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AI in Investment: A Guide for Asset Managers

Source: Finance derivative

Giacomo Barigazzi ,Co-founder, Axyon AI

In today’s dynamic investment landscape, the race to harness new technologies for a competitive advantage is more fierce than ever. For those in the asset management sector, embracing innovation isn’t just a choice—it’s a necessity to stay ahead in the relentless pursuit of investment opportunities.

The bedrock of investment management has always been grounded in exhaustive research and due diligence. However, the rapid evolution of technology mandates a shift in strategy. Now, it’s critical for leaders in this space to not just familiarise themselves with, but to fully integrate advanced technologies such as artificial intelligence (AI) and machine learning (ML) into their processes.

Exploring the Varieties of AI in Asset Management

It’s essential for asset managers to recognise the specific AI technologies available to them, as this understanding can greatly influence their approach to investment strategy. Broadly speaking, AI in asset management can be categorised into generative and predictive models, each with distinct capabilities and applications.

Generative AI, powered by advanced machine learning techniques, is designed to produce new data that mimic real-world information, such as text, images, and more. This technology is especially useful for creating realistic and diverse datasets, enhancing personalisation, and improving the accessibility of financial services. For asset managers, generative AI can play a crucial role in developing innovative solutions and strategies by generating novel insights and scenarios.

On the other hand, Predictive AI focuses on analysing historical data to forecast future trends and patterns. This aspect of AI is invaluable for asset managers aiming to anticipate market movements and adjust their strategies accordingly. The predictive capabilities of AI provide a strategic edge by enabling more informed decision-making and risk assessment.

For asset managers intent on leveraging AI to enhance their operations, distinguishing between these AI types is a fundamental step. By adopting the appropriate AI technologies, they can significantly improve client outcomes, operational efficiencies, and, ultimately, investment performance.

Creating a personalised client experience

Improved performance is not the only advantage AI brings to asset management; it significantly enhances the client experience by enabling the development of personalised services. For clients, generative AI tools like chatbots and virtual assistants establish a continuous support system that provides instant responses to queries, as well as up-to-date insights on market developments and portfolio adjustments.

A heightened level of personalisation throughout the investment journey ensures clients are not just satisfied but also better informed – a dynamic which undoubtedly fosters greater human relationships in the industry.

Strategic considerations for asset managers

As the widespread adoption of AI in the financial services sector continues to materialise, asset managers face a crucial task in nailing down the right WealthTech solution. It’s not just about adoption; it’s about making strategic choices.

Ultimately, companies expect to see a strong ROI after adopting an AI solution. Only by making a well-informed choice will they see the expected tangible impact of AI in asset management. A lack of due diligence in the procurement process risks introducing a solution that is both ineffective and disruptive.

Integration is key. AI solutions should align seamlessly with existing systems to avoid unwanted disruption to day-to-day operations. Therefore, choosing a provider that is ready to provide extensive training to support a smooth assimilation into operations should also be a priority for management.

There is an element of self-assessment required in the decision-making process. By recognising areas in a firm that require enhancement and understanding the specific value offered by each AI solution, leaders will be best positioned to identify a product that will bring significant improvements in targeted areas.

With a sea of options available in 2024, selecting an AI solution demands thoughtful consideration. Managers need to assess how each aligns with their investment strategy and delivers results. Consulting with experts and analysing case studies from similar businesses equips managers with valuable insights for informed decision-making.

AI as an empowerment tool

While AI will be a revolutionary tool in the asset management industry that will drive efficiency and innovation, it is not intended to replace the human touch. The technology should be viewed as a tool that empowers asset managers to focus on high-value work of greater importance to clients.

AI’s transition from a nascent curiosity to an integral business tool underscores a pivotal shift in industry dynamics. Asset managers who are slow to adopt these technologies risk falling behind in a market that’s increasingly influenced by AI’s capabilities. By contrast, those dedicated to swiftly and responsibly adopting this technology will likely be rewarded with an extra edge in performance.

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Business

Adapt or fall behind: why embracing data-centric technology is key for investment firms

Source: Finance Derivative

By Murray Campbell, Product Manager at AutoRek

The investment sector has often relied on conventional procedures and stringent regulations. However, coping with obsolete legacy software can impede an organisation’s growth and development. Despite being aware of these challenges, investment companies worldwide tend to persist with these systems due to the perceived high cost and complexity in implementing modern technology. 

As technology continues to advance and the world becomes more digitally dependent, there is increasing pressure on firms to ensure their buy-side operating model is as efficient as possible. While investment firms have typically prioritised the front-end of their product, the back-office is equally important as this is the engine that drives any organisation. This is particularly key in today’s rapidly evolving markets where significant rewards await businesses that can successfully deliver innovation and efficiency within their organisation.

The unforeseen costs of manual processes

When investment firms operate independently, they often end up utilising various platforms that offer similar functions. However, this approach results in the accumulation of expensive and disjointed systems, leading to inefficient workflows, high costs, and the need to maintain multiple vendor relationships. Such inefficiencies can hinder a firm’s ability to adapt to new market challenges and demands, which can be a major problem for companies in the long-term.

For many, the lack of suitable IT systems is the most common operational challenge UK investment businesses face. Many face obstacles when it comes to reliance on manual processes, an absence of suitable solutions available in the market, or a lack of resources available to invest in such solutions. In the dynamic realm of data management, the choice of tools and solutions is crucial for steering business decision-making and operational efficiency. Investors need faster, more personalised customer experiences and investment firms need to focus on providing seamless journeys – even in the face of economic turbulence and increasing regulatory requirements.

One area where organisations can greatly benefit from advanced technology is by reducing their dependency on spreadsheets. Currently, many buy-side investment managers are still reconciling data in spreadsheets or using generic platforms that lack key features. In fact, more than nine in 10 agree that their firm relies too heavily on manual tasks and spreadsheets, meaning that the UK investment management industry still has some distance to go to remove reliance on manual reconciliations. Relying on outdated methods can be a costly mistake.

The expansion of the digital economy, increasing transactional volumes, and ever-changing regulatory obligations have made it necessary to adopt more sophisticated solutions. Excel, for instance, lacks key controls and has limited auditability, making it almost impossible to track and evidence actions. As a result, organisations end up spending more resources and money to fix errors, leading to higher costs in the long run. Therefore, transitioning to more advanced solutions is crucial to ensure data accuracy, integrity, and scalability as they continue to grow and evolve.

How is automation changing the investment industry?

In the current digital age, management of complex operations is heavily reliant on automation. With the help of data-driven insights, automation can enable investment managers to make informed decisions, identify market trends, and optimise portfolio performance. By automating tasks such as validations and cash transfers, investment managers can ensure that data-related tasks are executed with speed and accuracy, freeing up their time to focus on activities where their human expertise and creativity can add more value.

According to a recent report by AutoRek, UK-based investment managers claim they are continuing to invest in automation, with 100% of respondents either maintaining or increasing their automation expenditure in the years ahead. Continued investment in automation is promising given firms remain too reliant on manual processes, particularly when it comes to reconciliations. Nevertheless, successful implementation isn’t about adopting every automation tool available. Instead, companies should focus on strategically selecting applications and carefully refining processes that are in line with their corporate objectives and unique requirements.

Act now or fall behind

The promise of emerging technologies lies in the ability to unlock new insights and improve productivity. But to use this technology effectively, modern infrastructure that can capture and validate large volumes of data in a scalable manner is required. Replacing manual processes with end-to-end automation can drive significant benefits for investment firms as it presents an opportunity to eliminate much of the friction around reconciliations, reduce operating costs, and liberate staff from repetitive manual tasks.

To conclude, the integration of data-centric technology is crucial. If investment firms want to remain competitive and innovative they must keep up with the demands of fast-moving markets. They must clear their data clutter and evolve quickly – or risk being left behind.

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Business

Why email marketing remains one of the best forms of digital marketing

Crafting a strong email marketing strategy involves a real balance between creativity and making data-driven decisions, which, is just one of the roles undertaken by marketing and data company Go Live Data on behalf of its many clients.

Guiding some of the biggest corporates in the UK including Amazon Business, AxA and Premierline Business Insurance, Adam Herbert, CEO of Go Live Data, advises on the key components to a successful email campaign and why as one of the most effective marketing tools available, email still plays a crucial role in digital marketing:

Forming a direct means of communication, emails provides a and two-way access between businesses and their customers. And it may sound obvious to say, but unlike social media or other digital channels, every email allows marketers to reach their audience straight into their inbox, and this is where individuals are most likely to engage with the content they’re being shown.

Offering a high return on investment,  emails consistently deliver one of the highest ROI’s compared to other forms of digital marketing such as PPC and advertising. According to studies, the average is around £40 for every £1 spent, which is huge; and due to the low cost of email, its ability to drive conversions and to retain customers.

What’s more, with email segmentation and many personalisation techniques available, marketers can tailor their messages to specific groups of their audience, based on demographics, their behaviours, interests, and purchase history making them not only very targeted, but personalised too. The key is to deliver relevant content to subscribers, which means marketers can increase engagement, conversions, as well as customer satisfaction.

There are specific platforms which allow for automation, giving marketers the ability to set up automated workflows triggered by user actions and also means that marketers can deliver timely and relevant messages at scale, by nurturing leads, as an effective way to guide customers efficiently through the sales funnel.

Emails are also an excellent way to build customer relationships, by nurturing over time. By consistently delivering valuable content, exclusive offers, and personalised recommendations, businesses can strengthen the ‘bond’ with their audiences and increase brand loyalty. Email provides a means of two-way communication, which allows customers to send in their feedback, to ask any questions they may have and to  engage with a brand directly.

They are also a great way to drive traffic to your website, blog and social media, or any other digital channels connected to your business. By including attractive or compelling calls-to-action (CTAs) and relevant content, you can encourage subscribers to take action such as making a purchase, signing up for a webinar, or downloading a resource, which in turn will drive conversions and revenue for your business.

Email platforms offer substantial analytics and reporting functions that enable marketers to track the performance of their campaigns in real-time. Monitoring of key metrics such as open rates, click-through rates, conversion rates, and revenue generated, allows marketers to measure the effectiveness of their campaigns and of course make data-driven decisions to optimise and plan future activities.

Overall, emails are an integral component of a digital marketing and by leveraging email effectively, businesses can engage their audience, nurture leads, drive sales, and ultimately grow their businesses.

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Business

Conflicting with compliance: How the finance sector is struggling to implement GenAI

By James Sherlow, Systems Engineering Director, EMEA, for Cequence Security

GenerativeAI has multiple applications in the finance sector from product development to customer relations to marketing and sales. In fact, McKinsey estimates that GenAI has the potential to improve operating profits in the finance sector by between 9-15% and in the banking sector, productivity gains could be between 3-5% of annual revenues. It suggests AI tools could be used to boost customer liaison with AI integrated through APIs to give real-time recommendations either autonomously or via CSRs, to inform decision making and expedite day-to-day tasks for employees, and to decrease risk by monitoring for fraud or elevated instances of risk.

However, McKinsey also warns of inhibitors to adoption in the sector. These include the level of regulation applicable to different processes, which is fairly low with respect to customer relations but high for credit risk scoring, for example, and the data used, some of is in the public domain but some of which comprises personally identifiable information (PII) which is highly sensitive. If these issues can be overcome, the analyst estimates GenAI could more than double the application of expertise to decision making, planning and creative tasks from 25% without to 56%.

Hamstrung by regulations

Clearly the business use cases are there but unlike other sectors, finance is currently being hamstrung by regulations that have yet to catch up with the AI revolution. Unlike in the EU which approved the AI Act in March, the UK has no plans to regulate the technology. Instead, it intends to promote guidelines. The UK Financial Authorities comprising the Bank of England, PRA, and FCA have been canvassing the market on what these should look like since October 2022, publishing the results (FS2/23 – AI and Machine Learning) a year later which showed a strong demand for harmonisation with the likes of the AI Act as well as NIST’s AI Risk Management Framework.

Right now, this means financial providers find themselves in regulatory limbo. If we look at cyber security, for instance, firms are being presented with GenAI-enabled solutions that can assist them with incident detection and response but they’re not able to utilise that functionality because it contravenes compliance requirements. Decision-making processes are a key example as these must be made by a human, tracked and audited and, while the decision-making capabilities of GenAI may be on a par, accountability in remains a grey area. Consequently, many firms are erring on the side of caution and are choosing to deactivate AI functionality within their security solutions.

In fact, a recent EY report found one in five financial services leaders did not think their organisation was well-positioned to take advantage of the potential benefits. Much will depend on how easily the technology can be integrated into existing frameworks, although the GenAI and the Banking on AI: Financial Services Harnesses Generative AI for Security and Service report cautions this may take three to five years. That’s a long time in the world of GenAI, which has already come a long way since it burst on to the market 18 months ago.

Malicious AI

The danger is that while the sector drags its heels, threat actors will show no such qualms and will be quick to capitalise on the technology to launch attacks. FS2/23 makes the point that GenAI could see an increase in money laundering and fraud through the use of deep fakes, for instance, and sophisticated phishing campaigns. We’re still in the learning phase but as the months tick by the expectation is that we can expect to see high-volume self-learning attacks by the end of the year. These will be on an unprecedented scale because GenAI will lower the technological barrier to entry, enabling new threat actors to enter the fray.

Simply blocking attacks will no longer be a sufficient form of defence because GenAI will quickly regroup or pivot the attack automatically without the need to employ additional resource. If we look at how APIs, which are intrinsic to customer services and open banking for instance, are currently protected, the emphasis has been on detection and blocking but going forward we can expect deceptive response to play a far greater role. This frustrates and exhausts the resources of the attacker, making the attacks cost-prohibitive to sustain.

So how should the sector look to embrace AI given the current state of regulatory flux? As with any digital transformation project, there needs to be oversight of how AI will be used within the business, with a working group tasked to develop an AI framework. In addition to NIST, there are a number of security standards that can help here such as ISO 22989, ISO 23053, ISO 23984 and ISO 42001 and the oversight framework set out in DORA (Digital Operational Resilience Act) for third party providers. The framework should encompass the tools the firm has with AI functionality, their possible application in terms of use cases, and the risks associated with these, as well as how it will mitigate any areas of high risk.

Taking a proactive approach makes far more sense than suspending the use of AI which effectively places firms at the mercy of adversaries who will be quick to take advantage of the technology. These are tumultuous times and we can certainly expect AI to rewrite the rulebook when it comes to attack and defence. But firms must get to grips with how they can integrate the technology rather than electing to switch it off and continue as usual.

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