1) Data Integration and Fragmentation

Financial institutions face considerable hurdles in integrating unstructured data, which originates from a wide array of sources such as emails, customer service transcripts, social media, legal contracts, and news reports. Each source uses varied formats—text, audio, video—and integrating these into a cohesive system is complex and resource-intensive. Additionally, data silos within organizations make it difficult to create a unified view, causing data fragmentation that limits visibility and insight. Take a look at how this may play out within different disciplines in financial services.

• Banking: In banking, data integration is critical for building a comprehensive view of customers across different departments. Integrating customer service transcripts, credit reports, and social media insights helps banks offer personalized services and detect potential issues before they escalate.

• Brokerages: Brokerages need to integrate news feeds, analyst reports, and market sentiment data for effective trading strategies. Fragmentation in data makes it difficult for traders to have a cohesive view of market conditions, impacting decision-making in real-time.

• Wealth Management: Wealth managers rely on integrating customer emails, financial statements, and legal documents to provide holistic advice. Data fragmentation can hinder their ability to provide tailored investment solutions that are needed to maximize ROI.

If you’re working with unstructured data, unifying data is essential for making smarter decisions, improving operational efficiency, and enhancing customer experiences.

2) Data Privacy and Compliance

Unstructured data in financial services often contains sensitive information such as personal identifiers, transaction details, and customer communication logs. This makes data privacy and compliance a significant challenge, especially in an industry heavily regulated by standards like GDPR, FINRA, and the SEC. Managing unstructured data while ensuring compliance requires a robust governance framework to prevent data breaches and unauthorized access.

Unlike structured data, which can be uniformly encrypted and managed, unstructured data can be a bit more challenging. That means that for every data type from audio transcripts to emails, must meet compliance standards.

• Payments: In the payments sector, unstructured data from transaction notes and customer support interactions must comply with PCI-DSS and GDPR. Ensuring that this data is properly encrypted and accessible only by authorized personnel is crucial to avoid penalties.

• Banking: Banks face challenges in ensuring that customer call transcripts and email communications comply with regulatory requirements. For instance, the storage and retrieval of these records must meet strict standards set by regulatory bodies.

• Brokerages: Brokerages handling client communication logs must ensure that all communications are archived and easily retrievable for compliance with SEC regulations, adding to the complexity of managing unstructured data.

3) Data Quality and Noise

Unstructured data often lacks consistency and quality, which complicates efforts to derive actionable insights. For instance, customer feedback in emails may contain irrelevant information, and sentiment analysis of social media posts may misinterpret sarcasm or nuances, leading to inaccurate insights. Financial services must filter out “noise” and standardize data to maintain quality and reliability.

Achieving high data quality from unstructured sources demands advanced data cleaning and normalization processes. However leveraging large language models are useful for extracting relevant information, but require fine-tuning to handle industry-specific terms and interpret complex language accurately. Without rigorous data quality control, unstructured data can lead to incomplete or misleading conclusions, impacting decision-making and customer experiences.

• Wealth Management: Wealth managers must extract meaningful insights from client communications, which often contain unstructured feedback. Poor data quality can lead to misinterpretation of client needs and inappropriate investment advice.

• Transaction Cost Analysis (TCA) in Trading: In trading, analyzing unstructured market data and trader notes requires high data quality. Inaccurate data can lead to erroneous conclusions in transaction cost analysis, impacting profitability.

• Corporate Actions in Asset Management: Processing corporate actions involves extracting information from various documents, such as press releases and shareholder reports. The unstructured nature of these documents makes it challenging to maintain data quality, which is essential for accurate portfolio management.

4) Resource-Intensive Processing

Extracting insights from unstructured data is resource-intensive, requiring advanced AI, machine learning, and natural language processing technologies. Financial institutions often face a lack of in-house expertise and infrastructure capable of processing large-scale unstructured data efficiently. This is particularly challenging as financial data needs to be processed in near real-time to detect fraud, assess risk, or respond to market trends.

Investing in the right technology stack, skilled personnel, and processing power is essential for deriving value from unstructured data. Some financial services adopt AI-driven solutions that automate parts of the process, but these solutions still require specialized expertise for deployment and maintenance. Without the proper resources, unstructured data remains an untapped asset that is costly to process and difficult to operationalize.

• Fraud Detection in Payments: Real-time fraud detection requires the processing of unstructured transaction notes and customer interactions. Without sufficient processing power and expertise, identifying fraudulent patterns becomes difficult.

• Risk Assessment in Insurance: Insurance companies need to analyze unstructured data from claim notes, medical reports, and customer calls to assess risk accurately. The resource-intensive nature of this analysis can delay decision-making, affecting customer satisfaction.

• Trade Surveillance in Brokerages: Brokerages must analyze trading communications, such as emails and chat transcripts, to detect market manipulation. Processing this unstructured data in real-time is crucial but requires significant computational resources and specialized expertise.