~ The benefits of AI when collecting and analysing financial data ~

Global fintech company Finder reported that around two in five people in the UK (42 per cent) currently invest, whether it’s in stocks and shares, funds or properties. Younger people are particularly interested in investing, with 60 per cent of members of Gen Z saying they have invested before. Data plays a pivotal role in managing these investments, according to Finder’s report. So how can wealth management companies streamline data collection, analysis and management? Here Alex Luketa, partner at artificial intelligence (AI) data management specialist Xerini, explores how wealth management companies can benefit from AI.

Wealth management firms collect various types of data to effectively manage their client’s portfolios. Data helps these companies understand their clients’ particular situations, goals, any risks and investment preferences. Finance managers can also analyse market trends, portfolio risks and other factors to make investment decisions and protect their clients.

Effectively managing this data can be difficult, particularly when it’s stuck in different systems and formats, meaning finance managers must use spreadsheets to consolidate everything they need. Building a data warehouse that copies all the data from systems across the business into one platform can resolve this issue, but it can also be a time-consuming and complex process. Putting the data in one place takes time and the copying process is only updated periodically, meaning that users cannot always access the most up-to-date information.

Streamlining data management

Proper data management is key to building trust with clients, keeping their data confidential, providing the best advice and maintaining integrity of the process. As a result, to remain competitive, wealth management companies should consider how they can streamline data management.

When planning to improve operations, wealth management companies should look at where they can make the most valuable gains. For example, the more time finance managers are spending rifling through different systems to find what they need and filling in spreadsheets, the less they can focus on sharing valuable advice with clients. So, how can they more effectively carry out these processes?

Enter artificial intelligence

Some businesses use data warehousing as a data management strategy, but this requires an expert to copy all the necessary information. While warehousing results in more accurate data, creating it is a time consuming process and periodic batch processing makes it difficult to see the most up-to-date information. Alternatively, more businesses are exploring how AI tools like ChatGPT can deliver business value in a range of applications and industries, including wealth management.

A cloud-based, AI management system centralises data across different systems and provides businesses with the ability to review and report on real-time metrics quickly and efficiently. Unlike warehouses, a cloud-based system leaves data where it is, hosting the information on one interface rather than splitting it between different systems, rapidly reducing the time required for reporting and data management.

Wealth management firms will deal with convoluted and diversified portfolios stored across various systems. Cloud-based data management systems, such as Xefr, are built to have one unified interface that can offer a single, comprehensive view of each portfolio, ensuring more informed decision-making. Additionally, to help better personalise investment strategies, systems like Xefr can convert complex datasets into valuable insights. With interactive querying, the firm can quickly access factors such as market trends, client risk appetite and portfolio performance to create customised advice.

Talk to your data

Interpreting complex data sets is not simple, meaning these platforms may not make it easier for everyone in the business to find and analyse the data they need. However, by integrating large language models (LLMs), businesses can create interactive interfaces that any user can confidently navigate. For example, by training the system on relevant prompts using natural language, users can ask questions of their data. Users can describe what they want the report to look like and the data it needs, and build a dashboard.

At a glance, users can interrogate existing client data alongside information such as market trends and risk to provide more effective advice without the need to rifle through manually-made reports. This means team members can spend the time saved on reporting on more valuable tasks.

Overcoming AI barriers

Businesses that are willing to rapidly adopt emerging technologies like AI could see significant benefits in automating laborious tasks, such as reducing costs and improving data integrity. While many businesses may see the potential gains, it is understandable that some are apprehensive.

When new technologies are introduced that automate tasks, some team members may be cautious that they will be replaced. In reality, AI still needs human input to interpret information and provide valuable prompts. Also, looking back at previous innovations, the computer nor the internet replaced us, they enhanced people’s work — AI is predicted to do the same.

Wealth management businesses handle confidential client information on finances, personal details and more. Using open platforms like ChatGPT raises privacy concerns, with a lot of data and queries being visible to software developers. Building a private platform with natural language processing capabilities enables wealth management businesses to ensure privacy, and developers can build barriers around data sets to ensure only authorised users can access private data.

As more people explore the benefits of investing, wealth management firms are looking at how they can improve efficiency, reduce costs and remain competitive. Developing a cloud-based data management system and leveraging AI allows businesses to streamline reporting, which frees up valuable time and provides more visibility for making decisions based on data. It also enables users to converse with their data, better understanding how they can use all the information at their disposal to provide a competitive edge to client portfolios.

Anuj Goel, Co-founder and CEO at Cyware 

Against the backdrop of Russian-based hacktivists declaring war on Europe’s financial systems, the passing of the EU’s Digital Operational Resiliency Act (Dora) and the potential threats posed by the emergence of generative AI, the finance sector has a lot to contend with. 

In today’s elevated threat environment, cybersecurity teams need to take proactive action fast. All too often, however, analysts are bombarded by a tsunami of alerts generated by countless security tools. According to recent estimates, today’s enterprises have on average 100+ discreet security tools, many of which do not play nicely together.  

At the same time as attempting to make sense of all this noise, IT security teams and their risk counterparts often work in isolation and rarely share resources or intel. Consequently, both teams are on the lookout for external indicators of looming threats despite the fact that internal log data often contains clues to the next attack. Without the right tools to effectively process and analyse this vast sea of data, these clues stay undetected, only to be discovered forensically long after an attack occurs. But rather than simply adding more security tools into the mix, security professionals need a better way to examine the threat data generated by disparate security tools and deduce high confidence and actionable threat intelligence. 

To improve their threat detection and response capabilities, banks need to adopt a cyber fusion strategy that makes it easier and faster to find indicators of potential compromise and collectively take informed defensive steps to prevent or mitigate an incident. 

What is cyber fusion? 

Initially developed by intelligence agencies to promote collaboration through intelligence sharing, the fusion centre concept is now gaining traction in the field of cybersecurity. 

Unifying security functions such as threat intelligence, security automation, threat response, security orchestration and incident response into a single connected unit, cyber fusion offers a more proactive approach to dealing with potential threats by bridging the gap between multiple teams through intelligence synthesis and inter-team collaboration. It also enables the fusion of contextualised strategic, tactical and operational threat intelligence for rapid threat prediction, detection and incident response.  

By initiating a cyber fusion centre (CFC), banks will be able to automate the ingestion of threat data from a variety of different sources including existing security tools, cloud apps, historic incident intelligence and other data sources, including external threat intelligence providers and regulatory advisories. This can be done in a way that allows security teams to contextualise insights into malicious activities and meaningfully orchestrate cybersecurity operations across the network. 

Leveraging AI and machine learning to enable faster actioning and analysis of threat intelligence, a CFC delivers complete visibility of security risks, threats, security controls and exceptions across cloud-based or on-premises infrastructures. It also enables banks to automate incident response and respond to threats in real time or proactively.  

Finally, and most importantly, it also boosts inter-team collaboration by automatically notifying the right stakeholders of relevant threat intelligence and changing scenarios in real-time via a shared platform that supports a truly holistic and joined-up response. 

Enabling a unified security posture 

Bringing together technologies, teams and processes under one roof, a CFC enables security teams to orchestrate and automate security workflows in an integrated and highly collaborative manner. 

Providing insights on all kinds of threats including malware, vulnerabilities, threat actors and previous incidents, cyber fusion supports the rapid dissemination of intelligence among all security teams to enable high-fidelity security decision-making at a technical, tactical, operational and strategic level. The exchange of situational intelligence at a cross-sectoral level empowers security teams to co-develop threat mitigation strategies. It also enables teams to leverage shared actionable intelligence to automate responses – such as blocking malicious IPs in firewalls or updating SIEM data – with no need for manual intervention. 

But that’s not the only benefit. To further reduce security vulnerability risks, banks can use their CFC platform to automatically feed relevant data into their other security tools (EDR, firewalls, IDS/IPS, SIEM, SOAR). Using automated cross-functional workflows to drive security actions significantly reduces the mean time to detect (MTTD) and mean time to respond (MTTR). 

Connecting the dots for enhanced resilience 

With a cyber fusion centre in play, banks can enable security teams to ingest, enrich, correlate and manage threat data into a single source of truth and turn that data into contextualised, noise-free and actionable threat intelligence. This can also then be shared in real time to identify and respond to threats faster. 

Enabling 360-degree threat visibility is just the start. Alongside promoting collaboration between teams by sharing real time threat alerts that support a collective defence approach, a CFC enables security operations teams to automate incident responses and initiate an end-to-end threat response process that keeps pace with the evolving threat landscape. By adding cyber fusion capabilities to their existing security operations centre (SOC), banks will be better equipped to connect the dots and respond to the prevailing threat landscape in real time.