~ 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.
