This report has been produced in partnership with Sionic

Sionic are a global consulting firm specialising in financial services with a longstanding presence here in Jersey.

If you would like to discuss how Sionic could help meet your requirements or find out more about any aspect of their expertise please visit their website or contact Alain Egre and Robert Roome at

The Future of AI in Financial Services

In our series of special reports on how artificial intelligence (AI) is radically transforming financial services, we’ve already highlighted the widespread debate around the definition of AI.

It’s a conversation that’s set to continue when you consider the breadth of AI technologies and the solutions they bring to our everyday lives – from passport facial recognition systems, apps for finding optimal driving routes and virtual assistants like Siri and Alexa, to chatbots, antivirus software and product recommendations based on past interactions.

This long list of applications, together with the current fashion to relabel much of today’s technology as ‘AI’, is a good reason to focus on what’s relevant to the financial services industry in Jersey.


While the concept of thinking machines and artificial intelligence has been with us since Greek mythology, it was the defeat of Gary Kasparov in 1997 by Deep Blue that marked the moment for many that the potential of AI moved from science fiction to potential reality. Since that moment, AI has gradually become more commonplace within our personal lives, from when we use a digital assistant, shop online, or pick a show to stream online. It is last few years, however, that AI has started to become ubiquitous within our business lives, with overall corporate spending rising from $12.75bn in 2015 to $67.85bn in 2020, and 85% of financial services organisations now adopting AI (Source: World Economic Forum study, 2020). Going forwards, the rapid pace of development of deep learning – the most advanced form of AI – will increasingly expand the usage and benefits from AI, as it’s deployed in everything from self-driving cars, to the fight against COVID-19.

Banking on Cost Savings

Autonomous Next research seen by Business Insider Intelligence has uncovered the potential cost savings for banks from AI applications. While Jersey’s financial services industry is, of course, much wider than banking, it’s useful to consider these figures and make comparisons between front, middle and back offices.

The research found that the aggregate potential cost savings for banks from AI applications is estimated to reach US$447 billion by 2023, with front and middle offices accounting for US$416 billion of this total.

Source: Autonomous Next research

Maturity of artificial intelligence applications across the financial services industry

Source: Adapted from Autonomous The Financial Brand May 2018

How can AI Support Business Activities in Jersey?

To appreciate why the vast array of today’s AI and wider fintech solutions aren’t all relevant to our financial services industry, we need to examine the unique characteristics of business in Jersey.

In contrast to high-volume, low-value global markets, most activities in Jersey don’t enjoy the same economies of scale, but they’re often highly complex and attached to large deals or transactions. Therefore, while the specific situations are different in our market, there are nonetheless significant benefits to be gained from the use of AI.

Broadly speaking, use cases and vendor-driven AI solutions tend to focus on opportunities linked to scale or an abundance of available data. Technologies such as machine learning, automated stock market trading, blockchain, virtual assistants and retail investments/robo-advice are, to varying degrees, not immediately relevant to the typical activities we see at our island’s trust companies, fund administrators, private banks, corporate service providers or legal and accountancy firms.

Fintech Eye

More relevant is the technology used for digital onboarding solutions that perform passport facial recognition as part of KYC (know your customer) checks, which ought to be an important advance for helping to manage the relentless demands of anti-money laundering (AML) or combating the financing of terrorism (CFT) compliance. A key element of identification and verification (ID&V) solutions is their ability to use AI to identify fraudulent documents, and extract and pre-fill information from the passport into a system and remove re-typing. There is also an opportunity with Digital ID&V to enable ‘remote’ on-boarding, allowing individuals to on-board without a face-to-face meeting.

Similar arguments apply to machine learning, which can be a boon if there are sufficient volumes of input data from which the machine can ‘learn’, such as data generated by payments and transactions. In a Jersey context, this learning could, for example, determine whether certain structures or types of information exist in a document. Or it could be used for the huge volumes of data analysis in M&A activity or private equity investment. However, the business case for machine learning solutions where the scales aren’t evident is generally niche.

The challenge for Jersey-based financial services firms is to understand where AI can benefit them and whether scale and data availability can be achieved when operating across multiple jurisdictions.

It may be that different regions, business segments and cultures make this impractical – or they introduce the risk of spurious results. On the other hand, a firm could find that the scaled benefits make AI adoption more practical than its sole use in Jersey.

Another consideration is the fact that many of Jersey’s clients have some form of financial relationship with other jurisdictions – and it may be that the other jurisdictions are better placed to leverage economies of scale in the use of AI. This could create an imbalanced client expectation of turnaround times or fees, making this another vital factor in deciding whether AI solutions should be introduced locally.

Two Driving Forces: Globalisation and Regulation

In our introduction to AI, we discussed why now’s the right time for AI to thrive. Globalisation is a key force driving change in the financial services industry, with several related consequences, each of which can trigger the rise or fall of industry segments as international market forces or regulatory change open up sectors or close them down.

In particular, globalisation is driving the consolidation of players in the industry as backers aim to build bigger, better businesses to take advantage of scaling opportunities.

This consolidation of businesses results in the redesign of operating models, giving rise to a trend towards outsourcing functions to lower-cost jurisdictions or centres of excellence.

Influenced by globalisation and changes in international standards, regulation, especially on the ground in Jersey, also materially impacts what businesses need to do and when they need to do it by. However, regulation has sometimes caused a reversal of what once seemed an inevitable outcome. An example of this is the moral debate around privacy and AI such as facial recognition or using personal data for marketing. While the use of these technologies had long been anticipated, in certain cases it has gone so far as to feel intrusive and uncomfortable, and adverse consumer reactions have limited or even reversed their adoption, driving positive change.

These factors play a significant role in shaping proactive and reactive business strategies and are rightly placed at board level. What’s often underexplored is the role AI can play in helping firms meet their strategic objectives.


  • Organic and business accelerated scaling
  • M&A activity
  • New markets
  • New business lines and asset classes


  • International standards
  • Regional or jurisdictionally specific
  • Increase in regulatory bodies or requirements

Industry Reactions

  • New or consolidated legal entities and structures
  • Increased operational risk
  • Reporting requirements and methodologies
  • Revised operating models and processes
  • Technology platform complexity

AI as an Enabler

  • Risk profiling
  • Deal and transactional due diligence
  • Contract review and analysis
  • Migration and data entry
  • Technology interfaces
  • Consolidated reporting
  • Cybersecurity

Building on Strong Foundations

Let’s turn to the technologies that are impacting the financial services industry in Jersey. Top of the list are the mainstream back-office systems used to manage the administration of processes across each of the main industry segments.

Capgemini estimate that 90% of European and North American technology budgets are spent on managing and maintaining legacy systems. Implementing or upgrading to later/better versions, or even consolidating costly technologies, has the potential to improve the efficiency or effectiveness of managing an organisation.

Also significant are the infrastructural technologies that underpin or complement  back-office systems. Among these are the so-called ‘cloud’ providers of outsourced computer servers, including all of the related productivity tools such as Microsoft Office 365, which means that no business needs to have its own internal centralised servers.

When you consider the sophistication of the internet, including interfacing technologies such as web services standards and the emerging internet-of-things, along with global low-cost technology suppliers, the direction of travel is very clear.

With a combination of consolidated systems, upgraded technology, cloud-based solutions and better interfaces between systems, you have strong foundations for the implementation of AI solutions.

However, VentureBeat AI predicted that just 13% of AI projects will make it into production, citing a lack of data strategy, overly complex projects, the inability to deploy models due to resources, and a hesitancy to invest from senior management.

Maturity of artificial intelligence applications across the financial services industry

In our research into technology adoption in Jersey’s financial services industry, we found that the path to achieving greater digital adoption is incremental. When it comes to the deployment of AI technology in finance, selecting the right project and ensuring the wider business strategy is in place first will be key. The measured approach by Jersey’s financial services sector is a familiar change management path that can be followed to deliver business gains.

Core Fulfilment

  • Firms have a high standard of legal professional practice and relatively bespoke client services, with limited standardisation between services, processes and documents.
  • The business strategy is to hire for legal expertise and experience, as well as client facing abilities, whereas technology know-how is limited to IT teams or knowledge managers.
  • The technology itself tends to be industry standard office systems with upgrades to software/hardware. There is relatively static on-premise infrastructure consisting of a mixture of physical and electronic documents and storage, with PCs and files typically only accessible in the office.

Systems Integration

  • Firms have a high standard of legal professional practice and relatively bespoke services, with some professional support from administrative colleagues aided by back office systems.
  • The business strategy is to hire for legal expertise with understanding of the legal context and proficiency in standard law firm tools. There tends to be at least one member of staff beyond an IT manager who has good IT knowledge to support efficient technology use.
  • In terms of technology strategy, firms have a desire to transform and integrate back office systems to make efficiency gains, but they are often hindered by the challenges of replacing legacy systems.
  • There is the use of a range of devices and software packages, with some amalgamation of systems and databases, but often with workarounds in place to address any integration difficulties.

Undergoing Change

  • Firms have a high standard of legal professional practice. The service is beginning to be delivered in a way that maintains personal elements but is more standardised operationally so as to drive up quality, mitigate risk, develop resilience and drive down costs and/or maximise profits.
  • The business strategy is to hire beyond legal expertise, with a focus on entrepreneurial insight. There tends to be broader knowledge of IT and business strategy, which can help shape the direction of the firm.
  • The technology strategy is integral to the broader business plan, which considers how the firm will develop over the next 3-5 years and includes staff development and training programmes.
  • A range of devices allow for more flexible and mobile working, often supported by integrated back-office systems and developed case management systems for electronic storage.

Continuous Improvement

  • Firms have a high standard of legal professional practice that is being developed within and across departments to maximise quality and minimise risk; this is applied from inception at onboarding, right through to completion. The practice is seen as a partnership between people (lawyers and professional services) and technology systems.
  • In terms of strategy, the business strategy comes first, and the people and technology strategies are then tailored to meet the needs of the firm.
  • When hiring, the practice will look for competencies across a range of domains, as well as behaviours and attributes. More focus is placed on the mindset of those being hired and their ability to work well within a team in a changing environment. There is also a developed education and training plan for all members of staff.
  • In terms of technology, the standard mode of work is entirely electronic. Mobile devices with secure access is the norm, enabling staff to access most work functions from all locations.

Leading with Technology

  • Research participants that were at the forefront of technology adoption stressed the importance of focusing on the cultural change work as the key to innovation, with technology being the enabler rather than the driver, and that technology alone is unlikely to yield real benefits for the firm or its clients.
  • With all the appropriate processes and support in place, the opportunities for those Jersey law firms that adopt lawtech are considerable.

Clear Opportunities to Create Better Systems

Back and middle office environments are the areas where the use of AI is most prevalent in financial services today. Processes and systems can be readily seen and therefore measured:

1) Back-office systems typically undertake the majority of a firm’s most repetitive tasks

2) Back and middle-office systems can often generate the most measurable amounts of data.

3) Middle-office functions tend to interface with many, if not all, parts of a business.

In middle-office environments especially, data is usually obtained from numerous sources and compiled for reporting and control purposes, often using manual spreadsheets or workarounds, making them prime for digitalization through the use of AI.

Source: Banking Hub

If you can't measure it, you can't improve it

Peter Drucker

AI or Not AI?

How Intelligent Automation is streamlining financial services

As we covered in our first report, Robotic Process Automation (RPA) along with machine learning are the most discussed technologies within financial services when it comes to automating tasks and increasing productivity.

Many wealth management industry players are positioning RPA solutions alongside AI within the badge of ‘Intelligent Automation’ and this can sometimes lead to situation where the market-place can confuse the two.

RPA gets a lot of positive press in mainstream global markets because it allows manual processes to be automated by copying a set of pre-existing steps using robots or bots to act as digital workers and replicate user activities. Critics of RPA consider the progress made so far to be disappointing to some extent, in that the technology is still based on pre-programmed steps and has yet to deliver all that it seemed to promise.

RPA can increase staff time for higher-value activities and reduce human error – when applied correctly, RPA tools can achieve a nearly 0% error rate (Source: Cloudstorm RPA).

By examining some existing examples of the effective use of Intelligent Automation in back and middle offices, we can start to identify some of the ideal processes to automate:

Compliance and fraud detection

Solutions can offer incredibly high accuracy and ‘always-on’ availability, which is useful given the 24/7 nature of global financial transactions. Tools can be used to collect and analyse thousands of transactions in mere seconds against a specific set of pre-defined rules. Where a transaction fails to meet the rules, and therefore may be deemed ‘suspicious’, a report to the compliance team can be generated instantly, at which point human intervention can handle the issue. AI can supplement identifying patterns in vast amounts of data and recognising potentially fraudulent transactions more quickly and accurately than human staff.

Data entry, capture across multiple systems

During the client onboarding process, many firms require a new client’s data to be captured and entered into multiple systems. A robot can automatically transfer information from one system to another – for example, from a back-office onboarding platform into a customer portal platform. AI can then enable the decision making process inline with business risk appetite and initiate the downstream data workflows and processes.


Reconciliations and validations

Most organisations will spend significant amounts of time manually checking and matching transactions and preparing and posting journals, making this an ideal area for Intelligent Automation.

Firms such as Duco and Xceptor provide Machine Learning powered reconciliation solutions, allowing data from disparate sources to be automatically ingested and compared, with installations providing an ROI within weeks.

Intelligent Automation can be used to complete tasks such as clearing trades, regulatory reporting, carrying out order research and resolving discrepancies. In some cases, functionality should be able to automate entire end-to-end reconciliation processes without the need for any manual intervention, but significant efficiencies can still be made where automation is around 90%, enabling staff to dedicate more time to complex cases and investigations.

A Spotlight on Forward-Thinking Regtech Solutions

Intelligent Automation technology is already evolving through the development of solutions that use machine learning to build on reactive rules-based technologies and recognise patterns and deviations from past normalities.

Extending our examples of how AI can be used in back and middle offices, fraud detection AI combined with natural language processing could automate report generation, with the AI spotting a suspicious transaction and the language tools being automatically engaged to ‘write’ a report. Alternatively, machine reading comprehension tools could be used to scan documents such as passports, deeds or company artifacts and input their data into a client onboarding platform.

In the regtech sector, emerging technology is being used to automate demands arising from global, regional or local regulators. It’s an important area where systems can help improve efficiency as well as the effectiveness of a firm’s compliance with mandatory regulatory requirements.

There are many players battling to win market share in this area. Some come from traditional international banking backgrounds and where high levels of AML/CFT compliance in client onboarding have been commonplace for many years. Others are emerging as grassroots start-ups, using the latest software solutions that incorporate some of the more AI-centric features mentioned in this report. It will be interesting to see how the competition between these contenders plays out.

In some of the use cases we’ve already looked at, size and scale have been the main drivers of a move to AI, and cost or resource efficiencies are the primary outcome. In the case of regtech, AI can greatly improve regulatory compliance. As organised crime becomes increasingly sophisticated, so too must the tools needed to fight it. The ability to undertake more in-depth and greater volumes of screening as part of monitoring suspicious activity is a potential benefit of applying AI to regtech solutions – so if volume and scale aren’t the drivers for AI, protecting your business might be.

Vital Protection Against Cyber Crime

Cybercriminals and organised crime groups are becoming more sophisticated all the time and, while it’s the responsibility of all staff to be aware of the threats, the technology responsibility typically sits within back-office teams. This threat is particularly pressing in Financial Services, with BCG identifying that banking and FS firms are up to 300 times more likely to be targeted than other firms.

Cyberattacks may very well be the biggest threat to the U.S. financial system.

Jamie Dimon
CEO of J.P. Morgan Chase & Co

AI solutions can help strengthen a firm’s cyber security by more quickly and dynamically responding to these evolving threats:

AI in cybersecurity strategies

Phishing and social engineering

Software that can understand language, tone and user behaviour to prompt when an activity or recipient is outside the norm and the user may be about to send information to the wrong person.

Data Loss and GDPR

With data the lifeblood of AI, firms increasingly hold larger amounts of personally identifying and sensitive information. This leads to an increased risk, and often this can come in the form of poor data protection. AI can be used to dynamically classify client identifying data, and prevent it from being sent or transmitted in an insecure fashion.

Network analytics and threat detection

Technology implemented that uses machine learning to understand what normal usage looks like across your technology stack and from this identifies suspicious changes in behaviour and activity, identifying and locking potentially compromised staff accounts and driving proactive alerts for investigation. Many vendors and service providers, especially those with cloud-based technologies, pool threats seen across several of their clients to identify patterns and act accordingly with updates and fixes

Many more examples exist; as with any cybersecurity strategy, the priority for firms is to understand what needs protecting and find the best technologies to provide this protection.


Getting the Back Office Ready for Insight and Automated Learning

Our research partner Sionic has looked at a number of cases where learning technology can be used to extend the range of automated processes adopted by the financial services industry:

At a basic level, Intelligent Automation is a capacity creator that saves time and removes the risk of human error. In each of these cases, deep learning can be taught to reduce or remove manual intervention:

Machine readable AML handbooks

Virtually all regulatory jurisdictions make their regulations available online, although formats can vary from PDFs to HTML pages and text feeds. Rulebook ingestion can help financial services firms keep up with changes in regulations and refresh their processes based on these changes.  Learning technology can be taught to understand the meaning behind the regulations and categorise them for ingestion by the governance, risk and compliance platforms that allow firms to evidence compliance with these regulations.

For firms that operate across multiple jurisdictions, there’s an additional benefit with technologies able to identify scenarios and the regulatory stances of different operating locations as an enhanced way of ensuring governance, risk and compliance procedures are acceptable across the entire business.

Credit and decisions

Credit departments review thousands, even hundreds of thousands, of annual reports every year to assess the credit quality of clients and potential clients. The process by which a bank transfers information from a borrower’s financial statements into the bank’s financial analysis program is a mainly manual process, employing armies of staff with the knowledge required to read and extract key data from the annual reports.


Onboarding Example

Onboarding is an important challenge across the financial services industry, with speed of execution and user experience being key success factors. The inclusion of AI to undertake liveliness checks (which can be completed remotely) can help ensure the entity being onboarded is who they say they are and confirms their link to complex structures, which is currently a critical bottleneck.

Reconciliation Example

Reconciliation within the back office is not restricted to books and records platforms but increasingly validating multiple sources, inputs and outputs from processes across the client lifecycle. The volume of data both manually checked and subsequently exception managed is material and growing across middle and back office controls.

Source: Goodfellow, Ian, Bengio, Yoshua, and Courville, Aaron. Deep Learning. MIT Press, 2016.

Artificial Intelligence

Any technique that enables computers to mimic human behaviour

Machine Learning

Subset of AI. Uses statistical methods to enable machines to learn and improve with experience

Deep Learning

Subset of ML. Makes the computation of multi-layer neural networks feasible

Innovative Ways of Working with AI in Jersey

Not all use cases are obvious. Examples of AI used by financial services firms in Jersey to meet specific needs:

Contract analysis – Natural language processing technologies can read and understand contracts to extract key information, compare versions and flag inconsistencies

Investment opportunities – Enhanced research and analysis tools compute data and risk to identify better investment opportunities for private equity funds

Investor relations – AI-driven software automates the gathering of investor relations data and automatically builds this into reports

M&A – RPA is used to automate the transfer of sensitive documents, removing the need for human access to the data, thereby reducing risk and increasing confidentiality

Pre-Post Trade Data –  Gathering regulatory documents and internal policy and “evidencing” actions and decisions to trading actions taken within core systems

Paper to data – Using natural language processing, image recognition solutions allow users to ask questions such as ‘Who’s the beneficiary?’, ‘What’s the interest rate?’ and ‘When does this passport expire?’.

Sentiment analysis – Typically used by communications and marketing teams to scan an abundance of web and social media data to identify good and bad sentiments towards firms and their competitors.

Where AI Meets Positive Friction

In financial services, friction often has a negative connotation, but in the context of AI technologies, especially Intelligent Automation, the end-to-end process needs to be considered.

In Jersey’s high-value, highly complex environment, many processes in back and middle offices are subject to manual interventions such as four-eyes checking, approval limits or AML checks. This concept is known as ‘positive friction’.

While end-to-end automated solutions remain the holy grail, presenting a great opportunity for process improvement, the reality is that there will still be friction as a result of the vital steps required to protect both a firm and its clients. In an approval or decision-making process, the ability to capture and analyse data using AI allows for better decision-making but the control requirements will remain.

Measuring Success: Understanding the Outcomes

Process improvement tools and technologies are well-established within financial services firms. When it comes to adopting AI technologies, the Plan, Do, Check, Act cycle applies to all implementations regardless of whether the driver is risk mitigation, greater efficiencies or cost reduction.

In our paper Jersey Means Business: A forward-thinking approach to measuring productivity, we explore some considerations for measuring meaningful changes, along with pre and post-change questions such as:

  • Have you considered the unit cost per process to allow you to act on any abnormal findings?
  • Has your business mapped out key processes or administrative tasks to identify duplication and opportunities to simplify them?

The outcomes of implementing AI technology in a back or middle-office environment will vary based on the nature of the outcome a firm is looking to achieve.

However, in the majority of cases, measuring success will typically be achieved by measuring one or more of the following:

Revenue or cost – increased profitability or cost savings / number of full-time equivalent (FTE) employees to give the figure per FTE

Payback period – typically linked to cost per FTE figures, the period of time by which the total investment has been repaid based on the realised benefits

Return on investment – realised benefits / total investment expressed in percentage terms

A back office example here would be addressing Error/Right First Time Rates’ – The average number of mistake or issues made during a manual process, for example re-typing mistakes during onboarding.

So, whether the AI technology used in financial services is innovative or not, it can be planned and measured using known methodologies, and quantified alongside other strategic priorities.

Is this Where the Story Ends?

It’s really just the beginning. AI is a big topic, and a popular buzzword in financial services, much like everywhere else. But while the hype is real, it makes sense for firms to think about specific opportunities and technologies that lend themselves to early and uncomplicated success.

While this report has focussed on back office efficiencies, in our next report, we’ll turn to look at the front office, examining how client experiences can be enhanced and competitive advantage gained through the use of AI technologies.


Find out more about Jersey as a centre of excellence for Fintech ›

Jersey is a world-class centre for fintech. We strive to be the easiest international finance centre to do business with remotely, in a digital world. We have a forward-thinking regulatory approach, which sets us apart, and this has been vital in cementing our standing as a highly-successful digital jurisdiction.

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