Businesses within the financial services sector are among the industries leading the way in delivering AI initiatives to enhance services and improve decision-making however, rich data and strong infrastructure is the essential foundation for successful implementation.
Still plagued by inefficient manual processes and lack sufficient data resources, only 31% of organisations are on track with AI integration. Models that operate with AI are only as good as the data we feed into them so firms need an optimised system that can handle the high volumes of client and business data.
Financial institutions should address these gaps by investing in a robust data infrastructure that connects these siloed sources, creating a firm foundation on which they can build new AI initiatives.
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The Pitfalls of Unorganised Data
Financial information can often be scattered across various locations in a range of formats, such as market insight presentations analysis, underwriting documents, or client emails. Without a predefined format, this disconnected data makes it challenging for AI systems to interpret information effectively and delivers inaccurate analytics that could take the business in the wrong direction.
Many institutions need help organising documents generated across disconnected systems and stored in duplicate data stacks which may produce conflicting versions of the truth. In a sector where client relationships are built on trust, responding to these data issues using obsolete tools, disrupted workflows, and any misstep in data consistency could lead to reputational damage, financial loss, or regulatory fines.
Organizing financial data like transaction records, customer data, and financial reports under centralised and labelled repositories, will make data collection and analysis for projects more accessible.
With these data management tools, firms can automate the process of organizing unstructured data that is easy to find, store and use. This can liberate their teams from the drudgery of manual processes while eliminating the potential for human error, resulting in richer data sources that is ready to fuel AI powered productivity.
Demystifying AI into a Workforce Ally
Workforce preparation and readiness may be an underestimated aspect of AI business readiness employees might be sceptical of AI accuracy and capability based on anecdotal stories following the failed usage of this new technology.
In 2023, a US attorney found himself embroiled in an AI disaster after using an AI chatbot to research precedents in a lawsuit against Colombian airline Avianca. In this case false names, numbers and internal citations were provided based on unverified online sources. The financial services sector is not immune from these types of incidents if generative AI tools are not used appropriately with a clear understanding of the source data. AI tools built on poorly managed inaccurate or incomplete company data can also provide outputs that suffer from similar “hallucinations”.
The rapidly evolving nature of AI tools means that means that both the value and risks are unclear to many users. Firms that do not properly articulate the value and limitations of AI may face inertia amongst workers. It is important to demystify the technology and show how it can improve work experience whilst setting out a framework for appropriate usage that aligns to client and regulatory expectations.
Training and upskilling workers can help explain the fundamentals of AI and teach hands-on skills in using these tools within their job functions, bridging any existing skills and knowledge gaps. This contextual understanding can showcase operational use of AI to assist with dull, repetitive tasks, thus opening up time for teams to focus on growth work that they enjoy and also adds value to firms’ progression.
Managing High Traffic and Data with Cloud
A recent report by Microsoft identified significant bottlenecks that can disrupt AI momentum; a key factor being low levels of compute capacity and adoption of background technologies such as cloud. Despite all its benefits, AI within data infrastructure does require substantial computing power and storage, making on-premise solutions cost-prohibitive. 31% of business have yet to adopt cloud and with the UK lagging other countries in digital technology infrastructure, businesses will need to become more familiar with this technology. Here, cloud computing emerges as a game-changer to grant businesses the flexibility needed to keep sensitive data secure while providing the computational power needed.
Leveraging cloud-based data management tools allows firms to store, process, and scale to accommodate increased traffic, which is particularly beneficial for handling data loads during high transaction periods. This ensures smooth user experiences and utilises decentralised networks for distributed low costs.
The successful deployment of such cloud-based services can help financial companies process enormous amounts of customer data and connect them with AI-processing capabilities without investing in expensive servers. With its ability to accommodate various infrastructure and AI capabilities, cloud solutions can easily handle changes in data to deliver unparalleled employee and customer experiences.
Future Success with Strong AI Foundations
A well-structured and forward-thinking approach to AI is essential, but the quality of AI outputs will only be as good as the data infrastructure that supports them. With the right foundation, even the most advanced AI systems will be able to deliver actionable insights. While it is a complex undertaking, a supported data infrastructure can yield significant rewards for improved decision-making and enhanced customer experiences.
A holistic approach encompassing AI technology, processes, and people can build an AI-ready data infrastructure, allowing financial institutes to remain competitive and adapt to evolving demand. This will secure their position in an increasingly competitive market and ensure sustained success as the financial industry continues its digital transformation.
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