Technology

REVOLUTIONISING GLOBAL BANKING THROUGH THE POWER OF AI

Published

2 years ago

December 17, 2021

admin

Richard Shearer, CEO of Tintra PLC

Let’s imagine a scenario in which an individual living in Kenya wants to send money to London. If the person were living in the UK, this would be a very simple matter: two taps on their smartphone phone would result in a near-instantaneous transaction with no questions asked.

As a cross-border payment, however, this transaction looks very different: The individual’s money will have multiple arduous hurdles to clear as it’s passed from a local bank to a local electronic money institution [EMI] to a UK EMI, then on to a UK bank, before – if they are lucky – arriving in the hands of the beneficiary. This is a long-winded process riddled with red tape that could last as long as three months.

In addition, this already lengthy process is compounded by a further impediment: because the person is from an emerging country, they will – in the eyes of KYC/AML compliance teams – be considered high risk, despite the fact their earnings and transactions are entirely above board.

In essence, these challenges boil down to two key and interrelated issues: compliance processes are full of friction [often in the form of sluggish, manual, complex, and time-consuming KYC checks from multiple banks and EMIs], and Western KYC/AML teams are subject to bias, meaning they can’t [or possibly won’t] discern ‘good’ clients from ‘bad.’

Therefore, in order to truly achieve global banking, we need to develop a solution that will allow financial institutions to deal with both challenges quickly, efficiently and automatically – which is where technology, and more specifically, artificial intelligence, has the answer.

Leveraging technology to eliminate barriers and address bias

The banking industry hardly needs persuading of the benefits of AI in broad terms, as demonstrated by a recent report from McKinsey,which signposted several advantages of its integration, including boosted revenues, lower costs, and the discovery of unrealised opportunities through insights generated by powerful, data-hungry technology.

Perhaps more importantly, however, AI has the potential to significantly improve AML processes. For example, in predictive analytics, machine learning methods can be used alongside customer data to predict possible criminal behaviour – at lightning speeds and at an unprecedented scale – which simply cannot be matched by the people who remain at the heart of legacy banks’ compliance teams.

Not only can AI speed up the cumbersome processes that create the kinds of barriers faced by the likes of the individual from Kenya, but – crucially – the adoption of AI can also help to overcome the significant barriers represented by KYC/AML bias.

For example, using cutting edge AI tools to streamline onboarding and compliance procedures and automate all processes that currently involve manual invention, will effectively replace subjective human decision making with intelligent machines that have learned from years of data and experience. As a result, by reducing human involvement to a minimum, these tasks become fast, fair, transparent, scalable, and flexible enough to be applicable to customers and transactions across the globe.

Of course, AI isn’t always entirely free from bias – it’s made by people, and its insights are interpreted by people too. This reinforced by the last Nordics Anti-Financial Crime Symposium, which highlighted the need to watch out for bias at the programming stage.

In the context of KYC/AML classifiers, an unfair bias could occur if the machine is trained to mimic the human decision-making process, where the ‘right decision’ is fed into the AI solution. This can be overcome by providing evidentiary data instead, where the machine can learn from examples of transactions that resulted in complications as opposed to modelling outcomes on potential human prejudice.

Another key challenge for AI is generalisation caused by ‘narrow’ training data, such as when certain demographics and/or ethnic groups aren’t represented sufficiently in the training set. A similar phenomenon can occur in the context of KYC / AML where criteria for accepting a customer or transaction can vary across geographic area, meaning those in emerging markets may suffer as a result.

That said, it doesn’t mean AI can’t help in eliminating prejudice in AML procedures – far from it – it simply means we need to ensure the next generation of fintechs and challenger banks utilising this technology are feeding their AI models good data that provide explainable results – and that these entities are sincere in their desires to level the global banking playing field.

Revolutionising the global finance industry

Taking this kind of technology seriously would be nothing short of revolutionary for the global finance industry.

After all, as the Centre for Global Development has recently noted, KYC/AML discrimination can have serious ramifications in emerging markets, with those most likely to be impacted including “the families of migrant workers, small businesses that need to access working capital or trade finance, and recipients of life-saving aid in active-conflict, post-conflict, or post-disaster situations.”

In looking beyond the benefits that this new breed of global banking will have on individuals, there are also huge implications for the global economy.

McKinsey’s report on the future of cross-border payments points out that international payments revenues already amount to around $200bn globally – but a closer look at the figures reveals that while Western Europe sees 5.5 annual cross-border transactions per capita, Latin America only sees 0.7.

If compliance barriers were lowered through the leveraging of new technology, it seems perfectly plausible to suggest that places like Latin America would see cross-border transactions increase, with all the economic benefits associated with this increased flow of money on an international scale.

And, with AI and machine learning leading the charge towards revolutionised banking, it’s worth remembering that decreased prejudice needn’t come at the cost of increased risk: in fact, a recent Deloitte survey found that 41 per cent of respondents believed too many false positive AML alerts were the biggest AML compliance challenge faced by banks today.

Therefore, the right technology operated by new, forward-thinking financial entities has the real potential to simultaneously address the prejudices that underpin AML compliance processes, eliminate the sluggishness that those processes entail, unlock new streams of money to circulate in the global economy, and address the current lacklustre state of addressing financial crime.

When one really allows oneself to really absorb this new paradigm, the potential is there for AI to completely repackage the way in which the global banking industry operates. The question is who will be first to the party!

Related Topics:CEO of Tintra PLC criminal finance derivative KYC/AML compliance Leveraging technology Richard Shearer transactions

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In-platform solutions are only a short-term enhancement, but bespoke AI is the future

Published

13 hours ago

September 27, 2023

editorial

By Damien Bennett, Global Director, Principal Consultant, Incubeta

If you haven’t heard anyone talking about artificial intelligence (AI) yet, then where have you been? Conversations about AI and its advantages to society have been a key talking point over recent months, with advances being made in the generative AI race and ChatGPT opening a whole plethora of possibilities. Many have highlighted the advantages of AI, but notably it’s ability to create human-like content.

But these discussions have only scratched the surface of what AI is capable of doing. It is for far more than just essay writing, adding Eminem to your rave and photoshopping dogs into pictures.

In marketing, we have been using AI for years, for everything from analyzing customer behaviors to predicting market changes. It’s enabled us to segment customers, forecast sales and provide personalized recommendations, having a huge impact on how our industry works.

It is even, for the more savvy marketers of the world, becoming a key tool in maximizing budget efficiency – which is apt, considering over 70% of CMOs believe they lack sufficient budget to fully execute their 2023 strategy.

Now, as AI becomes more intelligent, the number of efficiencies it can unlock continues to rise. Not only can it help brands get the most out of their available resources and identify any areas of waste, but it can also help highlight new opportunities for growth and maximize the impact of your budget allocation.

The trick, however, is to veer away from the norm of using in-platform solutions with a one-size-fits-all approach and create your own, bespoke solutions that are tailored to your business needs.

Pitfalls of in-platform solutions

In-platform solutions aren’t by any means a bad thing. In fact, built-in AI tools have become increasingly popular, owing to their ease of integration, user-friendly interfaces and minimal set up requirements. They come pre-packaged with the platform, offering the user the ability to leverage AI technologies without the need for in-depth technical expertise or the upfront cost of building a solution from scratch.

However, the streamlined and accessible nature of in-platform AI solutions comes at the expense of complexity and customization. They are designed to serve a broad user base, but for the most part are built using narrow AI solutions with predefined features and workflows.

This makes them great for assisting with common AI tasks, but they lack the flexibility to tailor functionality towards unique business requirements or innovative use cases, limiting the potential efficiencies and cost savings that can be unlocked. Additionally, if a business’ competitors are using the same platform, they are probably using the same AI solution, meaning any strategic advantage gained from these will be reduced.

Bespoke AI solutions, on the other hand, may carry a higher initial investment – but can offer a significantly more attractive ROI over a short amount of time.

Why customized and adapted AI is the key

The difference between bespoke AI and in-platform solutions is similar to that between home cooked food and a microwave meal. Yes, it is more time consuming to prepare, and yes it likely carries more of an upfront cost, but the end result is going to be far more appealing and will carry more long-term value (financially… not nutritionally).

That’s because bespoke solutions, by nature, will have been tailored to address your brands specific needs and challenges. These custom-built tools allow for much greater efficiencies by streamlining workflows across different channels, automating more complex tasks, and providing deeper, more relevant insights.

The increased level of optimization can significantly improve productivity and reduce operational costs over time, offering a higher ROI. The increased flexibility of bespoke AI also allows brands to implement innovative use cases that can significantly differentiate them from their competitors.

The data analyzed can be specifically chosen to match business requirements, as can the outputs of the AI tool, providing a significant advantage when understanding and acting on the insights provided.

Additionally, these tools are, by nature, more scalable. They can be updated, upgraded and expanded as needs change, ensuring they continue delivering value as the business grows. They can also be designed to integrate with any existing IT infrastructure, from CRM systems and databases to marketing platforms and sales tools – leading to more efficient and effective decision-making.

Managing finances with AI

It’s no secret that AI in marketing automation has, and will continue to, revolutionize the way marketing is done. It has a bright, if slightly terrifying, future and can help CMOs to unlock new efficiencies, maximize the impact of their budgets and increase their ROI. And as this technology becomes more advanced, its impact will only increase.

But we already know that…and so does everyone else.

So, in order for businesses to make themselves stand out from the crowd , they must look to fully adopt the power of AI. Creating a customized and unique AI solution could be the way to set yourself apart from your competitors. A bespoke AI tool can provide brands and businesses with features unique to them and their business needs. As a result, companies will benefit from more useful data and better results to make more data-driven decisions for their business. Ultimately, this will help brands to maintain a competitive edge over their competitors, deliver ROI and most importantly optimize their budgets.

Business

Exploring the Transformative Potential and Ethical Challenges of AI in Wealth Management

Published

1 day ago

September 26, 2023

admin

Nuno Godinho, Group CEO of Industrial Thought Group

In recent years, the advent of AI has sparked both excitement and scrutiny within the Wealth Management industry. The technology’s capabilities, including but certainly not limited to generative AI algorithms like ChatGPT, offer a new dimension to data analysis, market prediction, and portfolio management. However, while it presents a promising avenue for enhancing decision-making and elevating client interaction, AI also carries inherent challenges that demand careful consideration.

Benefits of AI in Wealth Management:

In a world where CX is key, AI enables wealth managers to provide personalised advice, improved portfolio performance, real-time insights, and convenient access to information and support. Previously it has been impossible for advisors to deliver hyper-personalisation at scale; now, AI-driven customisation lets them tailor investment strategies and recommendations to their clients’ unique financial goals, risk tolerance, and investment horizon.

AI algorithms can also analyse vast amounts of data to identify trends and opportunities, resulting in potentially higher returns on investments. And, more widespread use of automation will gradually reduce the cost of wealth management services, meaning higher-quality investment advice at a lower price. This is critical as firms fight to stay relevant for modern investors disillusioned by traditional advisory firms and private banks.

Relationship-wise, there are many other advantages. AI-driven data analytics make it easier to gain a deeper understanding of an investor’s needs, preferences, and behaviours, all of which help to build long-term relationships. Through predictive analytics, firms can differentiate their service and proactively identify new investment opportunities, such as emerging market trends or underperforming assets. At the same time, chatbots and virtual assistants facilitate constant communication to answer queries and increase engagement. By strategically integrating AI technology into their operations, firms have the power to optimise top and bottom lines, strengthen client connections and position themselves for long-term growth.

Navigating the Ethical and Practical Challenges:

While AI holds remarkable potential, major obstacles must be overcome. With AI’s reliance on large amounts of data, ensuring client data confidentiality, managing consent, and complying with global data protection regulations like GDPR are significant challenges. Another issue is algorithmic bias – as AI learns from data, it may inadvertently perpetuate inequalities or biases present in the training datasets used. Vigilance is necessary to ensure that AI systems don’t amplify these issues. A key concern is the absence of standard governance, leading to a lack of accountability and transparency. Black-box algorithms can make decisions without providing clear explanations for their reasoning, making it difficult for clients and regulators to understand and trust AI-driven outcomes. Overall, the responsibility for AI-generated recommendations remains complex, requiring collaborative efforts to establish robust regulatory frameworks.

Striving for Data Integrity and Reliability:

The efficacy of AI-driven solutions hinges on the quality of training dataset they are supplied with and rely upon. Therefore, ensuring accurate, unbiased, and comprehensive datasets is paramount to generating trustworthy insights. The absence of standardised data sharing can lead to skewed results, ultimately impacting the quality of AI-generated advice. Transparency in data usage, validation, and generation reasoning will be pivotal to cultivating client trust and minimising systemic risks, which ties back to the absence of standard governance, as the output from AI-generated advice will only be as good as the data sets provided. We need to understand the “lineage” of all data used and generated by the algorithms. Until the industry can come to some accord on how we plan to use all of our respective data, it will be prone to various biases and fragmented advice, which will lead to liability and reliability issues down the line. It’s worthwhile wondering whether we can see the industry opening up in an age of data equals value.

The Role of Collaborative Partnerships:

Amidst these challenges, collaborative partnerships emerge as a potent avenue. Established wealth management firms can harness the expertise of FinTech AI companies to augment their capabilities while mitigating the risks associated with AI adoption. A symbiotic relationship, where innovative AI solutions are developed by trusted partners, helps safeguard against potential pitfalls and aligns with the pursuit of ethical, data-driven decision-making.

Looking Ahead: Striking a Balance for Sustainable Progress:

As we journey into the AI-powered future of wealth management, it’s evident that a balanced approach is essential. The integration of AI has the potential to expedite the transition to wealth management 4.0, revolutionising personalised client experiences and advisory services. However, this progress must be underpinned by clear ethical guidelines, data integrity, and collaborative partnerships. Striking this equilibrium promises not only a more informed, efficient, and personalised industry but also one that upholds the principles of transparency, accountability, and client trust.

In conclusion, AI’s impact on the wealth and asset management landscape is profound, offering unparalleled insights and opportunities. While navigating challenges will be crucial, a collective effort to harness AI’s power while ensuring its responsible application will pave the way for a resilient, future-forward industry.