Business
A journey into the heart of sustainable practices
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By Rosemary Thomas, Senior Technical Researcher, AI Labs, Version 1
Artificial Intelligence is a transformative force that is reshaping our daily lives. It serves as an instrument of change, driving innovation across various sectors by automating tasks, providing insightful data analysis, and enabling new forms of interaction. AI is fostering a new era of efficiency, productivity, and creativity.
More importantly though, through transparency, ethical AI practices, and healthy privacy safeguards, AI can help to strengthen our trust in technology and its role in our daily lives. It is a catalyst for changing how society perceives sustainability, helping us predict and work towards a more sustainable, ethical future.
Making a difference with AI for good
‘AI for good’ pertains to the use of AI technologies to help solve specific societal challenges and contribute towards making people’s lives better. It leverages the strength of AI to address issues like economic hardship, physical and mental wellbeing, academic achievement, and the preservation of nature.
For businesses, ‘AI for good’ can mean using AI to contribute towards environmental, social, and governance (ESG). Used correctly, AI can help to create sustainable strategies, powering solutions that present a greater advantage to society. It can also help with ESG reporting, which has become a highly time-consuming process involving data collection, the use of multiple frameworks, rapidly changing disclosure requirements, the integration of different models, reporting, and data analysis. By adding AI capabilities into this process, businesses can streamline their operations, increase data accuracy, and increase confidence in stakeholder engagement.
A recent example of an ‘AI for good’ application is the TNMOC Mate designed for The National Museum of Computing. The app offers a different experience, tailored to each guest meaning neurodiverse and non-English speaking individuals, as well as young children, can engage with the museum exhibits equally. This is a prime example of AI being used to bring societal advantage, helping people regardless of their background or abilities to enjoy the museum experience as intended, by using generative AI to present complex exhibit information in a way that is easily understandable.
Improving sustainability with green AI
Green AI is another aspect of ‘AI for good’. It relates to eco-friendly artificial intelligence algorithms, models or systems that use less computational power and emit lower carbon. It holds significant importance, given that a call for a thorough review of sustainability has arisen since Large Language Models (LLMs) have been criticised for their large carbon footprints and energy usage.
One way of implementing Green AI, is leveraging AI systems for efficient inventory and resource management. Machine Learning models can analyse the performance data of equipment and devices, then use this data to help extend the lifespan of resources and ensure their optimal utilisation. They can also schedule updates, hardware upgrades and maintenance proactively, avoiding potential downtime. Furthermore, these models can detect abnormalities in system operations early, allowing organisations to conduct timely maintenance. This can help them save time and money, as well as reducing wastage.
AI models also play a crucial role in computing and energy efficiency. They can analyse and optimise energy consumption patterns, leading to significant improvements in operational efficiency.
Additionally, while LLMs can contribute to carbon emissions, they can also serve as a powerful tool in battling climate change. LLMs can expedite research and innovation processes while maintaining a focus on sustainability. By generating creative and diverse solutions, they can help organisations stay at the forefront of their industries, while keeping sustainability at the core of their operations.
Measure more than carbon footprint in AI metrics
It is no doubt important to measure carbon emissions during the training of models. It can prove crucial when considering regional differences, as this plays a key role in promoting sustainability. But given the wide range of energy efficiency measurements across different AI algorithms, it is essential to include additional energy metrics along with traditional performance indicators. Choosing cloud providers that prioritise eco-friendliness is recommended, as well as strategically selecting the locations of data centres; the ultimate aim should be to foster the creation of AI solutions that are not only energy-efficient, but also environmentally friendly.
There is a call to standardise energy and carbon data reporting, which has been seen as a step towards encouraging social responsibility in the field of AI research and development. However, reporting cannot be done without accurate calculations, and carbon measurement is still in its early stages. When calculating the carbon footprint of a model, we should consider all variables equally, not just the final value of carbon. This is fundamental because, without this knowledge, we are ill-equipped to manage or improve it.
Fortunately, there are organisations working to solve this challenge. For example, The Green Software Foundation (GSF) is a non-profit organisation that aims to create a trusted ecosystem of people, standards, and best practices for developing green software and AI. The GSF have various tools and methods to help us measure and reduce the environmental impact such as the ‘Impact Framework’,‘Software Carbon Intensity’ (SCI) specification, and the Green Software Maturity Matrix[1].
Inclusion and diversity in the ethical use of AI
Safeguarding ethical use involves laying the groundwork for ethical standards, tackling biases in AI systems, prioritising transparency and explainability, and protecting against privacy concerns. The impact on human autonomy and responsibility gaps must also be contemplated, along with calculating the financial and environmental costs of training deep learning models.
There are implications arising from both responsible and irresponsible AI deployment, and it is important to illustrate examples of both sides in AI applications. In healthcare, for example, AI systems are used to assist medical professionals in transparent diagnosis and accountable treatment planning. This boosts patient care, promotes fair and informed decision-making, and contributes to better health outcomes.
In human resources, AI can be used for unbiased staffing processes. It moderates human biases, elevates inclusion and diversity, and promotes evenly balanced opportunities for all candidates.
Finally, in environmental monitoring, AI is used for the transparent monitoring and managing of eco-friendly dynamics, such as air and water quality, using sensors, transmitters, and data analytics. This helps to care for the environment, protect ecosystems and support the well-being of groups by addressing environmental hazards.
The non-ethical use of AI is more prevalent in surveillance systems, especially with facial recognition deployed in public spaces. This technology is used for mass surveillance, tracing individuals without their consent, and disregarding privacy rights, and in the US in particular this can be easily misused. AI tools can also be used in the creation of deepfakes to create dangerous misinformation.
Additionally, if the training data consists of historical biases, AI systems can spread and increase prejudice – resulting in unjust treatment which can excessively impact certain demographic communities. Finally, social engineering attacks using AI systems can be much more difficult to detect, and prompt injection attacks and LLM poisoning can intentionally cause harm and malice for a larger population.
Ethical, sustainable AI
As we collectively strive towards a sustainable future, AI is emerging as a key driving force. It is steering us towards solutions that are not only economically viable, but also environmentally sound and socially responsible.
Organisations should start to leverage sustainable AI, making sure that these technologies are having a positive impact of the ESG commitments, while ensuring they are created and used in a way that is ethical, fair, and transparent. In this journey, every algorithm we design, every model we train, and every AI-powered solution we deploy can take us one step closer to our goal of sustainability.
[1] https://medium.com/version-1/what-really-matters-for-green-calculations-a-practical-perspective-0bc0f5c7540c
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Business
Building Compliance into Business Culture is Essential in Fintech
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Source: Finance Derivative
Tetyana Golovata, Head of Regulatory Compliance at IFX Payments
Regulation plays a critical role in shaping the fintech landscape. From Consumer Duty and FCA annual risk reporting to APP fraud, the tectonic plates of the sector are shifting and whether you consider these regulations as benefiting or hindering the industry, businesses are struggling to keep up.
According to research by fraud prevention fintech Alloy, 93% of respondents said they found it challenging to meet compliance requirements, while in a new study by Davies a third of financial leaders (36%) said their firms had been penalised for compliance breaches in the year to June. With the FCA bringing in its operational resilience rules next March, it is more important than ever to ensure your company makes the grade on compliance.
Lessons from history
Traditionally, FX has struggled with the challenge of reporting in an ever-developing sector. As regulatory bodies catch up and raise the bar on compliance, responsible providers must help the industry navigate the changes and upcoming deadlines.
Fintechs and payments companies are entering uncharted waters – facing pressure to beat rivals by offering more innovative products. When regulators have struggled to keep up in the past, gaps in legislation haveallowed some opportunists to slip between the net, as seen in the collapse of FTX. Because of this, implementation and standardisation of the rules is necessary to ensure that innovation remains seen as a force for good, and to help identify and stamp out illegal activity.
Culture vs business
Culture has become a prominent factor in regulatory news, with cases of large fines and public censure relating to cultural issues. As the FCA’s COO Emily Shepperd, shrewdly observed in a speech to the finance industry, “Culture is what you do when no one is looking”.
Top-level commitment is crucial when it comes to organisational culture. Conduct and culture are closely intertwined, and culture is not merely a tick-box exercise. It is not defined by perks like snack bars or Friday pizzas; rather, it should be demonstrated in every aspect of the organisation, including processes, people, counterparties, and third parties.
In recent years, regulatory focus has shifted from ethics to culture, recognising its crucial role in building market reputation, ensuring compliance with rules and regulations, boosting client confidence, and retaining employees. The evolving regulatory landscape has significantly impacted e-money and payments firms, with regulations strengthening each year. Each regulation carries elements of culture, as seen in:
- Consumer duty: How do we treat our customers?
- Operational resilience: How can we recover and prevent disruptions to our customers?
- APP fraud: How do we protect our customers?
Key drivers of culture include implementing policies on remuneration, conflicts of interest, and whistleblowing, but for it to become embedded it must touch employees at every level.
This is showcased by senior stakeholders and heads of departments facilitating close relationships with colleagues across a company’s Sales, Operations, Tech and Product teams to build a collaborative environment.
Finance firms must recognise the trust bestowed on them by their customers and ensure the protection of their investments and data is paramount. Consumer Duty may have been a wake-up call for some companies, but progressive regulation must always be embraced and their requirements seen as a baseline rather than a hurdle.
Similarly, the strengthening of operational resilience rules and the upcoming APP fraud regulation in October are to be welcomed, increasing transparency for customers.
Compliance vs business
Following regulatory laws is often viewed as a financial and resource drain, but without proper compliance, companies are vulnerable to situations where vast amounts of money can be lost quickly.
A case in point is the proposed reimbursal requirement for APP fraud, which will mean payment firms could face having to pay compensation of up to £415,000 per case.
Complying not only safeguards the client and their money, but also the business itself. About nine in ten (88%) financial services firms have reported an increased compliance cost over the past five years, according to research from SteelEye. Embedding compliance earlier in business cultures can be beneficial in the long run, cutting the time and money needed to adapt to new regulations and preventing the stress of having to make wholesale changes rapidly.
Building a cross-business compliance culture
Compliance is a key principle at IFX, and we strive to be a champion in this area. In response to these challenges, the business restructured, establishing dedicated risk and regulatory departments, along with an internal audit function.
Regulatory compliance aims to support innovation by developing and using new tools, standards, and approaches to foster innovation and ensure product safety, efficacy, and quality. It has helped the firm to navigate the regulatory landscape while driving growth and maintaining high standards.
This organisational shift allowed each business line to own its own risk, with department partaking in tailored workshops designed to identify existing, new, and potential risk exposure. Shared responsibility for compliance is the only way to create a culture which values it. We see this as a great way for organisations to drive innovation while sticking to the rules.
Business
How AI virtual assistants are transforming education and training
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By Gregor Hofer, CEO and Co-founder at Rapport
What separates good doctors from excellent doctors, the type that might get five-star reviews if, like an Uber driver, their services were supported by a smartphone app?
Medical knowledge, expertise, and better outcomes are, of course, the most important factors. But – particularly when dealing with patients’ relatives, discussing risk assessment and imparting bad news – we shouldn’t underestimate the importance of bedside manner.
This might come naturally to some doctors but there are none for whom training isn’t useful, whether at medical school or on the job.
There will always be a place for real human interaction in this training, the type that involves role-play, with actors or colleagues playing out different scenarios that explore the most effective ways to handle difficult situations.
But what if this could be supplemented by more readily available and less resource-intensive experiences that simulate these training environments? And what if it could be applied across numerous sectors, industries and professions, of which there are a great many that could benefit from such an opportunity?
What might that mean for those instigating tricky conversations and, perhaps more importantly, those at the receiving end of them?
Advances in generative artificial intelligence – or GenAI – mean that these are no longer hypothetical questions.
There’s no limit to the type of person this technology could help, but we’ll review three – doctors, those working in corporate HR, and online students – to give a flavour of the benefits it brings.
Before we do, a quick word on how such applications work.
An overview of the technology
It all starts with data. With access to enough content, the type that you store and curate on your internal systems, large language models (LLMs) can be trained to find the most appropriate response to whatever user input they’re exposed to, whether in writing or spoken, and then you as a user can respond to that response, and so the cycle continues.
You’ll have experienced something similar using the likes of CharGPT, but because this is based on your own content, you’re more in control. (For simpler and more prescriptive scenarios, though, I’d add that with the best solutions, you can alternatively import predefined branching dialogue to keep your conversations on track.)
It doesn’t stop there, though; by tapping into a solution that’s supported by experts in linguistics and computer-aided animation, your colleagues can interact in real-time with avatars equipped with believable facial expressions, accurate lip-synching capabilities, natural gestures and the ability to detect emotions.
All of this adds to the user’s willing suspension of disbelief that they’re interacting with a real person, or AI avatar, thereby enhancing the effectiveness of their learning.
These innovations are reshaping how we approach learning and skill development in so many critical fields. We said we’d look at three. We’ll start by returning to medicine.
Medical training
AI assistants can supplement the way doctors are taught to break bad news to patients, one of the hardest things they’ll face in practice and, given its subjectivity, something that can’t easily be looked up in a textbook on anatomy or physiology.
As we said from the outset, this is easier for some doctors than others, but given the literal life-and-death nature of such conversations and the shattering impact that the death of a loved one can have on a relative, there’s always room to improve medics’ empathy and communication skills – which is exactly what this technology delivers.
By utilizing experiential AI tools, clinicians can better use their time, alleviate pressure, fatigue and burnout symptoms, and ultimately allow them to better serve their patients.
Corporate HR
In corporate HR, virtual assistants can significantly streamline and enhance the hiring and firing process, as well as any difficult conversation; whether it’s a tough review, a disciplinary hearing, letting down an employee about a promotion they’d applied for or any other scenario that might bring a bead of sweat to your forehead, it’s all about providing safe and cost-effective practice before doing it for real.
Tech research consulting firm Gartner recently found that more than three-quarters (76%) of HR leaders believe that if their organisation doesn’t adopt and implement AI solutions, such as generative AI, in the next 12 to 24 months, they’ll lag in organizational success compared to those that do, while 34% of HR leaders participating in their January benchmarking session said they were exploring potential use cases and opportunities when it came to generative AI.
If they do manage to adopt the right technology, the impact will be massive among those who deploy it wisely. After all, which company wouldn’t want to upskill its HR professionals in tangible soft skills such as empathy, communication, problem-solving, and conflict resolution in a controlled setting?
Online education
AI-powered tools can hugely boost student engagement in remote learning environments, and the research suggests that it comes close to rivalling in-person experiences. When you consider the staff-to-student ratios common in most educational settings, this should be no surprise – think how many students can fit into a lecture hall (even if they don’t always turn up!).
But we’re not necessarily talking about formal education; this applies equally to any informal setting in which someone needs to improve their education in some way.
With this technology, you can invent new ways to educate your students – or staff – by transforming lessons into experiences, using interactive characters reflective of the subject. This means you can increase user satisfaction and performance without compromising on content.
Whatever the scenario and whatever the use case, the chances are that if you have the right content in sufficient quantities, you can tap it for interactions that would otherwise be lacking in uniqueness or prohibitively expensive.
With AI virtual assistants, everyone’s a winner.
Business
How GenAI is Shaping the Future of Compliance
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Gabe Hopkins, Chief Product Officer, Ripjar
Generative AI or GenAI uses complex algorithms to create content, including imagery, music, text, and video with amazing results. Less well known are some of the ways in which it can transform data processing and task performance. This groundbreaking technology not only saves time, effort, and money, but has become a game-changer in enhancing operational efficiency and fostering innovation across various sectors.
However, some industries like anti-financial crime compliance – have been slow to adopt new innovations like GenAI, predominantly due to concerns over potential risks. In fact, they can even see it as a risk in itself. Legal, Compliance and Privacy leaders rank rapid GenAI adoption as their top issue in the next two years, all while other, less risk-averse organisations enjoy the upside of implementing GenAI in their systems.
This delay means many compliance teams are not taking advantage of AI tools that could revolutionise their processes and help them save up to 200 hours annually per user.
Entering the New Era of GenAI in Compliance
Teams in largely regulated sectors like banking and fintech face enormous pressures. Their responsibilities include identifying risks, such as sanctioned individuals and entities, updating policies to keep up with ever-evolving regulations, and handling expansive datasets. The high volume of this data makes manual reviews exhausting and susceptible to errors, which can lead to financial and reputational damage.
One way to overcome these challenges is by leveraging GenAI. For example, false positives – where a risk is raised incorrectly or false negatives, where a real risk is not flagged, are common issues caused by trying to deal with very high volumes of alerts and risk matches. Implementing GenAI can reduce these inaccuracies, significantly enhancing the efficiency and effectiveness of customer and counter-party screenings.
In practical terms, GenAI can reinvent how compliance tasks are performed. For instance, in drafting Suspicious Activity Report (SAR) narratives, where analysts need to justify suspicions in transactions, GenAI can help automate this writing process, combining human oversight with artificial efficiency. Platforms using GenAI excel in summarising vast amounts of data— crucial for tasks like screening adverse media, where they assist in identifying potential risks linked to negative information about clients.
Understanding the Opportunities of GenAI and Overcoming Fears
For the compliance sector, it’s a crucial time to explore how to incorporate GenAI effectively and securely without undue risks. Dispelling fears about data misuse, the high costs of initial model setups, and the ‘black box’ nature of AI models are central to this transition. Teams are particularly cautious about sharing sensitive data and the hidden biases that AI might carry.
Yet, some strategies can counter these challenges. By choosing suitable models that ensure robust security and privacy and adjusting these models within a solid statistical framework, biases can be mitigated. However, organisations will need to turn to external expertise – whether data scientists or qualified vendors – to support them in training and correctly deploying AI tools.
The latest advancements in GenAI suggest that virtual analysts powered by this technology are achieving, and sometimes surpassing, human-level accuracy. Despite ongoing concerns, which may slow adoption rates, the evident potential benefits suggest a bright future for compliance teams using GenAI. These technological innovations promise not only to improve speed and efficiency but also to enhance the capability of teams to respond and adapt swiftly.
Embracing GenAI will not only significantly elevate the effectiveness of compliance operations but also safeguard organisations against potential pitfalls while maintaining trust and integrity in their industry practices.
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Building Compliance into Business Culture is Essential in Fintech
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How AI virtual assistants are transforming education and training
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How GenAI is Shaping the Future of Compliance
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The Sustainability Carrot Could be More Powerful Than the Stick!
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