As the use of AI in business functions becomes ever more ubiquitous, the importance of high-quality data that supports the building blocks of AI and machine learning becomes even more critical. Artificial intelligence and machine learning are dramatically changing how firms function.
To date, many financial services firms have been implementing AI in various use cases, but are primarily focused on automating basic tasks:
User experience: AI-powered chatbots and virtual assistants provide support for users of internal technology tools, as well as client log-in
Reconciliations: AI can significantly streamline reconciliations by using pattern recognition to perform root cause analysis
Data outliers: AI can easily detect outliers among a set of data, such as unique swings in prices or a significant difference in transaction volume
Significant improvements in implementation would allow AI applications to be used in a more forward-looking way:
Investment strategies: Analysis of market trends and historical data to help optimize investment portfolios
Risk assessment and management: Machine learning models to process complex datasets to evaluate risk
Compliance and regulatory reporting: Automation of compliance processes to reduce risk of human error and ensure adherence to regulatory requirements
Challenges in developing and successfully implementing AI
The foundation of all AI systems is only as strong as the data on which they are built. Poor quality data that is incomplete, inaccurate, outdated or irrelevant poses significant risks to the reliability and effectiveness of AI applications.
Clean data enhances the reliability of a firm’s analytics and business intelligence. With the increasing volume of data generated by firms, maintaining data quality has become ever more challenging yet essential to a firm’s efficiency and growth.
The consequences of using poor-quality data are far-reaching, including erosion of customer trust, regulatory noncompliance and financial and reputational damage.
In addition, poor data quality can significantly affect the performance and reliability of AI systems, leading to significant issues and potential risks:
Biased and inaccurate results: AI systems trained on poor quality data can produce biased or incorrect outcomes.
Incorrect decisions and security risk: Erroneous or inaccurate data can cause AI systems to make poor decisions, which can have a cascading effect. In addition, poor data quality can create security vulnerabilities that malicious actors may exploit.
According to Gartner, 30% of generative AI projects are expected to be abandoned by 2025 due to poor data quality, inadequate risk controls, escalating costs or unclear business value.
Arun Chandrasekaran, Distinguished VP Analyst at Gartner states: “Through 2025, at least 30% of GenAI projects will be abandoned after proof of concept due to poor data quality, inadequate risk controls, escalating costs or unclear business value.” 1
Firms remain cautious about large-scale adoption of AI
Prioritizing data quality in the financial services industry is essential for compliance, risk management and decision-making, enhancing operations through real-time accuracy and advanced tools.
Although many firms are expressing significant interest in expanding their use of AI, according to new data from tech.co’s Impact of Technology on the Workplace report, caution abounds. Over two-thirds (67%) of the more than 1,000 business leaders surveyed said AI integration either remains limited or is non-existent.2
Financial services firms, in particular, are still cautious about AI’s possibilities and risk.3 Many firms are more likely to be watching and learning about AI tools rather than implementing them.4
The exception is the very large banks, where the AI landscape is dominated by JPMorgan Chase, Capital One and Royal Bank of Canada. For these market leaders, the path is already set, internal doubts about the quality of their data have been mostly satisfied and a clear strategic vision has been set. That said, aside from the pacemakers, the rest of the industry is lagging, primarily owing to risk aversion.
Given the breakneck pace of adoption, it’s critical at this stage to help institutions harness the power of high-quality data and share best practices so that firms can remain competitive.
“Despite AI’s potential, most finance functions’ AI implementations have remained limited,” said Marco Steecker, Senior Principal in the Gartner Finance Practice. “As they begin to chart out a plan for how best to prioritize that additional investment, CFOs should partner with their finance leadership teams to compare their current progress against their peers’ and identify concrete recommendations from early adopters on how best to accelerate AI use in their function.”5
Mitigating risks with robust data management
Trusted, governed data is essential for ensuring the accuracy, relevance and precision of AI. To unlock the full value of data for AI, firms must be able to navigate their complex IT landscapes to break down data silos, unify their data and prepare and deliver trusted, governed data for their AI models and applications.
Continuous data quality monitoring grants financial services firms enhanced oversight across their complete data pipelines, vital for operational and analytical functions. This data also provides the foundation for enabling AI applications to train machine learning models. Adopting a practical, self-service model ensures that data quality remains decentralized to all organizational stakeholders, empowering them to actively detect and address data quality challenges.
The strength of AI depends on the quality of data
As organizations continue to leverage AI for competitive advantages, the focus must increasingly shift toward implementing and maintaining high-quality data management practices. By doing so, companies can reduce the risks associated with poor data, paving the way for AI solutions that are both innovative and reliable. To ensure that AI systems are reliable and responsible, data should be:
Accurate: reflect the real world
Complete: have required information available
Consistent: synchronized across the organization
Timely: available when needed
Valid: follows business rules, in specified format, and can be used with other sources
Unique: no instances of duplicates within the dataset
GenAI initiatives will be driven by trusted data
The key driver for any GenAI initiative is high-quality data. Since the end results will reflect the data that is being used to make predictions, that data needs to be clean, reliable, accessible and discoverable. A well-designed purpose-built tool that integrates data quality, governance and lineage into its design can bring a competitive advantage by giving firms the confidence that they have the appropriate inputs for large language models (LLMs) to generate responses, in addition to the right data architecture to build applications on top of GenAI capabilities.
Following are some examples of use-cases that would be appropriate for financial services and investment management:
A GenAI-based enterprise application that can detect price movement anomalies for an investment portfolio requires a clean, consolidated dataset of instrument pricing.
A chatbot/copilot application that can answer user questions about a firm’s investment portfolios requires curated data store with high-quality data on transactions and holdings so that any queries generated by LLMs can be executed and ultimately produce high-fidelity results.
AI can simplify the extraction of insights and datapoints from unstructured data, text and documents. Achieving this requires data ingestion and a data transformation framework that can provide AI capabilities access to the source material and then store the results.
Using GenAI to simplify investment reporting and to handle investor inquiries requires a complete data catalog of available data fields, their semantic meanings and sources from which data was populated.
Initiatives like GenAI represent a critical step forward in harnessing the power of AI. By collaborating with a trusted data partner, financial services firms can be confident that data used by AI technologies will uphold principles of transparency, accountability and privacy.
Financial services firms thrive based on their reputations
Data quality is crucial in shaping risk management strategies and ensuring regulatory compliance in financial services, while the need for quick action means data errors can quickly propagate throughout operations. Data integrity holds critical significance in reporting, analytics, and forecasting in the financial sector, and data consistency can lead to better decision-making processes.
