that level of trust and faith, that would allow movements and health status to be tracked by office automation? For example, how often should employees overtly be aware of their temperature being scanned?

Abilities of Buildings to Manage

Facilities management is trending towards intelligent building management systems (iBMS) which know about room occupancy, room hygiene and are tracking who has been where and with whom. Elevators will limit occupancy and direct users to the correct lift going to the correct location. I have already seen this in our city hospital where you get directed to the correct lift once you have entered information on your destination. This combines user interface devices such as touchless pads, system hardware, and access control management software.

The building can also possibly direct you via a building app to request a place to work. You could swipe your personnel card and then be shown several options based on your personal profile and job role, including private quiet rooms, communal areas, and outside meeting tables. Previous occupants can be noted to share hygiene tracing if necessary. Intelligent buildings already offer direct support to the employees who interact with them for HVAC, lighting control, and occupation sensor. They have the ability to reduce user friction while raising workplace experience metrics to create a measured environment.

User Trust & Participation

Users should be willing to participate to get access. To create the trust that is required for employees to be willing to participate in the process, companies need to share policies and demonstrate stewardship of the data accessed. Who is holding my locational data, for how long, and for what purpose?

Trust facilitates successful data sharing, which in turn reinforces trust. Trust is built when the purpose of data sharing is made clear, and when those involved in the process know each other, understand each other’s expectations, and carry out their commitments as agreed. Trust increases the likelihood of further collaboration and improves core surveillance capacity by supporting surveillance networks.

Conclusion

Will we put our trust in buildings and facilities management on our return to the office? If communication is clear and policy well articulated, the building can play a role in engaging users to return to some standards of in-office participation. But if communication is muddy and policy not made clear, people will make their own way to safety – potentially impacting the environment of others.


Transform and be better prepared for future disruption, and the ever-changing competitive environment and customer, employee or partner demands in 2021. Download Ecosystm Predicts: The top 5 Future of Work Trends For 2021.

Ecosystm Predicts: The Top 5 FUTURE OF WORK Trends for 2021
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Prioritise your Customer Experience spend for faster Business Growth

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The past twelve months have been tough. Most businesses in Singapore (68%) still haven’t seen revenue recover to pre-pandemic levels. Many budgets are down and you are likely to have a long list of spending options that might help you grow revenue and pull your business out of the pandemic-induced slump. Even if your business is doing well, the pressure on budgets is real.

Businesses in Singapore have not recovered from the pandemic

Increasing your CX Spend

Despite the pressure on budgets Ecosystm data makes a strong case to not cut your customer experience (CX) spend! Businesses in Singapore that are cutting their CX spend are less likely to return to growth, more likely to be competing on price (hence cutting margins), not focused on their digital and omnichannel customers, and have lower levels of innovation. Funnily enough, these are also the businesses with complex, legacy systems which need more focus to provide an improved CX! To be quite frank, businesses in Singapore who are cutting CX spend are setting themselves up for failure. With other businesses increasing CX spend, the gap between the customer experiences will grow to a point where customers will leave and it will be hard to catch up.

Prioritising your CX Spend

So now that you have secured your CX spend, where will you get the biggest bang for your buck? Let’s look at where businesses in Singapore are focusing their CX initiatives in 2021.

Offering an omnichannel experience. Your customers expect more than just a great digital experience – they want the right experience at the right touchpoint. The CX leaders in Singapore (who, unsurprisingly are often the market leaders) are already offering great omnichannel experiences, so this is quickly becoming about catching up – and not about getting ahead. Providing a consistent, personalised, and optimised experience across your digital touchpoints needs to be a top priority for your business today. If you are not offering conversational commerce solutions, start that strategy as soon as possible – you need to be where your customers are today. Extending this to physical channels and broader ecosystem partners should also be on your agenda.

Improving knowledge systems. Your knowledge systems don’t do what they say on the box. They don’t provide answers to questions – for employees or customers. In fact, if your customer service agents get asked a question they don’t know the answer to, their number one source for answers is actually their colleagues or team leaders – NOT the knowledge management system! Start investing in systems – or ideally a single system – that help your employees get better, faster answers to questions. Make sure that the system is providing the same answers to both your employees and your customers across all touchpoints – physical and digital.

Where do Customer Service agents go for answers

Migrating customer service platforms to the cloud. Over half the businesses in Singapore that we assessed have this as a top CX priority. Cloud solutions offer faster time to value, lower management costs, give access to more regular improvements and often provide the ability to easily integrate with partners who offer product extensions and customisations. This trend will continue in 2021 and 2022 as more businesses realise that their legacy customer service or contact centre platform is inhibiting their ability to innovate their customer experience. These systems also help businesses to stay compliant and reduce the reliance on internal IT – which has traditionally struggled to keep up with the fast-changing nature of the contact centre and customer service teams.

Reasons for Adopting Cloud Customer Service solutions

Investing in AI and machine learning. Many businesses are using AI to provide the personalised and optimised customer experiences they aspire to. AI and machine learning are allowing businesses to create personalised offers, offer a next-best action and automate services. Advanced banks in Singapore can create interest rate offers for each individual customer based on their credit profile and history. 46% of businesses in Singapore are already using AI to offer recommendations for customer service agents, 44% to optimise or test messaging and campaigns and 43% to provide faster, more accurate access to information and knowledge. 18 months ago, AI was a business differentiator – allowing your business to create a stand-out CX. Today AI is quickly becoming a standard practice – the battle now is around using AI to create personalised and optimised experiences.

A great customer experience will be the most important factor in lifting your business to pre-pandemic growth levels and helping your business remain competitive in today’s tough business conditions. When it comes to CX, there is no such thing as “saving your way to growth”.


Your opportunity to drive greater business success lies in your ability to better win, serve and retain your customers. Refresh your customer strategy and capability today to make 2021 an exceptional year for your business.

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SAS Acquires Boemska to Boost its Cloud-Native Vision

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SAS announced that it has acquired Boemska, a provider of low-code development tools and analytics workload management software. The small, privately held company is UK-based with an R&D centre in Serbia. The acquisition will be integrated into SAS Viya, its cloud-native platform, which includes containerised analytics and machine learning offerings. Terms of the deal have not been disclosed.

A SAS silver partner, Boemska has wins in Health, Finance, and Travel. Most of its reference clients are based in Europe in addition to a small number in the US and South Africa. Boemska has two primary software offerings – Enterprise Session Monitor (ESM) and AppFactory. Additionally, it delivers cloud migration, performance diagnostics, and application development services.

Boemska Capabilities

Boemska ESM provides visibility into performance and cost management of analytics workloads. The product enables self-service root cause analysis for developers, monitoring and batch schedule optimisation for administrators, and departmental cost allocation of cloud resources. ESM manages SAS, R, and Python workloads and is compatible with workload management platforms from the likes of IBM and BMC. Boemska shipped an updated version of ESM in 2020 to improve the UI and ensure support for SAS Viya. At the time, it announced that its development team had doubled in the preceding 12 months, suggesting a trajectory of growth.

AppFactory is a low-code development platform for data scientists and data engineers using SAS, which generates JavaScript for front-end developers along with data transport, authentication, and exception handling. SAS emphasises the portability of apps that can be created and run on mobile and IoT devices. Examples provided include machine learning and event alerts in healthcare wearables, video-based defect identification in Manufacturing, and drone-based asset monitoring in Utilities. Boemska states that its low-code offering seeks to bridge the “last mile of analytics” by putting insights into the hands of decision-makers.

SAS Focuses on Cloud-Native Analytics and AI

SAS launched Viya 4.0 in mid-2020, a major step in its vision to become a provider of cloud-native analytics and machine learning solutions. The platform includes offerings, such as Visual Analytics, Visual Statistics, Visual Machine Learning, and Visual Data Science packaged in containers and orchestrated by Kubernetes. Microsoft Azure has become its preferred cloud partner, assisting in developing SAS Cloud, hosted from data centres in the US, Brazil, Australia, and newly launched facilities in Germany and the UK. Viya managed services are also available from Azure regions. AWS and Google Cloud are expected to make the leap to Viya 4.0 from version 3.5 soon. As part of its cloud-native strategy, SAS now offers three tiers for software updates – bi-annual, monthly, or immediately after release.

Ecosystm Comment

The major overhaul of SAS Viya is part of the vendor’s USD 1B investment into AI over three years from 2019-2021. The platform includes a heavy emphasis on NLP, machine learning, and computer vision. The integration of Boemska’s low-code development offering into Viya will allow SAS clients to extract greater value from AI by quickly embedding it in mobile and enterprise applications. The converging trends of citizen developers and data literacy suggest SAS has selected the right path for the future.


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Policy Making in a Pandemic: Use of AI in SupTech

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Artificial Intelligence (AI) is becoming embedded in financial services across consumer interactions and core business processes, including the use of chatbots and natural language processing (NLP) for KYC/AML risk assessment.

But what does AI mean for financial regulators? They are also consuming increasing amounts of data and are now using AI to gain new insights and inform policy decisions. 

The efficiencies that AI offers can be harnessed in support of compliance within both financial regulation (RegTech) and financial supervision (SupTech). Authorities and regulated institutions have both turned to AI to help them manage the increased regulatory requirements that were put in place after the 2008 financial crisis. Ecosystm research finds that compliance is key to financial institutions (Figure 1).

Drivers for Cybersecurity and Regulatory Investments

SupTech is maturing with more robust safeguards and frameworks, enabling the necessary advancements in technology implementation for AI and Machine Learning (ML) to be used for regulatory supervision. The Bank of England and the UK Financial Conduct Authority surveyed the industry in March 2019 to understand how and where AI and ML are being used, and their results indicated 80% of survey respondents were using ML. The most common application of SupTech is ML techniques, and more specifically NLP to create more efficient and effective supervisory processes.

Let us focus on the use of NLP, specifically on how it has been used by banking authorities for policy decision making during the COVID-19 crisis. AI has the potential to read and comprehend significant details from text. NLP, which is an important subset of AI, can be seen to have supported operations to stay updated with the compliance and regulatory policy shifts during this challenging period.

Use of NLP in Policy Making During COVID-19

The Financial Stability Board (FSB) coordinates at the international level, the work of national financial authorities and international standard-setting bodies in order to develop and promote the implementation of effective regulatory, supervisory and other financial sector policies. A recent FSB report delivered to G20 Finance Ministers and Central Bank Governors for their virtual meeting in October 2020 highlighted a number of AI use cases in national institutions.

We illustrate several use cases from their October report to show how NLP has been deployed specifically for the COVID-19 situation. These cases demonstrate AI aiding supervisory team in banks and in automating information extraction from regulatory documents using NLP.

De Nederlandsche Bank (DNB)

The DNB is developing an interactive reporting dashboard to provide insight for supervisors on COVID-19 related risks. The dashboard that is in development, enables supervisors to have different data views as needed (e.g. over time, by bank). Planned SupTech improvements include incorporating public COVID-19 information and/or analysing comment fields with text analysis.

Monetary Authority of Singapore (MAS)

MAS deployed automation tools using NLP to gather international news and stay abreast of COVID-19 related developments. MAS also used NLP to analyse consumer feedback on COVID-19 issues, and monitor vulnerabilities in the different customer and product segments. MAS also collected weekly data from regulated institutions to track the take-up of credit relief measures as the pandemic unfolded. Data aggregation and transformation were automated and visualised for monitoring.

US Federal Reserve Bank Board of Governors

One of the Federal Reserve Banks in the US is currently working on a project to develop an NLP tool used to analyse public websites of supervised regulated institutions to identify information on “work with your customer” programs, in response to the COVID-19 crisis.

Bank of England

The Bank developed a Policy Response Tracker using web scraping (targeted at the English versions of each authority/government website) and NLP for the extraction of key words, topics and actions taken in each jurisdiction. The tracker pulls information daily from the official COVID-19 response pages then runs it through specific criteria (e.g.  user-defined keywords, metrics and risks) to sift and present a summary of the information to supervisors.

Market Implications

Even with its enhanced efficiencies, NLP in SupTech is still an aid to decision making and cannot replace the need for human judgement. NLP in policy decision is performing clearly defined information gathering tasks with greater efficiency and speed. But NLP cannot change the quality of the data provided, so data selection and choice are still critical to effective policy making.  

For authorities, the use of SupTech could improve oversight, surveillance, and analytical capabilities. These efficiency gains and possible improvement in quality arising from automation of previously manual processes could be consideration for adoption.

Attention will be paid in 2021 to focusing on automation of processes using AI (Figure 2).

Digital Focus for 2021 in Financial Services

Based on a survey done by the FSB of its members (Figure 3), the majority of their respondents had a SupTech innovation or data strategy in place, with the use of such strategies growing significantly since 2016.

Summary

For more mainstream adoption, data standards and use of effective governance frameworks will be important. As seen from the FSB survey, SupTech applications are now used in reporting, data management and virtual assistance. But institutions still send the transaction data history in different reporting formats which results in a slower process of data analysing and data gathering. AI, using NLP, can help with this by streamlining data collection and data analytics. While time and cost savings are obvious benefits, the ability to identify key information (the proverbial needle in the haystack) can be a significant efficiency advantage.


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For more insights, attend the Singapore FinTech Festival 2020: Infrastructure Summit which will cover topics tied to creating infrastructure for a digital economy; and RegTech and SupTech policies to drive innovation and efficiencies in a co-Covid-19 world.

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Conversational AI Gets a Boost – Five9 Acquires Inference Solutions

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Five9, a cloud-based contact centre solutions provider announced the acquisition of intelligent virtual agent (IVA) platform provider, Inference Solutions for about USD 172 million. Five9 and Inference Solutions have been partnering for the last couple of years, with Five9 being a reseller for Inference Solutions’ IVA platform. The acquisition is expected to provide a boost to Five9’s AI portfolio, automate contact centre agent activities and provide AI-based omnichannel self-service solutions.

The need to drive greater automation in the contact centre is high on the agenda, and this acquisition demonstrates how important AI and automation is to contact centre modernisation. The old-fashioned ways of long wait times, being passed on through different menus on the IVR and being asked to repeat yourself through the older speech recognition engines is starting to not only frustrate customers but will become obsolete. Based on Ecosystm’s research, close to 60% of contact centres globally stated that investing in machine learning and AI is a top customer experience priority in the next 12 months.

Inference has come a long way since its inception at Telstra Labs

Inference Solutions (founded in 2005) was spun out of Telstra Labs. It has since expanded to the US and developed a suite of solutions in the IVA segment. They have a good partnership strategy with the leading telecom providers globally as well as the UC/contact centre vendors. Inference Solutions uses resellers such as service providers, UC, and contact centre software providers – and these include AT&T, Cisco (Broadsoft), Momentum Telecom, Nextiva, 8×8 and many others. The Inference Studio solution will see a new release in the next few months where the solution will come pre-built with the ability for the contact centre team to pre-load the contact centre conversations. These can be conversations that have been going on for 6 months or longer. The Studio solution will then be able to analyse and understand the underlying intent of the conversation, match the intent so that it can be used to auto train the bots accurately. That process of matching the intent and training is expensive and if you can automate some elements of that, it will bring the cost of the deployment down. Its solution integrates into NLP engines from Google, AWS, and IBM. In Australia they continue to work on patents in close partnerships with Melbourne University and RMIT. Throughout its journey, Inference has built a good base of customers in the US, UK, and Australia.

Five9 to accelerate on its vision of AI and Cloud

Contact centre modernisation is high on the agenda for many organisations and this will lead them to build AI and automation at the core of their customer strategies. The discussion spans across the CEO, Digital and Innovation, and the Contact Centre teams.

Five9 had acquired Whendu, an iPaaS platform provider empowering businesses and developers with no-code, visual application workflow tool, optimised for contact centres in November 2019, and Virtual Observer, an innovative provider of cloud-based workforce optimisation, also known as Workforce Engagement Management (WEM) in February of this year.

The pandemic has resulted in increased engagement of contact centres with customers. Companies are gradually looking for ways to automate tasks, deliver better communication, speech and text recognition, decipher languages, and implement solutions mimicking humans. As a solution to these challenges, IVAs are being viewed as efficient and effective digital workers for a modern contact centre. IVAs represent increased throughput, more accurate results, and better-informed agents.

Successful use cases have shown that conversational AI can reduce calls and repetitive queries by 70-90%. IVRs with monolithic, complicated menus will start becoming unpopular and force contact centres to embark on a modernisation and automation strategy. If we evaluate the shift in priorities after COVID-19, we see that organisations are ramping up their self-service capabilities and their adopt of AI and machine learning (Figure 1).  

Contact Centres Conversational AI

The acquisition will give Five9 a foothold in the Asia Pacific region with an initial focus on the Australia market. The Australia market is by far the most advanced cloud contact centre market in the Asia Pacific. Five9 gains a team of staff that will help them fuel the contact centre modernisation discussion across the Asia Pacific. As the region has a complex market, the need to work with local carriers and partners will be critical for further expansion. Five9 has made an important acquisition in building in IVA capability into its CCaaS solution.


Click below to access insights from the Ecosystm Contact Centre Study on visibility into organisations’ priorities when running a Contact Centre (both in-house and outsourced models) and the technologies implemented and being evaluated

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Ecosystm Predicts: The Top 5 Retail & eCommerce Trends for 2021

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The Retail industry has had to do a sharp re-think of its digital roadmap and transformation journey – Ecosystm research shows that about 75% of retail organisations had to start, accelerate, or re-focus their digital transformation initiatives. However, that will not be enough as organisations move beyond survival to recovery – and future successes. While retailers will focus on the shift in customer expectations, a mere focus on customer experience will not be enough in 2021. Ecosystm Principal Advisors, Alan Hesketh and Alea Fairchild present the top 5 Ecosystm predictions for Retail & eCommerce in 2021.

This is a summary of the predictions, the full report (including the implications) is available to download for free on the Ecosystm platform here.

The Top 5 Retail & eCommerce Trends for 2021

  1. There Will Only be Omnichannel Retailers

The value of an omnichannel offer in Retail has become much clearer during the COVID-19 pandemic. Retailers that do not have the ability to deliver using the channel customers prefer will find it hard to compete. As the physical channel becomes less important new revenue opportunities will open up for businesses operating in adjacent market sectors – companies such as food and grocery wholesalers will increasingly sell direct to consumers, leveraging their existing online and distribution capabilities.

Most customers transact on mobile device – either a mobile phone or tablet.  New capabilities will remove some of the barriers to using these mobile devices. For one, technologies such as Progressive Web Apps (PWA) and Accelerated Mobile Pages (AMP) will provide a better customer experience on mobile platforms than existing websites, while delivering a user experience at par or better than mobile apps. Also, as retailers become AI-enabled, machine learning engines will provide purchase recommendations through smartwatches or in-home, voice-enabled, smart devices.  

  1. COVID-19 Will Continue to be an Influence Forcing Radical Shifts

In driving the economic recovery in 2021, we will see ‘glocal’ consumption – emphasis on local retailers and global players taking local actions to win the hearts and minds of local consumers. There will be significant actions within local communities to drive consumers to support local retailers. Location-based services (LBS) will be used extensively as consumers on the high street carry more LBS-enabled devices than ever before. Bluetooth beacon technology and proximity marketing will drive these efforts. Consumers will have to opt-in for this to work, so privacy and relationship management are also important to consider.

But people still want to “physically” browse, and design aesthetics of a store are still part of the attraction. In the next 18 months, the concept of virtual stores that are digital twins will take off, particularly in the holiday and Spring clearance sales. Innovators like Matterport can help local retailers gain a more global audience with a digital twin with a limited technological investment. At a minimum, Shopify or other intermediaries will be necessary for a digital shop window.  

  1. The Industry will See Artificial Intelligence in Everything

AI will increase its impact on Retail with an uptake in two key areas.

  • Customer interactions. Retail AI will use customer data to deliver much richer and targeted experiences. This may include the ability to get to a ‘segment of one’.  Tools will include chatbots that are more functional and support for voice-based commerce using mobile and in-home edge devices. Also, in-store recognition of customers will become easier through enhanced device or facial recognition. Markets where privacy is less respected will lead in this area – other markets will also innovate to achieve the same outcomes without compromising privacy but will lag in their delivery. This mismatch of capability may allow early adopters to enter other geographic markets with competitive offers while meeting the privacy requirements of these markets.
  • Supply chain and pricing capabilities. AI-based machine learning engines using both internal and increased sources of external data will replace traditional math-based forecasting and replenishment models. These engines will enable the identification of unexpected and unusual demand influencing factors, particularly from new sources of external data. Modelling of price elasticity using machine learning will be able to handle more complex models. Retailers using this capability will be in a better position to optimise their customer offers based on their pricing strategies. Supply chains will be re-engineered so products with high demand volatility are manufactured close to markets, and the procurement of products with stable demands will be cost-based.
  1. Distribution Woes Will Continue

Third party delivery platforms such as Wish and RoseGal are recruiting additional international non-Asian suppliers to expand their portfolios. Amazon and AliExpress are leaders here, but there are many niche eCommerce platforms taking up the slack due to the uneven distribution patterns from the ongoing economic situation. Expect to see a number of new entrants taking up niche spaces in the second half of 2021, sponsored by major retail product brands, to give Amazon a run for their money on a more local basis. 

As the USPS continues to be under strain, delivery companies like FedEx in the US who partner with the USPS are already suffering from the USPS’s operational slowdown, in both their customer reputation and delivery speed. In 2021, COVID-19 – and workers’ unions – will continue to impact distribution activities. Increased spending in warehouse automation and new retail footprints such as dark stores will be seen to make up for worker shortfalls.  

  1. China’s Retail Models Will Expand into Other Markets  

China’s online businesses operate in a large domestic market that is comparatively free of international competitors.  Given the scale of the domestic market, these online companies have been able to grow to become substantial businesses using advanced technologies. All the Chinese tech giants – among them Alibaba, ByteDance, DiDi Chuxing, and Tencent – are expanding internationally.

China’s rapidly recovering economy puts those businesses in a strong position to fund a competitive expansion into international markets using their domestic base, particularly with their Government’s promotion of the country’s tech sector. It is harder to impose restrictions on software-based businesses, unlike the approach that we have witnessed the US Government take for hardware companies such as Huawei – placing constraints on mobile phone components and operating systems.

These tech giants also have significant experience in a Big Data environment that provides little privacy protection, as well as leading-edge AI capabilities. While they will not be able to operate with the same freedom in global markets, and there will be other large challenges in translating Chinese experience to other markets – these tech players will be able to compete very effectively with incumbent global companies. Chinese companies also continue to raise capital from US stock exchanges with The Economist reporting Chinese listings have raised close to USD 17 billion since January 2020.  


Download Ecosystm Predicts: The Top 5 Retail & eCommerce Trends for 2021

The full findings and implications of The Top 5 Retail & eCommerce Trends for 2021 are available for download from the Ecosystm platform. Signup for Free to download the report.

Ecosystm Predicts: The Top 5 Retail & eCommerce Trends for 2021
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