As we return to the office, there is a growing reliance on devices to tell us how safe and secure the environment is for our return. And in specific application areas, such as Healthcare and Manufacturing, IoT data is critical for decision-making. In some sectors such as Health and Wellness, IoT devices collect personally identifiable information (PII). IoT technology is so critical to our current infrastructures that the physical wellbeing of both individuals and organisations can be at risk.
Trust & Data
IoT are also vulnerable to breaches if not properly secured. And with a significant increase in cybersecurity events over the last year, the reliance on data from IoT is driving the need for better data integrity. Security features such as data integrity and device authentication can be accomplished through the use of digital certificates and these features need to be designed as part of the device prior to manufacturing. Because if you cannot trust either the IoT devices and their data, there is no point in collecting, running analytics, and executing decisions based on the information collected.
We discuss the role of embedding digital certificates into the IoT device at manufacture to enable better security and ongoing management of the device.
Securing IoT Data from the Edge
So much of what is happening on networks in terms of real-time data collection happens at the Edge. But because of the vast array of IoT devices connecting at the Edge, there has not been a way of baking trust into the manufacture of the devices. With a push to get the devices to market, many manufacturers historically have bypassed efforts on security. Devices have been added on the network at different times from different sources.
There is a need to verify the IoT devices and secure them, making sure to have an audit trail on what you are connecting to and communicating with.
So from a product design perspective, this leads us to several questions:
- How do we ensure the integrity of data from devices if we cannot authenticate them?
- How do we ensure that the operational systems being automated are controlled as intended?
- How do we authenticate the device on the network making the data request?
Using a Public Key Infrastructure (PKI) approach maintains assurance, integrity and confidentiality of data streams. PKI has become an important way to secure IoT device applications, and this needs to be built into the design of the device. Device authentication is also an important component, in addition to securing data streams. With good design and a PKI management that is up to the task you should be able to proceed with confidence in the data created at the Edge.
Johnson Controls/DigiCert have designed a new way of managing PKI certification for IoT devices through their partnership and integration of the DigiCert ONE™ PKI management platform and the Johnson Controls OpenBlue IoT device platform. Based on an advanced, container-based design, DigiCert ONE allows organisations to implement robust PKI deployment and management in any environment, roll out new services and manage users and devices across your organisation at any scale no matter the stage of their lifecycle. This creates an operational synergy within the Operational Technology (OT) and IoT spaces to ensure that hardware, software and communication remains trusted throughout the lifecycle.
Rationale on the Role of Certification in IoT Management
Digital certificates ensure the integrity of data and device communications through encryption and authentication, ensuring that transmitted data are genuine and have not been altered or tampered with. With government regulations worldwide mandating secure transit (and storage) of PII data, PKI can help ensure compliance with the regulations by securing the communication channel between the device and the gateway.
Connected IoT devices interact with each other through machine to machine (M2M) communication. Each of these billions of interactions will require authentication of device credentials for the endpoints to prove the device’s digital identity. In such scenarios, an identity management approach based on passwords or passcodes is not practical, and PKI digital certificates are by far the best option for IoT credential management today.
Creating lifecycle management for connected devices, including revocation of expired certificates, is another example where PKI can help to secure IoT devices. Having a robust management platform that enables device management, revocation and renewal of certificates is a critical component of a successful PKI. IoT devices will also need regular patches and upgrades to their firmware, with code signing being critical to ensure the integrity of the downloaded firmware – another example of the close linkage between the IoT world and the PKI world.
PKI certification benefits both people and processes. PKI enables identity assurance while digital certificates validate the identity of the connected device. Use of PKI for IoT is a necessary trend for sense of trust in the network and for quality control of device management.
Identifying the IoT device is critical in managing its lifespan and recognizing its legitimacy in the network. Building in the ability for PKI at the device’s manufacture is critical to enable the device for its lifetime. By recognizing a device, information on it can be maintained in an inventory and its lifecycle and replacement can be better managed. Once a certificate has been distributed and certified, having the control of PKI systems creates life-cycle management.
Organisations have found that it is not always desirable to send data to the cloud due to concerns about latency, connectivity, energy, privacy and security. So why not create learning processes at the Edge?
What challenges does IoT bring?
Sensors are now generating such an increasing volume of data that it is not practical that all of it be sent to the cloud for processing. From a data privacy perspective, some sensor data is sensitive and sending data and images to the cloud will be subject to privacy and security constraints.
Regardless of the speed of communications, there will always be a demand for more data from more sensors – along with more security checks and higher levels of encryption – causing the potential for communication bottlenecks.
As the network hardware itself consumes power, sending a constant stream of data to the cloud can be taxing for sensor devices. The lag caused by the roundtrip to the cloud can be prohibitive in applications that require real-time response inputs.
Machine learning (ML) at the Edge should be prioritised to leverage that constant flow of data and address the requirement for real-time responses based on that data. This should be aided by both new types of ML algorithms and by visual processing units (VPUs) being added to the network.
By leveraging ML on Edge networks in production facilities, for example, companies can look out for potential warning signs and do scheduled maintenance to avoid any nasty surprises. Remember many sensors are linked intrinsically to public safety concerns such as water processing, supply of gas or oil, and public transportation such as metros or trains.
Ecosystm research shows that deploying IoT has its set of challenges (Figure 1) – many of these challenges can be mitigated by processing data at the Edge.
Predictive analytics is a fundamental value proposition for IoT, where responding faster to issues or taking action before issues occur, is key to a high return on investment. So, using edge computing for machine learning located within or close to the point of data gathering can in some cases be a more practical or socially beneficial approach.
In IoT the role of an edge computer is to pre-process data and act before the data is passed on to the main server. This allows a faster, low latency response and minimal traffic between the cloud server processing and the Edge. However, a better understanding of the benefits of edge computing is required if it has to be beneficial for a number of outcomes.
If we can get machine learning happening in the field, at the Edge, then we reduce the time lag and also create an extra trusted layer in unmanned production or automated utilities situations. This can create more trusted environments in terms of possible threats to public services.
What kind of examples of machine learning in the field can we see?
Health systems can improve hospital patient flow through machine learning (ML) at the Edge. ML offers predictive models to assist decision-makers with complex hospital patient flow information based on near real-time data.
For example, an academic medical centre created an ML pipeline that leveraged all its data – patient administration, EHR and clinical and claims data – to create learnings that could predict length of stay, emergency department (ED) arrival models, ED admissions, aggregate discharges, and total bed census. These predictive models proved effective as the medical centre reduced patient wait times and staff overtime and was able to demonstrate improved patient outcomes. And for a medical centre that use sensors to monitor patients and gather requests for medicine or assistance, Edge processing means keeping private healthcare data in-house rather than sending it off to cloud servers.
A retail store could use numerous cameras for self-checkout and inventory management and to monitor foot traffic. Such specific interaction details could slow down a network and can be replaced by an on-site Edge server with lower latency and a lower total cost. This is useful for standalone grocery pop-up sites such as in Sweden and Germany.
In Retail, k-nearest neighbours is often used in ML for abnormal activity analysis – this learning algorithm can also be used for visual pattern recognition used as part of retailers’ loss prevention tactics.
Working with the data locally on the Edge, creates reduced latency, reduced cloud usage and costs, independence from a network connection, more secure data, and increased data privacy.
Cloud and Edge computing that uses machine learning can together provide the best of both worlds: decentralised local storage, processing and reaction, and then uploading to the cloud, enabling additional insights, data backups (redundancy), and remote access.
The last year has really pushed the Education sector into transforming both its teaching and learning practices. The urgency of the situation accelerated the use of networking to extend the reach and range of educational opportunities for remote learning.
Education technology has rushed to embrace opportunities to facilitate a new normal for Education. This new normal must enable and support education access, experiences, and outcomes as well as aid in developing strong relationships within Education ecosystems.
Education technology, commonly known as EdTech, focuses on leveraging emerging technologies like cloud and AI to deliver interactive and multimedia coursework over online platforms. This also requires a state-of-the-art network to support. 5G provides instantaneous access to cloud services. Use of 5G – as well as network function virtualisation (NFV), network slicing, and multi-access edge computing (MEC) – has the capability of delivering significant performance benefits across these emerging educational applications and use cases.
At present, many educational institutions are aware of the possibilities, but are not active users of 5G network infrastructure (Figure 1).
Educational institutions plan to do some near-term investments but are not clear in what areas to apply the enhanced capabilities (Figure 2).
Role of the Network in Adaptive Learning
In their recent whitepaper, network provider Ciena talks about “the concept of an adaptive learning strategy – a technology-based teaching method that replaces the traditional one-size-fits-all teaching style with one that is more personalised to individual students. This approach leverages next-generation learning technologies to analyse a student’s performance and reactions to digital content in real-time, and modifies the lesson based on that data.”
To create an adaptive learning strategy that can be individualised, these learners need to be enabled by technology to be immersed in a learning experience, complete with multimedia and access to a knowledge base for information. And this is where a solid 5G network implementation can create access and bandwidth to the resources required.
Example of 5G and Immersive Learning
An example of adaptive learning where the technology not only supports but challenges the learner can be found in a BT-led new immersive classroom developed within the Muirfield Centre in Cumbernauld, North Lanarkshire, using innovative technology to transform a classroom into an engaging and digital learning environment.
Pupils at Carbrain Primary School, Cumbernauld, were the first to dive into the new experience with an underwater lesson about the ocean. The 360-degree room creates a digital projection that uses all four classroom walls and the ceiling to bring the real-world into an immersive experience for students. The concept aims to push beyond traditional methods of teaching to create an inclusive digital experience that helps explain abstract and challenging concepts through a 3D model. It will also have the potential to support students with learning difficulties in developing imagination, creative and critical thinking, and problem-solving skills. BT has deployed its 5G Rapid Site solution to support 5G innovation and digital transformation of UK’s Education sector. The solution is made possible through the EE 5G network which brings ultrafast speeds and enhanced reliability to classrooms.
5G is expected to provide network improvement in the areas of latency, energy efficiency, the accuracy of terminal location, reliability, and availability – therefore creating the ability to better leverage cloud capacity.
With the greater bandwidth that 5G provides, learners and instructors, can connect virtually from any location with minimal disruption with more devices than on previous networks. This allows students to enjoy a rich learning experience and not be disadvantaged by their location for remote learning, or by the uncertainty of educational access. This also provides more possibilities of exploration and discovery beyond the physical confines of the classroom and puts those resources in the hands of eager learners.
As educational institutions reopen, institutions are looking at ways to redesign the education experience. Connected devices are helping schools and universities expand the boundaries of education. Explore what the IoT-enabled future of education would look like
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.
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.
We are heading into the one-year anniversary of global COVID confinements. This confinement period has seen the Hospitality industry impacted strongly by the lack of mobility of populations and government regulations. Hotels had previously used a consistent flow of booking and revenue information using historical and current pricing data from distribution and revenue management tools. They adapted in the “new normal” and the evolution of hotel infrastructure during this period – forced by necessity – has led them to try to create a contactless, more automated interaction, both for efficiency and for the work-from-home status of many employees.
Ecosystm research shows the digital technology focus of the industry to address the necessary shifts, in 2021 (Figure 1).
Distribution Data in the New Normal
Hotels are still struggling to get a clear overview of demand forecasting. Their data infrastructure is evolving and will continue to evolve to tackle this problem. The reliance on distribution information had to shift as fluidity in bookings could not rely on historical norms.
Hotels use a complex structure of promotion via distribution channels. This included direct booking via websites or central call centres, and use of online travel agents (OTAs), bed banks and wholesalers. That mix of channels was monitored and managed by the properties to leverage across these channels to optimise room occupancy. Over the past decades there has been an increased reliance on OTAs. But in more recent years, many hotel players have pushed back, promoting direct bookings made through own website booking engines or other direct means.
The pandemic has disrupted this complex orchestration of data. Moving from 65-75% occupancy to 10-15% was not financially viable for hotels. Because the pandemic reduced demand, both direct booking and OTA bookings have grown their share at the expense of other channels such as bed banks and global distribution systems (GDS). Guests wanted confirmation of the status of the hotel and what services were available, so data with extra content from the hotel itself or frequently updated OTA services were reliable.
Building Better Bundles and Contact Points
The goals for many hotels were to create frictionless digital customer journey (preferably by brand), leveraging existing infrastructures and integrating them to mobile apps, more robust CRM, and a more flexible property management set of tools. Part of that integration was having newly launched hygiene initiatives and branding those as part of the offering.
New bundles and packages were created to deal with the hygiene constraints and the new form of guest stays (daycation, staycation, remote learning) that have developed from the pandemic conditions.
Workcations using the hotel facilities as a workplace became attractive for those stuck at home with many interruptions. InterContinental Hotels Group, Marriott and Accor are among the major names that have launched or are considering monthly payment plans, as the hotel industry tries to attract restless remote workers ready for a change of scene.
The disconnect in guest information is being addressed by rebuilding the infrastructure of the guest journey – tracking their pre-stay investigation and booking interaction, the kind of on-property engagement they have with the hotel and its staff, their in-room experience, and their sharing of feedback on social media post-stay are all part of their guest experience.
Multiple business priorities will guide the industry in 2021 (Figure 2).
For the hotels serving different customer segments, specific actions were initiated.
- For the economy hotel chains, the flow of customers was not that significantly different, but how they booked and how many rooms they needed changed. This was handled more at the individual hotel property level as different COVID constraints applied to different regions.
- Larger chains already had their property management systems (PMS) set up as tied to a centralised structure, but a chunk of their business (leisure, corporate and business events) was directly tied to the restrictions on the domestic population and inability to access international guests.
- For luxury brands, it was a bit of a challenge as the hygiene aspect impacted the use of several extras that luxury brands rely on, such as spas, one-to-one interaction and facilities.
- Independent hotels needed some guidance that they were not getting from historical norms. Many went to external infrastructure providers to try to create workflow processes that would help them stay afloat.
Technology investments: Some Examples
One of the first concerns of regional travellers was the operational status of the hotel. One example of a digital investment was the Louvre Hotels Group, Europe’s second-largest hotel group that used used its ‘Résa Pro’ dedicated reservation platform for working professionals. It showed the listing of available accommodation per city and region for business travellers to meet the accommodation and catering needs of retail and sales professionals. Using this digital platform, companies could locate the Group’s open hotels in the city or region of their choice and see what guest offering best suited their requirements.
This webcast of Radisson’s Remy Merckx and Managing Director Sally Richards from RaspberrySky is a great example of building a digital platform to restructure the guest experience. Radisson outsourced the building of a digital platform that linked their eight hotel brands under one platform for a consistent digital experience, leveraging mobile, social and cloud technologies. The higher engagement rate with the mobile app and the chatbot helped create the contactless experience the guests are now looking in their accommodation journeys.
Many brands are now focusing on app-centric approaches for the guests, adding the value of human engagement for the more complex tasks. The emphasis is on the brand and digitising the guest journey to make it more customer-centric. This has been a time of reflection for some of the more organised hotel chains to make the time investment into the digital journey, upskill and upscale their operations to be in line with customer engagement.
New Normal for Hotel Stays
But not every independent hotel or small hotel chain had that financial investment to make during this period. According to Ecosystm data, approximately 41% of hospitality firms put their digital transformation on hold in 2020 – higher than any other industry that we cover. Technologies that will see increased investments in 2021 included cloud collaboration (44%) and cloud enterprise solutions (23%).
What does cloud have to do with this? Cloud is part of the infrastructural investment that allows the Hospitality industry to connect and enable its participants throughout the ecosystem, enabling mobile and social as well. This enables service providers to engage with intermediary partners, travel agents and consolidators and consumers, hyperconnecting in ways that provide convenience, ease of use and seamless information retrieval to bed banks and timetables, from business rules to collaborative mapping of codes.
This use of technology transforms the elements of inventory and availability into experiences and destinations.
- Messaging tools help harmonise communication across the network.
- Monitoring apps manage factors that impact distribution health, including rate integrity, availability, and visibility.
- AI – for example in the form of voice assistants – helps guide consumers and partners to timely information and decision making.
But it will still be a blend of digital solutions and human interaction, where humans add the core competency and collective knowledge, and technology provides the seamless data exchange and network connectivity.
- Sally Richards at RaspberrySky
- Anders Johansson at Hospitality Visions
- Mark Haywood and Ankit Chaturvedi at RateGain
New Normal for The Hospitality Industry
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There is a lot of hope on AI and automation to create intellectual wealth, efficiency, and support for some level of process stability. After all, can’t we just ask Siri or Alexa and get answers so we can make a decision and carry on?
Automation has been touted as the wonder formula for workplace process optimisation. In reality it’s not the quick fix that many business leaders desire. But we keep raising the bar on expectations from automation. Investments in voice technologies, intelligent assistants, augmented reality and touchscreens are changing customer experience (Figure 1). Chatbots are ubiquitous, and everything has the potential to be personalised. But will they solve our problems?
100 percent automation is not effective
Let’s first consider using automation to replace face-to-face interactions. There was a time when people were raving about the check-in experience at some of the hotels in Japan where robots and automated systems would take care of the check-in, in-stay and check-out processes. Sounds simple and good? Till 2019, if you checked into the Henn-na Hotel in Japan, you would be served and taken care of by 243 robots. It was viewed by many as a template for what a fully automated hotel could look like in the future.
The hotel had an in-room voice assistant called Churi. It could cope with basic commands, such as turning the lights on and off, but it was found to be deficient when guests started asking questions about places to visit or other more sophisticated queries. It was not surprising that the hotel decided to retire their robots. In the end it created more work for the hotel staff on-site.
People love the personal touch when they are in a hotel; and talking to someone at the front desk, requesting assistance from hotel staff, or even just a short chat over breakfast are some of the small nuances of why the emotional connection matters. Many quarantine hotels today use robots for food delivery, but the hotel staff is still widely available for questions. That automation is good, but you need the human intervention. So, getting the balance right is key.
Empathy plays a big role in delivering great Customer Experience
Similarly, there was a time when many industry observers and technology providers said that a contact centre will be fully automated, reducing the number of agents. While technologies such as Conversational AI have come along where you can now automate common or repetitive questions and with higher accuracy levels, the human agent still plays a critical role in answering the more complex queries. When the customer has a complicated question or request, then they will WANT to speak to an agent.
When it reaches a point where the conversation with the chatbot starts getting complicated and the customers need more help there should be the option – within the app, website or any other channel – to escalate the call seamlessly to a human agent. Sometimes, a chat is where the good experience happens – the emotional side of the conversation, the laughter, the detailed explanation. This human touch cannot be replaced by machines. Disgruntled customers are happier when an agent shows empathy. Front line staff and human agents act as the face of a company’s brand. Complete automation will not allow the individual to understand the culture of the company. These can be attained through conversations.
Humans as supervisors for AI – The New Workplace
Empathy, intuitiveness, and creativity are all human elements in the intelligence equation. Workers in the future will need to make their niche in a fluid and unpredictable environment; and translating data into action in a non-replicable way is one of the values of human input. The essence of engineering is the capacity to design around human limitations. This requires an understanding of how humans behave and what they want. We call that empathy. It is the difference between the engineer who designs a product, and the engineer who delivers a solution. We don’t teach our computer scientists and engineering students a formula for empathy. But we do try to teach them respect for both the people and the process.
For efficiency, we turn to automation of processes, such as RPA. This is designed to try to eradicate human error and assist us in doing our job better, faster and at a lower cost by automating routine processes. If we design it right, humans take the role of monitoring or supervisory controlling, rather than active participation.
At present, AI is not seen as a replacement for our ingenuity and knowledge, but as a support tool. The value in AI is in understanding and translating human preferences. Humans-in-the-loop AI system building puts humans in the decision loop. They also shift pressure away from building “perfect” algorithms. Having humans involved in the ethical norms of the decision allows the backstop of overly orchestrated algorithms.
That being said, the astute use of AI can deepen insights into what truly makes us human and can humanise experiences by setting a better tone and a more trusted engagement. Using things like sentiment analysis can de-escalate customer service encounters to regain customer loyalty.
The next transformational activity for renovating work is to advance interactions with customers by interpreting what they are asking for and humanising the experience of acquiring it which may include actually dealing with a human contact centre agent – decisions that are supported at the edge by automation, but at the core by a human being.
Ecosystm research shows that process automation will be a key priority for technology investments in 2021 (Figure 2).
With AI and automation, a priority in 2021, it will be important to keep these considerations in mind:
- Making empathy and the human connection the core of customer experiences will bring success.
- Rigorous, outcome-based testing will be required when process automation solutions are being evaluated. In areas where there are unsatisfactory results, human interactions cannot – and should not – be replaced.
- It may be easy to achieve 90% automation for dealing with common, repetitive questions and processes. But there should always be room for human intervention in the event of an issue – and it should be immediate and not 24 hours later!
- Employees can drive greater value by working alongside the chatbot, robot or machine.
Ecosystm Predicts: The Top 5 Customer Experience Trends for 2021
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The disruption that we faced in 2020 has created a new appetite for adoption of technology and digital in a shorter period. Crises often present opportunities – and the FinTech and Financial Services industries benefitted from the high adoption of digital financial services and eCommerce. In 2021, there will be several drivers to the transformation of the Financial Services industry – the rise of the gig economy will give access to a larger talent pool; the challenges of government aid disbursement will be mitigated through tech adoption; compliance will come sharply back into focus after a year of ad-hoc technology deployments; and social and environmental awareness will create a greater appetite for green financing. However, the overarching driver will be the heightened focus on the individual consumer (Figure 1).
2021 will finally see consumers at the core of the digital financial ecosystem.
Ecosystm Advisors Dr. Alea Fairchild, Amit Gupta and Dheeraj Chowdhry present the top 5 Ecosystm predictions for FinTech in 2021 – written in collaboration with the Singapore FinTech Festival. This is a summary of the predictions; the full report (including the implications) is available to download for free on the Ecosystm platform.
The Top 5 FinTech Trends for 2021
#1 The New Decade of the ‘Empowered’ Consumer Will Propel Green Finance and Sustainability Considerations Beyond Regulators and Corporates
We have seen multiple countries set regulations and implement Emissions Trading Systems (ETS) and 2021 will see Environmental, Social and Governance (ESG) considerations growing in importance in the investment decisions for asset managers and hedge funds. Efforts for ESG standards for risk measurement will benefit and support that effort.
The primary driver will not only be regulatory frameworks – rather it will be further propelled by consumer preferences. The increased interest in climate change, sustainable business investments and ESG metrics will be an integral part of the reaction of the society to assist in the global transition to a greener and more humane economy in the post-COVID era. Individuals and consumers will demand FinTech solutions that empower them to be more environmentally and socially responsible. The performance of companies on their ESG ratings will become a key consideration for consumers making investment decisions. We will see corporate focus on ESG become a mainstay as a result – driven by regulatory frameworks and the consumer’s desire to place significant important on ESG as an investment criterion.
#2 Consumers Will Truly Be ‘Front and Centre’ in Reshaping the Financial Services Digital Ecosystems
Consumers will also shape the market because of the way they exercise their choices when it comes to transactional finance. They will opt for more discrete solutions – like microfinance, micro-insurances, multiple digital wallets and so on. Even long-standing customers will no longer be completely loyal to their main financial institutions. This will in effect take away traditional business from established financial institutions. Digital transformation will need to go beyond just a digital Customer Experience and will go hand-in-hand with digital offerings driven by consumer choice.
As a result, we will see the emergence of stronger digital ecosystems and partnerships between traditional financial institutions and like-minded FinTechs. As an example, platforms such as the API Exchange (APIX) will get a significant boost and play a crucial role in this emerging collaborative ecosystem. APIX was launched by AFIN, a non-profit organisation established in 2018 by the ASEAN Bankers Association (ABA), International Finance Corporation (IFC), a member of the World Bank Group, and the Monetary Authority of Singapore (MAS). Such platforms will create a level playing field across all tiers of the Financial Services innovation ecosystem by allowing industry participants to Discover, Design and rapidly Deploy innovative digital solutions and offerings.
#3 APIfication of Banking Will Become Mainstream
2020 was the year when banks accepted FinTechs into their product and services offerings – 2021 will see FinTech more established and their technology offerings becoming more sophisticated and consumer-led. These cutting-edge apps will have financial institutions seeking to establish partnerships with them, licensing their technologies and leveraging them to benefit and expand their customer base. This is already being called the “APIficiation” of banking. There will be more emphasis on the partnerships with regulated licensed banking entities in 2021, to gain access to the underlying financial products and services for a seamless customer experience.
This will see the growth of financial institutions’ dependence on third-party developers that have access to – and knowledge of – the financial institutions’ business models and data. But this also gives them an opportunity to leverage the existent Fintech innovations especially for enhanced customer engagement capabilities (Prediction #2).
#4 AI & Automation Will Proliferate in Back-Office Operations
From quicker loan origination to heightened surveillance against fraud and money laundering, financial institutions will push their focus on back-office automation using machine learning, AI and RPA tools (Figure 3). This is not only to improve efficiency and lower risks, but to further enhance the customer experience. AI is already being rolled out in customer-facing operations, but banks will actively be consolidating and automating their mid and back-office procedures for efficiency and automation transition in the post COVID-19 environment. This includes using AI for automating credit operations, policy making and data audits and using RPA for reducing the introduction of errors in datasets and processes.
There is enormous economic pressure to deliver cost savings and reduce risks through the adoption of technology. Financial Services leaders believe that insights gathered from compliance should help other areas of the business, and this requires a completely different mindset. Given the manual and semi-automated nature of current AML compliance, human-only efforts slow down processing timelines and impact business productivity. KYC will leverage AI and real-time environmental data (current accounts, mortgage payment status) and integration of third-party data to make the knowledge richer and timelier in this adaptive economic environment. This will make lending risk assessment more relevant.
#5 Driven by Post Pandemic Recovery, Collaboration Will Shape FinTech Regulation
Travel corridors across border controls have started to push the boundaries. Just as countries develop new processes and policies based on shared learning from other countries, FinTech regulators will collaborate to harmonise regulations that are similar in nature. These collaborative regulators will accelerate FinTech proliferation and osmosis i.e. proliferation of FinTechs into geographies with lower digital adoption.
Data corridors between countries will be the other outcome of this collaboration of FinTech regulators. Sharing of data in a regulated environment will advance data science and machine learning to new heights assisting credit models, AI, and innovations in general. The resulting ‘borderless nature’ of FinTech and the acceleration of policy convergence across several previously siloed regulators will result in new digital innovations. These Trusted Data Corridors between economies will be further driven by the desire for progressive governments to boost the Digital Economy in order to help the post-pandemic recovery.
Environmental, social, and governance (ESG) ratings towards investment criteria have become popular for potential investors to evaluate companies in which they might want to invest. As younger investors and others have shown an interest in investing based on their personal values, brokerage firms and mutual fund companies have begun to offer exchange-traded funds (ETFs) and other financial products that follow specifically stated ESG criteria. Passive investing with robo-advisors such as Betterment and Wealthfront have also used ESG criteria to appeal to this group.
The disruption caused by the pandemic has highlighted for many of us the importance of building sustainable and resilient business models based on multi-stakeholder considerations. It has also created growing investor interest in ESG.
ESG signalling for institutional investors
The increased interest in climate change, sustainable business investments and ESG metrics is partly a reaction of the society to assist in the global transition to a greener and more humane economy in the post-COVID era. Efforts for ESG standards for risk measurement will benefit and support that effort.
A recent study of asset managers by the investment arm of Institutional Shareholder Services (ISS) showed that more than 12% of respondents reported heightened importance of ESG considerations in their investment decisions or stewardship activities compared to before the pandemic.
In the area of hedge funds, there has been an increased demand for ESG-integrated investments since the start of COVID-19, according to 50% of all respondents of a hedge fund survey conducted by BNP Paribas Corporate and Institutional Banking of 53 firms with combined assets under management (AUM) of at least USD 500B.
ESG criteria may have a practical purpose beyond any ethical concerns, as these criteria may be able to help avoidance of companies whose practices could signal risk. As ESG gets more traction, investment firms such as JPMorgan Chase, Wells Fargo, and Goldman Sachs have published annual reports that highlight and review their ESG approaches and the bottom-line results.
But even with more options, the need for clarity and standards on ESG has never been so important. In my opinion, there must be an enhanced effort to standardise and harmonise ESG rating metrics.
How are ESG ratings made?
ESG ratings need both quantitative and qualitative/narrative disclosures by companies in order to be calculated. And if no data is disclosed or available, companies then move to estimations.
No global standard has been defined for what is included in a given company’s ESG rating. Attempts at standardising the list of ESG topics to consider include the materiality map developed by the Sustainable Accounting Standard Board (SASB) or the reporting standards created by the Global Reporting Initiative (GRI). But most ESG rating providers have been defining their own materiality matrices to calculate their scores.
Can ESG scoring be automatically integrated?
Just this month, Morningstar equity research analysts announced they will employ a globally consistent framework to capture ESG risk across over 1,500 stocks. Analysts will identify valuation-relevant risks for each company using Sustainalytics’ ESG Risk Ratings, which measure a company’s exposure to material ESG risks, then evaluate the probability those risks materialise and the associated valuation impact. ESG rating firms such as MSCI, Sustainalytics, RepRisk, and ISS use a rules-based methodology to identify industry leaders and laggards according to their exposure to ESG risks, as well as how well they manage those risks relative to peers.
Their ESG Risk Ratings measure a company’s exposure to industry-specific material ESG risks and how well a company is managing those risks. This approach to measuring ESG risk combines the concepts of management and exposure to arrive at an assessment of ESG risk – the ESG Risk Rating – which should be comparable across all industries. But some critics of this form of approach feel it is still too subjective and too industry-specific to be relevant. This criticism is relevant when you understand that the use of the ESG ratings and underlying scores may in future inform asset allocation. How might this better automated and controlled? Perhaps adding some AI might be useful to address this?
In one example, Deutsche Börse has recently led a USD 15 million funding round in Clarity AI, a Spanish FinTech firm that uses machine learning and big data to help investors understand the societal impact of their investment portfolios. Clarity AI’s proprietary tech platform performs sustainability assessments covering more than 30,000 companies,198 countries,187 local governments and over 200,000 funds. Where companies like Cooler Future are working on an impact investment app for everyday individual users, Clarity AI has attracted a client network representing over $3 trillion of assets and funding from investors such as Kibo Ventures, Founders Fund, Seaya Ventures and Matthew Freud.
What about ESG Indices? What do they tell us about risk?
Core ESG indexing is the use of indices designed to apply ESG screening and ESG scores to recognised indices such as the S&P 500®, S&P/ASX 200, or S&P/TSX Composite. SAM, part of S&P Global, annually conducts a Corporate Sustainability Assessment, an ESG analysis of over 7,300 companies. Core ESG indices can then become actionable components of asset allocation when a fund or separately managed accounts (SMAs) provider tracks the index.
Back in 2017, the Swiss Federal Office for the Environment (FOEN) and the State Secretariat for International Finance (SIF) made it possible for all Swiss pension funds and insurance firms to measure the environmental impact of their stocks and portfolios for free. Currently, these federal bodies are testing use case with banks and asset managers. Its initial activities will be recorded in an action plan, which is due to be published in Spring 2021.
How can having a body of sustainable firms help create ESG metrics?
Creating ESG standard metrics and methodologies will be aided when there is a network of sustainable companies to analyse, which leads us to green fintech networks (GFN) of companies interested in exploring how their own technology investments can be supportive of ESG objectives. Switzerland is setting up a Green Fintech Network to help the country take advantage of the “great opportunity” presented by sustainable finance. The network has been launched by SIF alongside industry players, including green FinTech companies, universities, and consulting and law firms. Stockholm also has a Green Fintech Network that allows collaboration towards sustainability goals.
We should be curious about how ESG can provide decision-oriented information about intangible assets and non-financial risks and opportunities. More information and data from ESG data providers like SAM, combined with automation or AI tools can potentially provide a more complete picture of how to measure the long-term sustainable performance of equity and fixed income asset classes.
Singapore FinTech Festival 2020: Investor Summit
For more insights, attend the Singapore FinTech Festival 2020: Investor Summit which will cover topics tied to 2021 Investor Priorities, and Fundraising and exit strategies
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).
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.
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).
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.
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.
Singapore FinTech Festival 2020: Infrastructure Summit
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.