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SUSTAINABLE AI: WHY IT’S GOOD FOR BUSINESS

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By Nick Dale, Senior Director, Verne Global

 

Society at large is becoming increasingly aware of its environmental impact, recently highlighted by the crystal-clear water in Venice’s canals and the Himalayas being visible across smog-free skies from over 120 miles away, amidst the global shutdown. This concern has also extended to the finance industry, with environmental, social, and governance (ESG) criteria rising in importance for both business and investment decisions.

At the same time, the financial sector has also been a major adopter of another significant trend – the use of AI and machine learning to improve efficiency and results. AI is particularly useful to the finance industry, from optimising asset portfolios and underwriting loans, to assessing risk and spotting fraud. In fact, the financial sector is leading the UK in the use of AI, with the great majority of banks, insurance firms and other financial institutions using such technologies.

Surprisingly, these two trends may be at cross purposes because of AI’s hefty carbon footprint. Training one deep learning model for natural language processing can emit more than 626,000 pounds of carbon dioxide equivalent, which is nearly 5 times greater than the amount generated during the entire lifetime of a car (including the manufacture of the car itself), per 2019 University of Massachusetts research.

But sustainable AI innovation is possible if financial services organisations begin to understand why AI can have a negative impact on the environment, and what they can do to minimise that impact.

 

Nick Dale

AI’s appetite for power

The field of AI has been growing in leaps and bounds in the last decade, and no where more so than the finance industry. Financial institutions like Goldman Sachs, Morgan Stanley, and S&P Global are routinely using AI tools like Kensho’s for investment insight. Kensho’s algorithms can process 65 million question combinations, analysing over 90,000 world events – such as political events, economic reports, and monetary policy changes – and their impact on asset prices. Forbes reports that traders with access to Kensho’s AI-powered data were able to foresee a protracted drop in the British pound in the days after Brexit.

But as AI technology grows and develops, the computations behind it are also increasing in size and complexity. There has been a 300,000-fold increase in the computations required for deep learning research from 2012 to 2018, according to analysis conducted by the Allen Institute for AI. On top of that, these AI computing platforms can sometimes run 24-hours a day, necessitating days and even weeks of processing, plus trillions of attempts, to get the numbers lined up. As a result, these applications consume an enormous amount of energy in order to function, and require significant and constant access to power.

The carbon cost of AI becomes even greater when you factor in the energy required to keep computing equipment cool in order to prevent overheating that can impact performance and damage equipment. In a conventional data center, at least 40% of all energy consumed goes towards cooling.

 

Steps to minimise carbon impact of AI

Green – or greenwashing?

For businesses looking to reduce the environmental impact of their AI, the first step is to check the green credentials of the cloud providers and data centers that power these applications. Despite the “green” label, there’s no guarantee that a cloud provider or data center is powered entirely, or even partially, by green energy. Instead, these green claims can be more akin to a “carbon offset” programme, with energy providers offsetting the carbon they produce through tree planting or other similar programs.

Renewable energy sources

Instead of greenwashing, make sure that the data centers housing your AI compute are actually powered by renewable energy. In many Nordic countries, data centers are powered by renewable energy sources like hydroelectric and geothermal power. Iceland, in particular, uses 100% renewable energy with no nuclear power. These renewable energies are much less harmful to the environment because, unlike fossil fuels, they don’t cause pollution and don’t generate greenhouse gases. Not to mention, renewable energy is based on natural resources that can be replenished within an average human lifetime, as compared to fossil fuels, which can take thousands—or even millions—of years to replace.

Data center location

The next step is to look at the location of your data centers. Over 80% of compute doesn’t need to be near the end-user, and in those situations, choosing data center locations in cool climates has a significant impact on carbon emissions. In such cases, AI compute can be located in places like Iceland, which can utilise free-of-cost, natural cooling, due to its year-round cool, temperate climate.

This is in stark contrast to data centers located in hot climates, like Arizona in the US. With average high temperatures of 40° Celsius in the summer, data centers in climes like these need high-powered cooling systems in operation around the clock, often supported by up to 4 million gallons of water a day used to absorb heat through evaporation into cooling towers. As a result, when it doesn’t affect performance or accessibility, housing AI compute in data centers with natural cooling seems like an environmental no-brainer.

 

Better for the environment – and for business

As much as the financial sector is starting to embrace sustainability as a key ESG criteria in their corporate strategies, some may still view such efforts as an added cost to the expense side of the balance sheet. But the truth is, green AI presents financial services firms with an opportunity to align profit with purpose. By housing the servers that train AI models in data centers powered by renewable energy sources – connected to a reliable power grid –, businesses can substantially reduce energy expenses and benefit from predictable pricing.

As well, choosing locations with year-round, cool climates that allow natural cooling of powerful AI servers further minimises energy usage. When it comes to green AI, reducing environmental impact also lowers energy demands and costs – something that’s well worth the investment.

 

Technology

WHAT TO KNOW ABOUT ENHANCING THE ORDER-TO-CASH PROCESS WITH ARTIFICIAL INTELLIGENCE

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Mark Sheldon, Chief Technology Officer, Sidetrade

 

The global pandemic has meant companies everywhere have woken up to the fact that cash is king, leading to a renewed prioritisation of liquidity generation and cash conservation to survive.

One of the ways that companies have sought to secure cash flow is through improved Order-to-Cash (O2C) processes – helping enterprises minimise risks and maximise returns in their financial dealings.

 

But not all O2C processes are created equal. In fact, I see the current O2C market split into three segments:

  1. The first being the companies that employ manual siloed systems, who continue to work predominantly with Excel spreadsheets and even the postal system, and they continue to manually process customer data across a variety of siloed functions (sales, support, finance, etc.).
  2. The second segment, and where we start to see technology being leveraged, is a more digitised approach, whereby the basics of automation are employed, or digital collection media such as email is the norm. Firms taking advantage of these kinds of technologies can expect to benefit from improved efficiencies across the whole O2C process.
  3. The third and most mature segment however, and where the full benefits can be reaped, is when AI capabilities are built into the O2C process.

Mark Sheldon

In overlaying AI into this process, firms are able to leverage data and intelligent insights to supercharge their efficiencies even further. Teams can benefit from AI-powered recommendations that lead to optimum results, improved customer retention and overall streamlined O2C workflows. Customers can enjoy a much more sophisticated and effective end-to-end experience with their suppliers. And the business at large can benefit from significantly healthier cash flow, reduced bad debt and the enhanced ability to better forward plan.

But where to start when implementing AI technologies into your O2C process and what are the most important things you should be aware of?

 

Top tips for implementing AI-powered O2C systems

Firstly, it’s important to look past the hype of AI. It’s become a bit of a buzzword across all industries, and there are many vendors out there that label themselves as an AI provider, but simply don’t have the creds for it.

Fundamentally, without historical data, there is no AI. A company just starting out for example, might have the technical abilities to build an AI platform, but is highly unlikely to have the data sets required to feed it, and make it truly “intelligent”.

Secondly, it is critical to clarify the difference between robotic process automation (RPA) and AI. Many RPA vendors talk about AI and RPA interchangeably, but they’re not the same thing.

RPA is generally concerned with automating everyday processes – using software “bots” that you can set up to emulate a particular task; so it’s very much focused on automation of existing manual processes. Whilst there are some similarities in terms of efficiency benefits, it’s very different from what we do in AI. In AI, we use algorithms to intelligently recommend the best course of action, by taking the human thinking out of the system.

The risk with a purely RPA-based solution is that you end up automating ineffective or even damaging processes. For example, missing insight into where bottlenecks lie, or where siloed systems further amplify cash flow problems. So you could argue there is nothing truly intelligent about RPA.

Thirdly and finally, one of the biggest barriers that still exists today for anyone looking to employ AI in the O2C process, is the cultural challenges. Wherever we deploy AI, the number one challenge in terms of the rollout is the cultural belief and trust that AI is going to do a good job.

Fundamentally, AI-powered O2C software has been proven to improve key business metrics. With AI solutions, you can expect significantly reduced DSO, faster cash collection, and improved efficiencies by up to 50%.

What’s more, with AI, businesses can enjoy far more in-depth insights: understanding which suppliers aren’t paying fast enough, where to get cash into the business more efficiently, or identifying where the business opportunities for growth lie. All of this is powerful ammunition for CFOs looking to implement a cash-to-cash culture across the entire business, and a valuable aid in demonstrating the significant impact that AI in the O2C process can have across the business.

 

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Business

DIGITAL TICKETING: THE CHALLENGES AND OPPORTUNITIES FACING PTOS AND PTAS.

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Arnaud Depaigne, Product Manager, Smart mobility at Fime.

 

Transport ticketing has rapidly evolved in the digital age. As recently as the 1990s, closed loop systems based around paper tickets or tokens were the norm. This resulted in a poor user experience. Lines to purchase tickets were often long, and turnstile throughput was inefficient. Today, passengers can use a smartcard or even their phone as their ticket, utilizing contactless and Near Field Communication (NFC) functionality to tap-and-go.

This proliferation of digital ticketing has only been further accelerated over the last 18 months. The pandemic has presented Public Transport Operators (PTOs) and Public Transport Authorities (PTAs) with an urgent need for hygienic contactless solutions. As passenger numbers slowly begin to return, the ecosystem is presented with a unique opportunity to advance urban mobility and move towards a Mobility-as-a-Service (MaaS) model. However, with this also comes a series of challenges.

 

Reacting to changing user behavior

Arnaud Depaigne

Today’s consumer world is digital, global and on demand. Passengers want seamless integrated solutions that allow them to plan and pay for their transit using only the device in their pocket. Furthermore, the urban mobility ecosystem is seeing a rising demand for interoperable MaaS solutions that provide end-to-end transportation on a single ticket. Mobile ticketing must deliver on these expectations as well as being user friendly, reliable and secure.

In part, this is being achieved by changing the focal point of urban mobility from the station to the passenger themselves. This consumer-centric approach allows PTOs and PTAs to reconfigure their sales and distribution channels to meet the growing demand for digital solutions.

Mobility providers can achieve this by integrating Host Card Emulation (HCE) and NFC technologies into their ticketing solutions. More technologically literate passengers will already be familiar with digital wallets and contactless payments. This mitigates concerns about achieving widespread user adoption and means that any digital urban mobility solution could be rolled out at speed. Another benefit to this is that it significantly cuts costs for providers. As passengers no longer require mode-specific travel cards, everything is instead accessible on one device. Providers can therefore cut their expenditure on manufacturing the cards themselves. They can also scale back the on-the-ground resources allocated to support issuance.

 

Context is key

When rolling out a solution, providers must be mindful that each individual passenger has different needs. Cities have unique transit networks of varying sizes that require different approaches. Furthermore, any solution must be accessible to all demographics, from digital natives to those who are less technologically adept. They must also remain aware that not every passenger will have a bank account. Solutions must not exclude people. They must offer customers a range of options to make their payment.

Account-based ticketing (ABT) manages the consumer’s funds in the back-office account, making the payment automatically. This gives users flexibility to move between several fare media to make payments depending on what is most convenient at the time – be it by smartcard, mobile device or wearable. To this end, ABT solutions simplify maintenance logistics, improve security while also ultimately reducing the cost of urban mobility.

By moving from a stored value card system to an account-based approach, PTOs and PTAs can achieve “the holy grail of ABT” as it has been described by Visa. This system opens the door for future adjacent services by achieving interoperability between different fare media.

 

The importance of open standards

Open standards can offer a pathway to truly realizing seamless transport ticketing. With open standards, PTOs and PTAs remain in control of their ticketing network as the supply chain remains open to multiple solution providers. Providers can therefore avoid vendor lock-in and the issues that can present. Furthermore, an open standards approach means that PTOs and PTAs can evolve organically with the technology as it is rolled out. This allows them to remain agile and prepared for future challenges and developments.

 

The need for expertise

PTOs and PTAs will need to continue evolving with future technological developments. By remaining aware of the challenges that may lie ahead, they can put themselves in the best possible position to capitalize on opportunities. Infrastructure migration does not necessarily require huge investments, and with the right support, the transition can be made as smooth as possible.

Fime’s global expertise can help demystify and simplify ABT deployment. With over 20+ years of experience ensuring the efficient and successful implementation of card and mobile transaction services. Fime is well-equipped and experienced in supporting the transport market in delivering the next generation of transit ticketing solutions in a complex market.

 

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