Used by Delta Capita to publish ‘Points of View’ posts to the Publications section / the homepage of the main website.

By Sarah Carver, Global Head of Digital

90% of the worlds data has been created in the last ten years, with that number increasing exponentially with Raconteur suggesting that by 2025, 463 exabytes of data will be created every single day. That’s both a huge opportunity but also a challenge for organisations to harness it, gain insights from it and ultimately use it to better serve customers and impact the bottom line.

How do organisations know where to start? And how do you prevent data becoming ‘dark data’ which you just store (at a significant cost) and derive no insights or meaningful decisions from? Furthermore, once you have identified your data sets, it’s not like drilling for oil where just finding it creates value, you must dive into your data, analyse it and make sense of it. You are also attempting to get these insightful analytics all whilst running a business area, developing new product offerings, servicing customers, assessing cost save opportunities and pivoting revenue models in light of the pandemic. Who are the superheroes who can help you do all of this? Enter data scientists.

Data science encompasses everything from data sourcing, cleaning and analysis, feature engineering, modelling, and prototyping to full blown engineering. The right data scientist can help you understand big data, machine learning and artificial intelligence. You may ask yourself why are many of these terms and their real-life use cases for financial services still mysterious to many?

Firstly, because there is a real art in explaining the ‘so what’ of data science and making it real and relevant to specific situations. This is something we are passionate about at Delta Capita. The goal of our data science team is NOT to confound our clients with terms and lecture them on ‘art of the possible’ use cases, but instead to continually demystify the different terms and to make them real and relevant to actual client problems and opportunities.

And secondly because it can require a significant amount of time and effort to learn about an entire new technique in enough depth to be able to think around it and understand its viable applications. Couple this with phenomenon of some individuals feeling ‘too senior to learn’ and its unsurprising that some of the more advanced tech is underutilized. In my opinion, this mindset is a flawed one anyway, because in any space linked to digital such as data, cyber, technology if you’re not learning everyday then you are already out of date.

Even if your day to day doesn’t fundamentally require in depth knowledge AI, you may just find knowing a bit more about it is enriching to unexpected conversations. We also know at a societal level it is being implemented in more and more of our everyday lives. When LinkedIn offers smart replies to a message, when Amazon makes a product recommendation to you, when Alexa remembers the specific version of a song you want to play and when uber matches you with your driver. The ability to question AI systems and fully understand why certain predictions are being made will become increasingly important to diminish theconcerns around AI ethics. Given that AI is becoming progressively and silently embedded into many of our interactions, why not learn a little more about it?

If you want to chat more about Big Data, AI & Machine Learning and real-life applicability in Financial Services, then click on the banner below to contact us. Our data science team would love to have a conversation about how it is used in financial functions such as KYC, cybersecurity, fraud detection, risk, regulatory compliance, and customer experience. We are also passionate about model explainability and inherent bias and trust challenges so we can help you navigate your data science journey.

Digitisation has had a strong pull for the financial sector – particularly investment/commercial and retail banks – with leading players adopting cutting-edge technology frameworks to stimulate revenue growth and expansion of their client base through competitive differentiation.

Now, banking’s nearest cousin – the insurance sector – is looking to follow suit.

The banking example: procurement

For banks, the growth in technology ‘real estate’ entailed a complete re-engineering of processes and platforms managing all functions, from the front-office to the back-office.

This led to many projects creating client portals, trading platforms, cloud services and more. More recently, investments have focused on robotics, machine learning, artificial intelligence (AI) etc.

And initially it paid off via advances in areas including straight through-processing, automation, cost reduction and enhanced client journeys. However, over the last two decades, these benefits have been in decline, and we can learn a great deal from it.

Firstly, cost varies greatly between sourcing models. Two main models exist:  IT departments led by strong in-house technologists tend to develop (or in-source development of) their own solution software from scratch, while others favour off-the-shelf vendor applications.

The first invariably led to massive increases in underlying costs, far outweighing the original investment. These involved not only skills such as design, coding, implementation and testing, but also support and maintenance throughout the shelf life of the solution.  

By comparison, vendor solutions frequently cost only a fraction of in-house solutions and require far less lead time to business value. Clients are also in a much better position to tighten purse strings.

The lessons for insurers: democratic procurement

With the wealth of vendor solutions on offer, the importance of the IT organisation as technology provider has inevitably eroded.

Marketers in the insurance sector are within their rights to question in-house tech builds that don’t consider alternatives, and secondly, they should negotiate equal decision-making rights when adopting new technology. In the past, voting has been skewed in favour of the IT organisation just because they are ‘in technology”. 

The banking example: Complexity and cost-consciousness

The digital evolution of banking has further created product processing silos that duplicated functions through a lack of standardisation. Adding costs, it has left the sector with legacy platforms and rapid obsolescence, requiring frequent replacements to remain competitive. By contrast, standard technologies integrate better with newer ones and enable smoother migration.

With escalating costs and a general decline in growth in the financial services sector, there is less and less vindication for spending on new technologies. Being held ransom to costs, organisations have had to forego procurement targeting growth through new products and clients.

The lessons for insurers: simplicity, strategy & sourcing

As the insurance sector expands its product base to enable growth by offering its clients more options, players should have a long-term strategic plan, focusing on a few key areas. The impact of plans on processing (front to back) should be thoroughly analysed, rather than shoe horning in ad-hoc solutions to point problems, which ultimately create technological chaos.

The case for system and process consolidation to streamline complex architectures is further mounting. There is even a good case to be made for wiping the slate clean by offloading processes to managed service providers (MSPs).

The role of FinTech collaboration

Even within back-to-front architecture considerations, gaps remain. Insurance firms should consider the benefits of smaller collaborative partnerships with FinTech companies, whose solutions can offer better client journeys and nimbler workflows and processes, allowing them to accelerate business value while reducing cost.

That being said, insurers should  carry out the necessary risk profiling before taking the plunge with smaller vendors, as they could be exposing themselves to unnecessary credit risk.   

Stay ahead

Digital transformation is vital to stay ahead of the game, but the way banks have approached this has resulted in a huge lack of agility, resulting in an inability to implement strategically important technologies, such as predictive elements to take advantage of unexpected events.

To ensure their choices help to increase growth, reduce cost and create a sticky customer experience, insurers cannot allow themselves to fall into the same trap.

Avoid unnecessary costs of in-house developments and architectural complication, and keep your eyes on the three-fold prize – cost cutting, growth and new product enablement.

For more information you can email us at or watch our webinar “Business and Technology Transformation: The role of a CIO and COO within Insurance“.

On the 14th of October, Steve Vinnicombe, our Head of Consulting and Solutions had the opportunity to steer a panel of experts on insurance technology and business transformation during Delta Capita’s latest Webinar: Business & Technology Transformation: The Role of a CIO and COO Within Insurance.

Joined by Bimal Umeria, Head of Wealth Management and Insurance at Delta Capita, Vince DiFiglia, from QBE, Stefan Verhoeven from AutoFill Technologies, and Shaymus Kennedy from Athora Holding Ltd.

The discussion was focused on critical issues, opportunities and threats facing insurance through the lens of senior operations and technology roles. In an era of unprecedented change, key points examined were specific issues facing technology and operations senior executives.

These issues included the approach to transformation and how that is being informed by the border market at Lloyd’s, how COVID-19 has changed decision making in short and medium terms, the role of insurtech for big insurance brands, and whether cost mutualisation offers value not yet realised for insurance forms.

Some of the key takeaways:

1. Understand your Business Model

Communicate your priorities to your potential insurance tech (insurtech) partner so that you can determine which processes should be kept internally and which ones should be outsourced. For example, some insurtech offerings might be more focused on automobile or housing markets but be less applicable to life insurance services.

2. Learn from Banks’ Mistakes

Insurance companies can learn from banking and other industries to find ways of more frequent interactions. The key here is to reduce total cost of ownership (TCO).

Insurtech is a few years behind banking. However, rather than investing resources in-house, CIOs and COOs can leverage proven SaaS or cloud-based models to save money on salaries and costly legacy systems that are no longer technologically viable. Developing tech inhouse is costly and time consuming. You can bring products to market factor with external providers and get everything you need and require.

3. Automate your Processes

Manual processes will slow down onboarding of your clients. The younger generation demands faster onboarding and response time or will favour the competition. Forrester reported that digital transformation efforts in more than 75% of enterprises will focus on automation in 2021.

Leverage the technology that is already available for other sectors. Clients are looking for straight forward answers and service. As well as cost reduction on the client side, make data more accessible and reduce cumbersome processes.

4. Define Boundaries

While digital transformation enables the workforce to operate seamlessly from home, be productive and access network and applications. However, IT can cooperate with other company stakeholders to encourage employees and contractors to work ergonomically and take breaks, without compromising on productivity.

Mental welfare is as important as technological training. Stepping up on technical training alongside life-family balance and self-development. For example, insurtech companies such as Lemonade, initiated a charity program for unused premiums.

5. Adopt Long-Term Solutions

Managing and mitigating risk is the bread and butter of insurance companies.

When employing insurtech, think in terms of long-term strategy, and not only ways of working at home during the initial phases of the pandemic.

While systems were not meant initially for working remotely, COVID-19 forced enterprises to implement solutions quickly. Insurance companies needs to adapt to long-term needs for an extended form in the new normal.

Growth in managed services is mature in other industries, whereas insurtech is catching up. Now is the time for IT to partner with business decision makers and evolve your strategy towards a global and long-term approach.

To learn more about your business transformation readiness, watch the whole webinar or contact us at

By Mark Aldous, Managing Director SRP

Despite the many changes underway in the banking industry, manufacturing of structured products continues to be an essential part of the financial services value chain. Structured product issuers provide attractive and innovative products for wealth managers and financial advisors, whilst simultaneously generating derivative transaction flow and a stable source of funding.

There are a wide range of skills needed to run a successful structured products manufacturing business but we have identified 3 critical components:

  • The ability to innovate carefully designed and properly tested products, suitable for a clearly defined target market, underpinned by strong risk management discipline & pricing capabilities
  • The capability to support increasing levels of digital connectivity to clients, either directly or via platforms, and meet the demand for more bespoke, tailored structured products.
  • A robust and efficient infrastructure, necessary to support issuance at scale and to handle the increased volume and lower latency demanded by the highly regulated and constantly changing digital business models

This infrastructure must be flexible enough to cope with the constant product innovation as well as the demands of the ever-changing regulatory and business environment, supported by a specialist workforce who understand the challenges.

Using our many years of experience of developing and managing structured products platforms we have compiled our ‘Top 5’ tips to apply when developing and managing a sustainable issuance platform in the increasingly digital world;

1.There are far better things ahead than any we leave behind.

Replacing legacy infrastructure is critical. Legacy tools may have been critical to reducing costs and coping with increased volumes in the past, so replacing them can be seen by some as a zero-sum gain, but building on legacy foundations will be complex, slow and ultimately more expensive  

2. Just because everything is different doesn’t mean anything has changed

Don’t just rebuild the tools you have today. Develop a platform, infrastructure and support organisation designed to deliver continuous automation. An efficient platform is not just about the capabilities of the tools themselves, it about the ability to support constant change.

3. Master your strengths. Outsource your weaknesses

Even the largest and most successful organisations focus their efforts and resources on the things that differentiate them. Leveraging external providers for services and infrastructure can help accelerate delivery of a new platform at vastly reduced cost from the mutualisation of the development costs

4. Transparency builds trust

At the heart of the design of any issuance platform should be complete transparency over the processes and controls the platform will provide. Provide platform users the ability to understand the platform capabilities, manage the controls and monitor outcomes.

5. A project is complete when it starts working for you, rather than you working for it.

In a world of limited budgets and urgent demands for change, a common path for structured products business managers is to take on more responsibility for delivering the changes themselves. In reality it is extremely hard to deliver real change by squeezing a little extra from the existing teams, regardless of how talented and capable the team are. This can be a false economy and lead to slower, not faster change.

To find out more you can email us at or by clicking on the banner below.

By Sarah Carver, Global Head of Digital

Machine Learning (ML) and more broadly Artificial Intelligence (AI) have quickly become everyday vocabulary for those of us that work in finance. AI and its subsets have the power to revolutionise how financial institutions work and have created opportunities to improve many aspects of the financial services value chain. It can make investment predictions smarter through NLP, improve financial monitoring, credit decisioning, help automate key business processes and even transform how talent is hired.  

Less discussed, however, is the darker side, the inherent risks and vulnerabilities artificial intelligence can introduce into the system in the pursuit of speed, experience and cost reduction.

Automated facial recognition utilizing ML has been increasingly used by financial institutions in onboarding journey’s for banking customers, providing a smoother and quicker customer journey. However less is known about the use of adversarial machine learning which allows for imperceptible changes to a photo ID to fool an identification system. As the collaborative research team from Delta Capita, CTO of dragonfly and Raphael Clifford note in their recent paper ‘ML risks in financial services’ “an attacker can maliciously perturb their face image so that the AI system matches it to a target individual. Yet to the human observer, the adversarial face image appears as a legitimate face photo of the attacker”. How do banks protect themselves against something they can’t see?

Take this further to automated processing of documents. OCR is now the go to for the automatic processing of customer documentation. However again an adversarial attack can now cause even ID numbers of legitimate ID documents to be read by an AI based OCR system as a completely different number.

Regulatory bodies are quickly jumping on this issue, but the risk is a moving target with the attackers quickly moving onto the next weakness.

And that is just the deliberate attacks. Next you must consider the unsightly reality uncovered by many of the models which shows unquestionable bias and discrimination in machine learning. This is not a deliberate attack but is instead based on inherent issues with the data sets being used to train the models. The outcomes of which can be dire.  There have been multiple stories in the news over the past two years whether in regards to US parole selection using AI to provide data driven recommendations to judges which perpetuated embedded biases, or Facebook posting ads for better paid jobs to white men. We also saw a more recent example in the UK with the ‘mutant algorithm’ as described my Boris Johnson, which disproportionately benefited students from private schools when they were unable to take their exams.

Earlier this year Algorithm Watch discovered that Google AI in the form of computer vision which automates image labelling was labelling temperature check devices as ‘guns’ when being held by dark skinned people but ‘electronic device’ when held by light skinned people. This was swiftly resolved by google but how many other biases lie in the underlying datasets which we are not aware of?

Machines alone do not have a capacity to be biased but artificial intelligence requires human intelligence or rather human data to learn from and this is where the problem arises. Artificial intelligence is becoming more embedded in our everyday lives but is there enough scrutiny on the outputs they produce? This is where explainability and transparency of the model will become increasingly critical and with regulation set to continue to increase in this space it is critical for organisations to act.

At Delta Capita we’ve created a solution to solve for this called DC Mint which essentially helps you gain trust in your AI model. It can help uncover underlying bias in your model, ensure regulatory compliance with regs such as GDPR and SR 11-7 4 and can help instil model confidence by understanding how the model behaves and works.

Machine learning has a huge capacity to streamline processes, improve human decisions and balance out the unconscious biases we all have. Human decisions are significantly more difficult to investigate and challenge, but a machines decision can be reviewed, and the algorithm or training data updated. Awareness and explainability is key: where did the data come from, has there been sufficient critique to the data sets on which the models have been trained. What are the outputs and can they be validated and deemed correct, legal and fair.

If we recognise the dark side, explore the potential attack avenues and understand the potential biases then we can address them. If we do this, machine learning offers a vast opportunity for good.

If you are interested to know more about how we can help you with your AI model then email us at or click on the banner below.

By Steve Vinnicombe, Head of Consulting and Solutions

While the insurance market remains robust and resilient during the covid volatility, increasing commercial pressures and market driven activity such as business interruption claims are putting pressure on margins. Regulatory obligations, Solvency 2 and stress testing oblige insurers to improve data, controls, reporting and business transparency. This is a familiar refrain for financial markets COOs and CIOs.

There remains a major task at hand for insurance sector C-suite as a legacy underinvestment in digital has been detrimental to insurance. New technology brings agile working, flexibility to deliver new product, improves STP and lower costs, while better client experience delivers higher retention and new business. The range of opportunities to benefit the business is wide and in conflict with budgetary limitations. The CIO and COO business case will be critical to prioritise digitisation in the sector.

The range of digitisation opportunities needs to be narrowed down to the most beneficial process changes and the optimally efficient automation initiatives. There is competition for budget and investment across underwriting, distributing risks and managing claims across wholesale and retail business. Key factors in the journey to automation and improved digital STP include opportunities for standardisation of data and process across bordereaux processes and end to end retail risk lifecycle.

There are major trends competing for investment budget including the following:

  • Increasing investment in InsureTech (est $3bn in 2018)
  • Increasing data sources require ‘big data’ techniques
  • Emerging trends in competition are triggering investment in the client experience.

For the CIO and COO a response is to consider starting with customer centricity and the complete customer experience – where traditionally interaction with an insurer is an annual event, playing a role for the customer that increases perceived value could move client engagement to real time.

Many insurance processes are still manual and paper based, dependent on completing forms and .pdfs. Some steps to digitise the paper process have increased efficiency but there are continuing widespread incidents of errors and poor data quality which can only be improved when a full digital client journey is enabled.

Emerging digital client journey platforms and visualisation techniques are enabling better client service, a more personal experience and personalised products and pricing. In return facilitating a change in service models triggering a target operating model review driving resources and staff efficiency.

Call centre capacity has seen capacity challenges during the covid crisis as more clients have had to adapt to digital remote working. Improved efficiency and a better client experience is a double win for agile disruptors and provides a model for incumbents to emulate

Distribution channels such as Agents and Brokers are coming under pressure from emerging competitors, increasing standardisation of processes and a reduction in ability for personal interaction (esp during lockdown).

Insurers are increasingly looking to an ‘omni channel’ experience, the level of digitisation will vary according to complexity and value. As we have seen in banking and wealth management, enabling a high touch personal experience in a developing low touch digital world will bring competitive advantage including speed, convenience and personalisation for the client.

The changing demographics of the client base, also mirrored across the financial sector, is bringing increased appetite for digital interaction, wide ranging experience of the ‘customer journey’ in other sectors and an understanding and expectation of a quality digital interaction. Insurers are turning to emerging Fintech solutions to enable this approach.

Any new technology or process must integrate with the legacy platforms and business operating model – the focus is on value creation through digitisation enabling new customer centric high value products processed and administered at a lower cost.

CIOs and COOs at insurers need a clear strategy and roadmap to stay competitive in a digital world. The strategy must be dynamic and adapt to change over time as new trends and technologies evolve to meet changing market demand.

We recommend the following focus areas:

  • Data analytics to better price risk and cross sell products to customers
  • Digital portfolio of tools to enhance client experience, intimacy and reduce cost
  • Data lineage and virtualisation to improve the quality and reduce the cost of compliance
  • Cyber crime prevention tooling to protect customers and reduce cost
  • Process reengineering – legacy processes need to be assessed and digitised, simplified and streamlined.
  • Outsourced service provider review to confirm process and performance are fit for purpose

Incumbent IT departments are adapting to change, partners with expertise, especially those with adjacent financial sector experience can provide additional capacity such as TOM, assessment and review of solutions, resources to inform client perspective.

If you are interested to find out more about how we can help with your business transformation then email us at We are also hosting a webinar on 14th October where we will be discussing the above topic and more with a panel of senior executives. Click on the banner below for more information and to register your place.

By Ana Arxer, Global Head of CLM Sales

Kick-Start your remediation and refresh projects as momentum may have slowed due to the impact of COVID-19 to ensure annual targets and compliance with regulatory requirements are met.

KYC remediation and refresh projects can be challenging in any year but as we all know, 2020 is not just “any year”. The global lockdowns that were put in place to deal with the pandemic have tested existing CLM target operating models in ways that were never foreseen or planned for. As well as managing remediation and refresh portfolios, some institutions have also experienced further strain on “bau bandwidth” for client onboarding as the market volatility experienced during COVID-19 has led them to have an increase in client on-boarding volumes not originally anticipated.  With the 4th quarter soon upon us, additional priority will be focused on meeting year end targets and finalising delivery schedule plans for the coming year, 2021. Many CLM teams are also now facing an additional hurdle in having to urgently incorporate extra remediation portfolio volumes into their production schedules due to strategic changes implemented during the pandemic to ensure regulatory compliance is met. Strategic changes over the last several months include moving client portfolios to new operating jurisdictions or consolidating client portfolios into one operating jurisdiction, resulting in the need to update KYC profiles in line with local jurisdictions.

Based on feedback shared by clients during the beginning of the lockdown period, many CLM teams were successful in implementing short term processes and procedures to assist the KYC analysts in working remotely, with the KYC analysts demonstrating how agile and adaptable they can be. Maintaining the level of productivity has been challenging for some as the lockdown phases continued to be extended. The focus is now being directed to transitioning to the “New Norm”, which may be a slow and bumpy journey due to possible renewed COVID-19 outbreaks. As CLM teams take the opportunity to review their target operating models and transition to the “New Norm”, it remains equally important to maintain the priority and urgency in completing the remediation and refresh portfolios due for completion this year as regulatory timeframes and deadlines have not changed.

CLM teams should be focused on meeting their respective deadlines and avoid incurring a knock-on-effect of having their 2020 remediation and refresh portfolios moved out to 2021. The risk of incurring a knock-on effect can lead to a never-ending spiral of remediation projects, negatively impacting the future target operating models being formed as well as the ability to remain compliant with current AML regulations, which in itself may lead to costly fines. To minimise the risks, CLM teams can consider working with a partner who has the capability and capacity to seamlessly implement a remediation project that delivers quality data and can meet the set timelines ensuring the business remains compliant with both its own business standards and global AML regulations.

Learn how Delta Capita’s CLM services can assist in picking up the momentum to meet the set delivery schedules through delivering high quality data via bespoke remediation projects led by senior practitioners and KYC operating teams with domain expertise in KYC/AML

To find out more you can email us at or by clicking on the banner below.

Delta Capita By Karan Kapoor, Head of Regulatory Change and Technology

As debates on key CSDR Settlement Discipline issues remain unresolved and the expectations of the delay come closer to realisation, the industry understandably remains in a state of flux. However, regardless of precisely what happens with the timing of the regulation, it is now the time to get more disciplined about settlements, which is strengthened further by the latest ESMA Trends Risks and Vulnerabilities (TVR) report.

The study shows a dramatic surge in the level of settlement fails during the second half of March. According to the report, fails climbed to around 14% for equities and close to 6% for government and corporate bonds.

Although, one can argue that COVID-19 induced volatility and adaptations to work environments have driven, what the study states to be, the most significant rise in European trade settlement fails since 2014. The truth, however, is that these numbers put into sharp focus the longstanding operational and structural issues that have hung over trade settlement processes like a dark cloud for far too long now.

Identifying and remediating the root cause of any settlement delay or failure in time to avoid CSDR consequences wastes on average 4-6 hours of capacity. Market participants, therefore, need to shift their attention away from how to deal with trade fails, towards pre-emptively reducing the number of transactions that are failing to settle through internal efficiency improvement and collaborative approaches.

Achieving the above objective is by no means a straightforward task, as most financial institutions still operate with an inherited legacy technology architecture and highly complex operating models.

Firms should explore solutions that could address the issue of settlement fails that do not require wholesale changes to their existing architecture at unmanageable costs.

In the post CSDR era, every hour will matter when it comes to settlement efficiency, as the luxury of ‘another day’ to resolve a failing trade will not be possible. Increasing the control organisations have over their trade lifecycles and identifying settlement delay or potential failure warning signs on T+0 will give participants a considerable advantage. Detecting and being alerted to transaction event anomalies in real-time will provide impacted teams valuable time to prevent failures before CSDR consequences materialise.

Improving the discipline around internal settlement processes and encouraging counterparts to follow suit through collaboration, incentives or slaps on the wrist where relevant, is where we see the market trending. 

Whether the European Commission confirms the delay of CSDR or not, it will be irrelevant as long as the industry conforms to find a solution to ensure penalties and buy-ins do not occur in the first place.

If you want to discover more about CSDR or want to know how we can help you with your CSDR transformation then click on the banner below or email us at

How banks can finally reduce their Karbon footprint

By Gary McClure, CEO KYC Business Services

Imagine this, you are a COO of a major bank managing hundreds, sometimes thousands of people globally on Know Your Customer (KYC) and Anti-Money laundering (AML) tasks. Time that could otherwise be spent focusing on the better risk decisions is instead used to run an army of people gathering information on clients, all before inputting the information (often incorrectly) into multiple systems.

For those that work on this daily, it will no doubt sound familiar, but when one actually steps backs and looks at the bigger picture, all KYC is really about is harvesting and inputting data into a system before then deciding on whether or not to on-board or retain a customer. Sounds simple when put in these terms, so why do so many banks still have KYC processes in place that are costing millions in additional tech and operational expenditure per year?

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High-Touch in a Low-Touch Environment Series: Wealth Management – Webinar Summary & Key Takeaways

By Rezwan Shafique – Head of Consulting, UK

The time is ripe for high-touch and low-touch environments to converge for the wealth management sector. On May 20, I had the pleasure of participating on the panel of Delta Capita’s High-Touch in a Low-Touch Environment webinar. Hosted by Delta Capita’s commercial officer, Julian Eyre, I was joined by industry experts Anand Rajan from UBS Wealth Management U.S., Hugh Adlington from Close Brothers Asset Management and Barclays’ James Penny. (Please click here to hear the full recording of the webinar).

Julian did a great job of chairing the panel. As I expected, the panelists shared similar views about convergence between high-touch, low-touch and the future of digital client services in the wealth management industry.  We discussed strategies to reduce costs and increase revenues in a wider digital engagement context. We also pondered what tools are missing from our current portfolios to bridge the gap between low-touch and high-touch client engagement, and the roles of mobile, machine learning and AI. Furthermore, we talked about how the recent Covid-19 lockdown affected our client engagements and we introduced our digital client engagement services.

Following are some of my takeaways, with credit to my friends and colleagues’ insights.

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