Setting and achieving Sustainability goals is complex in BFSI. To be truly sustainable, organisations need to:
- Reduce internal energy consumption and carbon footprint
- Fund the transition to decarbonisation in high emission industries
- Introduce “green” customer products and services
- Monitor carbon data for financed emissions
Data and AI have the potential to assist in achieving these objectives, provided they are used effectively. Here is how.
Download ‘Driving Sustainability with Data and AI in Financial Services’ as a PDF
Leading Banking and Financial Services organisations play a crucial role in financing sustainability transition. They have the infrastructure and resources to kickstart their own sustainability journey. But beyond that, they also have a greater role in building a sustainable value chain.
This extends to helping the traditional economy to transition; green investments to promote organisations with the right intentions; and empowering their customers to make environmentally-friendly choices.
As a technology leader in BFSI, you are an integral part of your organisation’s sustainability journey. Here are 5 ways in which BFSI tech leaders can support their organisations to turn sustainability intentions into reality.
Align tech with business goals and strategy. Think like a business leader and understand larger goals beyond technology deployments to empower your team.
View reporting as more than a checklist. You are in an ideal position to demonstrate the value of data insights beyond reporting mandates to the leadership team – link them to larger business outcomes.
Build intelligence into your facilities and assets. Consider investing in an intelligent enterprise asset management solution to automate asset and infrastructure management, remotely monitor and manage asset operations, and achieve sustainable business outcomes.
Automate your infrastructure allocation. You are increasingly using FinOps tools and other predictive analytics dashboards for cost and resource optimisation – extend the use for greater energy efficiency.
Understand your organisation’s unique sustainability journey. Seek independent opinion from third parties to empower your organisation to take the first step in the sustainability strategy, derive insights from data assets, and create market differentiation.
Read on to find more.
Download 5 Sustainability Actions for BFSI Tech Leaders as a PDF
The Internet of Things (IoT) solutions require data integration capabilities to help business leaders solve real problems. Ecosystm research finds that the problem is that more than half of all organisations are finding integration a key challenge – right behind security (Figure 1). So, chances are, you are facing similar challenges.
This should not be taken as a criticism of IoT; just a wake-up call for all those seeking to implement what has long been test-lab technology into an enterprise environment. I love absolutely everything about IoT. IT is an essential technology. Contemporary sensor technologies are at the core of everything. It’s just that there are a lot of organisations not doing it right.
Like many technologists, I was hooked on IoT since I first sat in a Las Vegas AWS re: invent conference breakout session in 2015 and learned about MQTT protocols applied to any little thing, and how I could re-order laundry detergent or beer with an AWS button, that clumsy precursor to Alexa.
Parts of that presentation have stayed with me to this day. Predict and act. What business doesn’t want to be able to do that better? I can still see the room. I still have those notes. And I’m still working to help others embrace the full potential of this must-have enterprise capability.
There is no doubt that IoT is the Cinderella of smart cities. Even digital twinning. Without it, there is no story. It is critical to contemporary organisations because of the real-time decision-making data it can provide into significant (Industry 4.0) infrastructure and service investments. That’s worth repeating. It is critical to supporting large scale capital investments and anyone who has been in IT for any length of time knows that vindicating the need for new IT investments to capital holders is the most elusive of business demands.
But it is also a bottom-up technology that requires a top-down business case – a challenge also faced by around 40% of organisations in the Ecosystm study – and a number of other architectural components to realise its full cost-benefit or capital growth potential. Let’s not quibble, IoT is fundamental to both operational and strategic data insights, but it is not the full story.
If IoT is the belle of the smart cities ball, then integration is the glass slipper that ties the whole story together. After four years as head of technology for a capital city deeply committed to the Smart City vision, if there was one area of IoT investment I was constantly wishing I had more of, it was integration. We were drowning in data but starved of the skills and technology to deliver true strategic insights outside of single-function domains.
This reality in no way diminishes the value of IoT. Nor is it either a binary or chicken-and-egg question of whether to invest in IoT or integration. In fact, the symbiotic market potential for both IoT and integration solutions in asset-intensive businesses is not only huge but necessary.
IoT solutions are fundamental contemporary technologies that provide the opportunity for many businesses to do well in areas they would otherwise continue to do very poorly. They provide a foundation for digital enablement and a critical gateway to analytics for real-time and predictive decision making.
When applied strategically and at scale, IoT provides a magical technology capability. But the bottom line is that even magic technology can never carry the day when left to do the work of other solutions. If you have already plunged into IoT then chances are it has already become your next data silo. The question is now, what you are going to do about it?
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?
Healthcare
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.
Retail
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.
Summary
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.
