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A journey into the heart of sustainable practices

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

2024: THE year for customer experience enhancement

Source: Finance Derivative

Rob Paisley, Director, Banking and Financial Services, SS&C Blue Prism 

How recently have you relayed to someone the immaculate service that your tax office, bank or insurance company provided you with? From renewing a bankcard, buying a house or undergoing an investment fund transfer, financial organisations are not noted for their NPS scores.

Nowadays, banking customers find the service inconvenient, due to errors, hidden fees, delays and fund-accessibility issues to name a few, not to mention that financial organisations must compete in a world where online shopping is only a few clicks away. In fact, in terms of satisfaction, customers rank their streaming and parcel delivery service higher. Highlighting the general dissatisfaction is the TrueDigital Quotient, standing at a meagre 25%, emphasising the consensus amongst customers regarding transactions processed wholly through digital channels.

And, while large financial organisations and banks are addressing enlarging customer satisfaction, decreasing operational costs and steering revenue growth by using artificial (AI) solutions at a future time, wouldn’t a more reasonable solution be to manage digital processes through investment in existing intelligent automation (IA)?

Neobanks and digital banks leverage intelligent automation for faster customer journeys. This includes ‘know your customer’ checks, digital onboarding, and seamless processes, catering to both digital and traditional customers effectively – meaning customers can get what they want quickly and without pain. For younger customers, this means digital banking, while traditional customers are provided with better service at a physical location that includes digital offerings.

In banking and finance, most companies think of RPA-IA as an efficiency tool, but significant opportunities often unexpectedly arise when they start to deploy it. Often, it’s the customer experience that benefits most as it’s not just about efficiency. Automation software can help re-imagine your offerings with the customer at the center of it. Amidst the AI rush, revisiting foundational basics before proceeding may be prudent, as IA establishes essential groundwork often overlooked.

Repeated shortcomings for the banking customer

From routine tasks like mortgage applications to specialised services, such as closing accounts, infrequent or one-time customer experiences, significantly shape long-term loyalty and recommendations.

Let me paint you a picture with a tangible example of why people might take their business elsewhere, to illustrate how today’s predominantly young customers are not brand-loyal, and seek the easiest route to fulfil their needs swiftly.

If you join a cloud-based digital bank that has no branches, all transactions will likely be delivered by a 24/7 customer support hotline. Certain banks like this also don’t do checking accounts, only high yield savings CDs and loans which many people are attracted to given preferrable interest rate offers. This all sounds great, but you still run into the infancy of some of these technologies.

To do a mobile cheque deposit, we’ve had clients say it might take 14 days to clear. That’s not good enough. Even two days isn’t good enough given the technology available for these processes. It may also require the customer to write a restrictive endorsement on the back of the check saying it can only be deposited at the specific bank. Once the endorsement is written, it can’t be taken anywhere else other than that bank. If they reject it, they don’t have branches, so customers can’t walk in and talk to a human being and talk to someone.

Anything that improves time to resolution in a self-service fashion on a digital channel helps, but in reality, it’s a dichotomy. How can you have a cashless society until you solve basic issues like that one? It’s a pain to transfer out and you don’t really want to, but lethargy is inherently baked into the system so anything that can speed up the process is going to improve the customer experience.

Dissatisfaction often goes unvoiced, with customers silently departing without notice. Many companies remain unaware until weeks later, indicating a blind spot in recognising and addressing evolving customer behaviour.

With so much money at stake why are organisations struggling to get it right? This year, customer experience takes center stage, with forward-thinking companies investing in process intelligence, business orchestration and automation. Those lagging lack measurement tools and awareness of their shortcomings. Banks excelling in this realm employ more than 500 digital workers and meticulously measure outcomes, while others trail behind with fewer than 10 or none at all.

Cash no longer reigns supreme

Northern Europe boasts the largest global digital banking market, with Sweden dominating with a 98% cashless economy. Nordea, a leading bank in the region, spearheads this transformation by prioritising customer-centricity around the concept of ‘the idea of something better’ through cutting-edge mobile and digital banking solutions. Despite its 200-year legacy, Nordea embraced online banking early on, and in 2015, it adopted banking automation software to revolutionise its operations. Some six million transactions are processed by its digital workforce, including simple tasks such as new card requests, reducing errors and costs, allowing Nordea to tailor its services based on customer preferences.

“It’s one of the key aspects where we want to be the leading bank. We have invested a lot into our mobile bank, which is regarded as the best in the Nordic markets,” says Ossi Leikola, Head of Operations at Nordea. “We also believe very much in a personal relationship with our customers – that’s why we’re very interested in omni-channel.”

Through Nordea’s employment of almost 400 workers and 450 automated solutions for its 10 million customers around the globe, customer satisfaction levels have transformed. Subsequently, by using SS&C Blue Prism intelligent automation, the bank is positioned as a regional leader.

Where customer experience is concerned, efficiency is crucial to retaining loyalty. Companies providing customers with prompt, precise interactions excel in the industry. Intelligent automation solutions streamline transactions, enhancing customer satisfaction, and therefore loyalty. In the current informed market, banks should prioritise use of tools on enhancement, or risk reputational damage to the organisation.

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Money laundering red flags: How to identify and combat financial crime

By Andrew Doyle, CEO, NorthRow

Money laundering, the process of disguising the proceeds of illegal activities as legitimate funds, is a grave financial crime that undermines the integrity of financial systems worldwide. 

When you consider that the National Crime Agency estimates that £10 billion of illegal money is laundered each year in the UK, financial institutions and regulatory authorities have a responsibility to be more adept at recognising the red flags indicative of these illicit activities. Understanding these warning signs is crucial in the ongoing battle to maintain financial integrity and protect the economy from the corrosive effects of money laundering. 

So, what exactly are the warning signs?

Unusual transactions

Financial activities that deviate significantly from a customer’s known income or business patterns is a clear warning sign. This can include large deposits, withdrawals, or transfers that seem inconsistent with their profile. 

Financial institutions need to scrutinise transactions in the context of their knowledge of the customer’s usual financial behaviour, risk profile and the nature of the business relationship. Any significant deviation should prompt a closer look to determine if the activity is legitimate or if it signals something more sinister.

Unexplained source of funds

Large sums of money appearing in a customer’s account from private or unfamiliar sources should raise immediate concerns. It is vital to look at how they acquired these funds and request supporting documentation such as bank statements, recently filed business accounts, or official documents like property or share sale records to verify any such transactions. 

When cash transactions are involved, the difficulty of tracing the origin of funds increases, making thorough due diligence even more critical. In such cases, the institution must ask whether the source of funds aligns with their knowledge of the customer and if there are any indications of criminal involvement.

Rapid movement of funds

When funds are swiftly transferred without a clear and justifiable business purpose, it can suggest an effort to conceal the true origin of the money. Sudden and unexplained changes in a customer’s transaction patterns, such as an abrupt increase in activity or a shift in transaction types, should also raise suspicion. These deviations may indicate attempts to disguise the nature of financial activities.

PEPs

Transactions involving Politically Exposed Persons (PEPs) are particularly high-risk due to the potential for corruption. PEPs include individuals holding prominent political positions and their close associates, who may be more susceptible to engaging in corrupt activities. These individuals often have access to substantial funds, making it easier for them to participate in money laundering schemes. Financial institutions must exercise enhanced due diligence when dealing with PEPs to mitigate the risk of being used to launder illicit gains.

Inconsistent documentation

Inconsistent documentation is another critical indicator of potential money laundering. This can include altered or forged documents, incompatible details between different records, or paperwork that does not align with the nature of the transaction. These inconsistencies suggest a lack of transparency and honesty in financial dealings, potentially indicating an effort to hide illicit origins or intentions. Financial institutions should be wary of any documentation that appears tampered with, or that provides conflicting information about a transaction.

Refusal to cooperate 

When customers are uncooperative or evasive in response to requests for additional information or documentation, it should raise immediate concerns. Avoiding straightforward questions about the purpose or source of funds, failing to provide necessary documents, or showing reluctance to clarify details can indicate a deliberate attempt to conceal illicit activities. Financial institutions must be prepared to report suspicious activities to the appropriate authorities for further investigation.

The presence of one or more of these red flags does not necessarily confirm money laundering but definitely warrants closer inspection. Financial institutions in the UK are legally required to implement robust procedures to detect and prevent money laundering. These measures include conducting thorough customer due diligence, continuously monitoring clients for any adverse changes to their risk profile, and reporting suspicious activities to relevant authorities.

Recognising and responding to money laundering red flags is essential for maintaining the integrity of the UK’s financial system. Financial institutions must remain vigilant, ensuring they have the procedures and expertise necessary to detect and address suspicious activities. By doing so, they can play a crucial role in combating financial crime and safeguarding the economy from the detrimental impacts of money laundering.

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The Human Advantage: Turning human-centred leadership into commercial success

By Helen Wada

We are living in a world where AI is becoming more prevalent, the economic environment is as challenging as it has ever been, yet organisations are at the same time being asked to become more “human-centric” and focus on their people.

A shift from performance to people

The 1980s and 1990s were characterised by a relentless performance culture, where metrics and outcomes were paramount. Autocratic leadership of the past gave way to a more collaborative approach as we entered the 21st century and we saw technology begin to disrupt the way in which we worked. Deliver more with less, work in a different way, grow the top line and reduce costs and technology was driving efficiency and growth.

Helen Wada

Today, as we look forward to 2025 and beyond, technology is once again shifting the dial, but there is also a real shift towards people, we are moving into a new era. The Human era.  Helen Wada, a top UK top executive business coach, who has spent more than 25 years in the corporate world working across professional services and with global organisations, is witnessing firsthand the need to prioritise the essentials of being human. 

The pandemic brought this sharply into focus as we think back to how so many within all kinds of professional settings kept the wheels in motion at a time of fear and uncertainty. Medical workers, civil servants and retail workers all continued while others were told to stay at home. Since then, there has been a significant shift in focus on prioritising humanness unlike ever before, yet the commercial imperative remains – and in some instances the commercial pressures are felt even more than before the pandemic.   

Combining the need to drive growth  while building a human centric culture

One of the main challenges businesses face is finding the middle ground between human-centred initiatives and commercial goals.

In March of this year, Forrester explored what human and technical skills will matter most to B2B Marketers…”Technical and AI analytical skills will no doubt have a crucial role to play, but those in B2B customer facing roles must develop soft skills such as self-efficacy, cognitive abilities, empathy and excellent communication. These human skills are vital for building strong relationships with clients, collaborating with team members and adapting to changing market dynamics.” In addition…we need leadership skills and business acumen….The reality is we need to think about developing that whole person.”

A Gartner survey conducted in 2022 found that 90% of HR leaders believe that to succeed in today’s working environment, leaders must focus on the human aspects of leadership. However, only 29% of employees report that their leader is a human leader.

According to Helen’s philosophy, these “human skills” that sales leaders require align completely to those that she developed through her executive coach training back in 2015.  Helen had always shied away from sales, preferring to focus on her technical expertise and delivery.  Yet, after training as an executive coach, she found a new confidence in having open-ended conversations with customers, building relationships and creating insight and value through the quality of her conversation and challenge.

This got her thinking, was there a way that coaching could prove to be the bridge between human-centric leadership and commercial focus

The Harvard Business Review, along with many other reports has highlighted the role of quasi-coaches; leaders who blend coaching with their managerial roles as pivotal to successful leadership.  But can this be taken one step further.

The sales leaders of tomorrow, not only require their technical expertise, their ability to collaborate and work with AI, they require these human skills, to connect with customers, be curious and create value.

Human-centred leadership in practice

Human-centred leadership requires an approach that looks at everyone as individuals. It is important to understand a person’s aspirations, values, and what drives them. This can be difficult where development programmes are delivered at scale with a one-size-fits-all approach.  Common coaching skills can be developed, yet the outcome of a coaching conversation is always personal and unique.

By themselves adopting a coaching mindset, leaders can demystify complex issues and foster a culture that supports both personal and professional growth. Helen’s thesis asserts that human-centred and commercial cultures do not have to be separate. Instead, they can “coexist harmoniously through coaching. By developing leaders as coaches, organisations can scale human-centric practices, as well as provide the skills required to foster commercial relationships, where connection, curiosity, challenge and collaboration are at the heart of working together.”

Scaling human-centric practices

At the heart of a coaching culture is the creation of personal responsibility and accountability.  Coaching, by its very nature encourages others to grow and thrive, creating a culture of trust and responsibility for everyone to play their role in their own personal growth and development.

By starting at the top, Helen highlights that coaching provides a framework that equips leaders with the skills to understand and support their teams effectively, as well as having better conversations with their clients, whether external or internal to the business.

This is particularly relevant in professional service or partnership environments, such as accounting, law, or engineering, where technical expertise is valued for promotion to a certain point, but to reach the next level of leadership requires an ability to build a different type of relationship with customers – often exploring areas outside of their comfort zone.

Coaching and coaching skills also support individuals deal with uncertainty, as Helen explored with a fellow coach, Paul Golding in her podcast Human Wise.

The HUMAN Framework

Helen has created a framework that encapsulates the essence of human-centred leadership, based upon coaching principles

H: How you show up

U: Understand yourself and others

M: Mindset

A: Act & Adapt

N: Next steps

By working with this framework,  leaders and executives can have a practical way to embrace a way of operating that fosters a human-centric culture with a commercial lens. The best outcomes for you, your team and your business.

The benefits of investing in coaching are both qualitative and quantitative. Qualitatively, individuals understand more about themselves, they gain confidence and develop stronger leadership capabilities.

Stretching these skills into commercial conversations translates into quantitative benefits   where companies can see tangible commercial outcomes resulting from an increased confidence in the market, new relationships, new opportunities, and an uptick in revenue and profitability.  All resulting from deeper, connections and human relationships.

Helen’s approach to coaching emphasises that making the human advantage your commercial advantage is not just beneficial, but essential to business success in today’s human-centric world.

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